Future technologies in science in next 5 years

Literature Review:

Future Technologies in Science over the Next Five Years: A Cross‑Domain Literature Review

Across the scientific landscape, converging waves of innovation in artificial intelligence, nanotechnology, genomics, renewable energy, and space systems are reshaping research agendas and application pathways, with most analysts anticipating not a single “disruptive” breakthrough but a cumulative, mutually reinforcing transformation over roughly the next five years.[11][24] Rather than isolated advances, the literature points to embedded and agentic AI diffusing into edge devices and critical infrastructures, AI‑enhanced genomics and nanomedicine accelerating precision health, integrated green technologies enabling more sustainable energy, mobility, and urban systems, and renewed momentum toward economically and ethically viable space resource utilization.[20][5][48] At the same time, systematic reviews and technology assessments stress that data quality, validation in real‑world conditions, human factors, ethical governance, and persistent inequalities in access will be decisive in determining whether these technologies deliver on their promises.[31] This review synthesizes evidence‑based projections from recent domain‑specific surveys, foresight-oriented reviews, and methodological studies to outline key technological trajectories, describe how researchers study and anticipate them, and map contradictions and open questions that will shape scientific and societal outcomes between now and the early 2030s.

Introduction: Framing Future Technologies in Science (2026–2031)

Scientific and technological change over the coming five years is widely described not as an unprecedented acceleration in any single domain, but as an unprecedented *convergence* across multiple domains that are each maturing at once. The Stanford Emerging Technology Review emphasizes that artificial intelligence, synthetic biology, advanced materials, and neuroscience are simultaneously reaching stages where they can be scaled in real-world systems, creating powerful complementarities but also new systemic risks. Similarly, broad surveys of artificial intelligence applications show that deep learning, natural language processing, reinforcement learning, and privacy‑preserving methods are no longer confined to specialized research areas but are being integrated across healthcare, finance, security, transportation, and environmental management, with cross‑domain synergies emerging around multimodal data fusion and explainable AI.[11] In parallel, reviews of nanotechnology in medicine, embedded AI, electric vehicles, and renewable energy integration all adopt increasingly “foresight‑oriented” perspectives, asking not only what has been achieved but what patterns of development suggest for the near future.[5][20][24]

The temporal focus of this review is approximately five years, extending from the mid‑2020s toward the early 2030s, which corresponds to the planning horizon used in many sectoral foresight exercises, technology roadmaps, and policy strategies.[5][10] Renewable energy reviews, for example, explicitly frame their analyses around emerging trends and “future pathways” for integrating solar, wind, and geothermal technologies into construction engineering, emphasizing implications for research, policy, and practice in the near term.[5] Reviews of electric vehicle technologies similarly synthesize advances in batteries, ultra‑fast charging infrastructure, and market projections up to about 2031, highlighting both expected growth and unresolved bottlenecks in range, cost, and infrastructure deployment.[10] Embedded AI surveys explicitly consider the period 2023–2026 as a transition from technical feasibility to large‑scale deployment, with clear trajectories for further evolution in edge computing and federated learning over the subsequent few years.[20] This convergence of five‑ to seven‑year horizons in domain‑specific literature provides a consistent temporal frame for our cross‑domain synthesis.

Thematically, the review focuses on “future tech” in the sense of technologies that are either entering, or are expected to enter, a phase of broader scientific and practical impact within approximately five years, rather than speculative concepts that remain largely at the level of distant vision. The emphasis is therefore on AI embedded in devices and infrastructures rather than general artificial intelligence; on nanomedicine and AI‑integrated genomics that are already in clinical or translational pipelines; on renewable energy and smart grid technologies that are being piloted or gradually scaled; and on space mining and space colonies as emerging areas in which enabling technologies and governance frameworks are under active development.[14][24][48][47] This approach mirrors the distinction drawn in nanomedicine between the “first wave” of therapeutic nanoparticles already in use and the “transformational impact” anticipated as lessons from early applications inform broader, systems‑level innovations in areas such as immunotherapy and regenerative medicine.[29]

Methodologically, much of the relevant evidence comes from systematic and scoping reviews, bibliometric and scientometric analyses, health technology assessments, and technology foresight studies, rather than from single experimental breakthroughs.[5][11][24] Reviews of embedded AI, digital health technology assessment, and AI‑based medical devices provide structured accounts of technological capabilities, deployment paradigms, and evaluation challenges, while foresight and external technology searching literature clarifies how organizations identify and prioritize emerging technologies.[20] Bibliometric studies of sustainable entrepreneurship, financial literacy, nanotechnology‑enhanced microalgae biorefineries, and AI in cancer research reveal the rapid growth and thematic clustering of research fronts, offering an empirical basis for claims about emerging trajectories. At the same time, methodological systematic reviews within health technology assessment highlight persistent weaknesses in how population‑adjusted comparisons and environmental sustainability are incorporated into technology evaluation, underscoring that methodological innovation must accompany technological advances.

This review is structured around three core questions aligned with the user’s request and with common organizing principles in technology assessments. First, what are the key findings from recent literature about future technologies likely to shape scientific practice and application in the next five years, across major domains such as AI, biomedicine, energy, and space? Second, what methods are being used to identify, study, and evaluate these emerging technologies, including foresight, bibliometrics, systematic review, and health technology assessment frameworks? Third, where do contradictions, unresolved debates, or open questions appear in the literature, and how might they influence the trajectory and social impact of future technologies? Each of these questions is addressed in a dedicated section, followed by an integrative synthesis and conclusion.

Key Findings: Major Technological Trajectories for the Next Five Years

Artificial Intelligence as a Ubiquitous Scientific and Infrastructural Technology

Across multiple reviews, artificial intelligence is consistently identified as the most pervasive and cross‑cutting technology shaping scientific and applied domains over the coming years.[11][31] A comprehensive survey of AI applications across healthcare, finance, security, and sustainability synthesizes over 130 studies published between 2023 and 2026 and concludes that while domain‑specific innovations continue apace, the most significant trajectory lies in the convergence of multimodal data fusion, explainable AI, and privacy‑aware computing across disciplines.[11] This convergence supports applications as diverse as medical question answering, drug discovery, financial fraud detection, autonomous driving, and environmental policy analytics, with shared technical foundations in deep learning architectures, reinforcement learning, and federated learning.[11] The implication is that future technologies in many fields will be less about standalone AI systems and more about AI as an embedded component of broader socio‑technical systems.

The evolution of embedded AI provides one of the clearest indicators of near‑term trajectories. A review of embedded artificial intelligence research between 2023 and 2026 documents a shift from “technically feasible” to “large‑scale deployment,” driven by innovations in non‑von Neumann hardware architectures such as compute‑in‑memory and neuromorphic chips, alongside advances in lightweight algorithms and edge–cloud collaboration.[20] At the hardware level, heterogeneous integration and neuromorphic designs promise ultra‑low‑power, low‑latency computation suitable for wearable devices, industrial IoT systems, and autonomous robots.[20] Algorithmically, dynamic adaptive lightweighting and specialized edge‑side optimization of large models make it possible to run large language models and diffusion models on resource‑constrained devices, enabling on‑device personalization and privacy‑preserving inference.[20] Deployment paradigms such as federated edge learning and edge–cloud collaborative intelligence further facilitate distributed training and inference while addressing data privacy and bandwidth constraints.[20] Collectively, these trends suggest that within five years, AI will be deeply embedded across physical infrastructures, from industrial machinery to consumer wearables, shifting the center of gravity of AI research from centralized cloud systems to distributed edge ecosystems.

Agentic AI in smart grids provides a concrete example of how embedded intelligence may transform critical infrastructures. A comprehensive review of agentic AI for smart grids describes a new paradigm in which AI systems perceive grid states, reason about operational goals, plan multi‑step actions, and interact with operators in real time, going beyond static prediction to autonomous, adaptive control.[14] Multi‑agent control strategies and reinforcement learning frameworks are being developed for voltage and frequency control, demand response, coordination of distributed energy resources, electric vehicle aggregation, and grid restoration.[14] Digital twin optimization and physics‑based control approaches further integrate simulation and real‑time data, enabling safe exploration of control policies.[14] The review emphasizes that within the next several years, the most consequential advances will likely involve integrating these agentic AI systems into operational grids in ways that satisfy stringent criteria for stability, safety, interpretability, and interoperability with existing grid codes, rather than purely algorithmic performance.[14] This pattern—where the frontier shifts from algorithm design to integration, validation, and governance—recurs across AI‑intensive domains.

AI applications in healthcare illustrate both the potential and the limitations of near‑term AI advances. A review of AI in healthcare organizes current applications into three themes: surgical care, clinical decision support, and specialized domains such as genomic interpretation and digital mental health, emphasizing both the promise of improved outcomes and the challenges of interpretability, data quality, and regulatory oversight.[31] Within radiology, a focused review of AI for radiology report generation highlights rapid progress in vision–language models trained on chest X‑ray datasets, yet concludes that current systems are not yet capable of consistently producing high‑quality reports and require further improvements in data diversity, training strategies, and standardized evaluation metrics before they can match human experts.[13] Reviews of AI in pancreatic cancer and Parkinson’s disease similarly document promising results in early diagnosis, characterization of tumor microenvironments, prediction of immunotherapy responsiveness, and smart rehabilitation, but stress that translational impact is constrained by complex biological heterogeneity, data integration challenges, and the need for rigorous clinical validation.[16][18] These findings together suggest that AI will significantly augment, but not replace, clinicians in the next five years, with the most impactful advances likely to come from tightly integrated, co‑designed systems rather than fully autonomous decision‑makers.[31]

AI is also reshaping scientific workflows beyond medicine. Surveys of federated learning in IoT highlight how privacy‑preserving collaborative training can harness the vast amount of data generated by interconnected devices in domains such as smart cities and industrial automation, addressing concerns about data security and regulatory compliance.[33] Reviews of soft computing techniques—neural networks, genetic algorithms, fuzzy logic, probabilistic reasoning—emphasize their utility for handling uncertainty and imprecision in complex systems, and argue that hybrid methods combining multiple soft computing approaches will be central to future intelligent systems in engineering and business.[15] Reviews of embedded AI and wearable technologies further suggest that the central research question is shifting from whether devices can sense and compute daily activities to how they can generate *reliable, interpretable, secure, and fair* intelligence under constraints of energy, latency, comfort, and trust.[20][36] In sum, AI’s most significant near‑term impact in science is likely to come from its role as an “enabling substrate” woven into sensors, instruments, analytical pipelines, and infrastructure control systems, rather than as a standalone technology.

Biomedicine, Genomics, and Nanotechnology: Precision and Convergence

In biomedicine, three interlinked technological trajectories stand out in the literature as particularly influential over the next five years: AI‑integrated genomics and next‑generation sequencing, nanomedicine and nanoscale materials, and multi‑technological platforms for regenerative and precision medicine.[24][29][12]

The integration of artificial intelligence with next‑generation sequencing is described as “revolutionizing genomics” by enhancing data analysis, accuracy, and scalability across the entire sequencing workflow. A detailed review of AI in NGS emphasizes that AI‑driven tools, including machine learning and deep learning, now support experimental design, wet‑lab automation, and bioinformatics analysis, with applications spanning variant calling, epigenomic profiling, transcriptomics, and single‑cell sequencing. Convolutional and recurrent neural network architectures, as well as hybrid models, outperform traditional methods in tasks such as basecalling, error correction, and structural variant detection, and are particularly valuable in complex settings such as cancer subtyping, biomarker discovery, and therapy prediction. The review also highlights emerging AI roles in third‑generation sequencing, where long-read technologies require fast and accurate basecalling and epigenetic modification detection, suggesting that within five years, AI‑enabled pipelines will be standard in both research and clinical genomics. At the same time, challenges around data heterogeneity, model interpretability, and ethical use of genomic data, including privacy and consent, remain significant constraints on full realization of this potential.

Clinical studies and consensus documents demonstrate how genomic technologies are diffusing into specific disease areas. National consensus guidelines for genetic detection in multiple myeloma in China codify the integration of multi‑gene panels, copy‑number analysis, and sequencing into diagnostic and prognostic workflows, reflecting a broader trend toward embedding NGS into standard oncology care. High‑throughput genomic profiling of non‑small cell lung cancer in Indian patients via tissue and plasma‑based cell‑free DNA sequencing reveals targetable mutations and validates liquid biopsy approaches, highlighting both the potential and the challenges of applying NGS in diverse clinical settings. Whole‑genome and transcriptome analyses of bladder cancer cohorts and detailed characterization of 17p13.3 microdeletion and duplication syndromes in pediatric patients further underscore how NGS is elucidating tumor heterogeneity and genotype–phenotype correlations across diseases. However, systematic reviews of hereditary polyneuropathy in children show that despite higher diagnostic yields from targeted gene panels and whole exome sequencing compared to nerve conduction studies, integration into routine diagnostic pathways remains inconsistent, illustrating the translational gap between technological capability and clinical practice.

Nanotechnology and nanomedicine constitute a second major biomedical trajectory. Comprehensive reviews of nanotechnology in healthcare and medicine emphasize the “multipronged” nature of nanomedicine, with applications in drug delivery, imaging, diagnostics, tissue engineering, and regenerative medicine.[24][25][26] Early work in nanomedicine focused heavily on therapeutic and imaging nanoparticles designed to improve treatment efficacy and reduce toxicity through controlled drug biodistribution, but more recent analyses argue that the true transformational impact lies in broader applications, including immunotherapy, neurology, cardiovascular and infectious diseases, anti‑aging, and orthopedics.[29] Reviews of personalized nanomedicine highlight the potential for tailoring nanoscale therapies to specific patient cohorts, leveraging the versatility of nanocarriers to adapt drug formulations to biological and molecular profiles.[28] At the same time, critical assessments of nanomedicine’s trajectory note that expectations in the early 2000s were arguably inflated, and that realizing the full promise of nanotechnology will require systematic learning from the first wave of therapeutic applications, closer integration with clinical needs, and proactive engagement with regulators and clinicians.[29]

Dental and oral health provide a focused example of nanotechnology’s diffusion into everyday clinical practice. A review of nanodentistry documents how nanomaterials have improved dental biomaterials, enabling better mechanical and biological properties, enhanced wear resistance, and new diagnostic and preventive approaches, while predicting a significant rise in publications and applications through 2026.[23] A comprehensive review of contemporary dental restorative materials further shows how advanced materials, including nanocomposites and ceramics, have improved durability and patient comfort but also exposes persistent unpredictability between laboratory results and clinical outcomes, underscoring the need for more robust testing–performance correlations.[21] Beyond dentistry, bibliometric analysis of nanoparticles in microalgae biorefineries illustrates how nanomaterials are being applied to enable sustainable production of biofuels and high‑value compounds, with research clustering around nanoparticle toxicity, environmental impacts, and nanoparticle‑enhanced biorefinery processes. Experimental work on selenium nanoparticles synthesized using pyrazole‑based derivatives demonstrates enhanced in vitro anti‑diabetic and anti‑Alzheimer’s activity compared to the base compounds, signaling the potential of tailored nanoparticles as multi‑target therapeutic agents.[22] These studies collectively indicate that nanotechnology will increasingly underpin both specialized medical interventions and broader bio‑industrial processes.

Multi‑technological integration in regenerative medicine exemplifies a third trajectory, where organoids, 3D/4D bioprinting, single‑cell omics, and AI are combined to advance musculoskeletal regeneration.[12] A detailed review of these integrated technologies shows how organoids provide highly biomimetic in vitro models, 3D/4D bioprinting enables precise and dynamic fabrication of tissue constructs, single‑cell omics deciphers cellular heterogeneity during development and repair, and AI optimizes data‑driven personalized regenerative strategies.[12] The review explicitly distinguishes in vitro and in vivo applications, provides extensive tabular summaries of recent work, and outlines future directions and challenges, suggesting that over the next five years, progress will depend on overcoming bottlenecks in vascularization, immune compatibility, and the integration of multi‑scale data.[12] Similar integrative patterns are visible in oncology, where AI, nanotechnology, and NGS converge in areas such as AI‑assisted characterization of the tumor immune microenvironment, nanomedicine design for targeted drug delivery, and multi‑omics analysis of treatment response in pancreatic cancer immunotherapy.[16] Taken together, these literatures suggest that biomedical innovation in the near future will increasingly be characterized by the co‑development and integration of multiple enabling technologies rather than isolated advances.

Energy, Environment, and Mobility: Toward Intelligent Green Systems

In the domains of energy, environment, and mobility, future technologies are framed around the dual challenge of meeting rising energy and mobility demands while reducing greenhouse gas emissions and environmental impacts. Reviews of renewable energy integration, green industry evolution, urban sustainability, and electric vehicles provide a consistent picture of near‑term technology trajectories and their potential contributions to decarbonization.[5][6][9][10]

A foresight‑oriented review of renewable energy integration in construction engineering emphasizes that the construction sector, as both a major driver of development and a large consumer of energy, faces pressing needs to reduce carbon emissions through the incorporation of renewable energy technologies.[5] The review synthesizes emerging trends in solar photovoltaics, wind energy, geothermal heat pumps, bioenergy solutions, and high‑tech energy storage, arguing that the integration of these technologies into buildings and infrastructure offers significant opportunities to improve energy efficiency and sustainability.[5] Digital technologies such as building information modeling and predictive analytics are highlighted as key tools for optimizing energy use, predicting maintenance, and ensuring system stability, pointing toward increasingly intelligent, data‑driven construction and building management systems in the next five years.[5] The review also adopts a unified foresight framework that integrates technological, digital, environmental, and social justice components, underscoring the importance of equity and inclusion in the deployment of green technologies.[5]

At the macroeconomic level, structural transformation toward medium‑ and high‑tech industries is shown to have complex temporal effects on environmental outcomes. An econometric study of BRICS economies finds that medium‑ and high‑technology sectors do not significantly impact CO\(_2\) emissions in the short term, but their long‑run effects are statistically significant and environmentally beneficial, suggesting that technological upgrading reduces emissions only after a time lag.[6] These findings lend support to the idea that environmental regulation and industrial upgrading, when pursued together, can strengthen competitiveness and improve environmental outcomes, but they also caution that short‑term assessments may underestimate long‑term benefits.[6] In rapidly industrializing regions such as ASEAN, a study of carbon emissions determinants from 1995 to 2022 using second‑generation econometric techniques finds that advancements in climate technology, stringent environmental regulations, industrial modernization, and green energy expansion significantly reduce CO\(_2\) emissions, whereas economic growth and urbanization remain emission‑intensive.[9] The authors derive region‑specific policy insights for balancing industrial development with environmental sustainability and frame their analysis as a roadmap for achieving post‑COP28 Sustainable Development Goals in emerging economies over the coming years.[9]

Transportation is another critical arena of technological change. A comprehensive review of electric vehicle technologies synthesizes recent advances in battery electric vehicles, hybrid and plug‑in hybrid vehicles, and fuel cell electric vehicles, with particular attention to emerging battery technologies such as solid‑state, sodium‑ion, lithium–sulfur, and metal–air batteries.[10] The review reports projections that the global EV battery technology market will grow from approximately 98.65 billion dollars in 2025 to 156.95 billion dollars in 2031, reflecting strong expectations of market expansion.[10] It also highlights technical obstacles, including range constraints, charging time, battery prices, and infrastructure shortages, and presents solid‑state batteries and ultra‑fast charging networks as promising, but not yet fully mature, solutions.[10] Complementary reviews of inductive wireless power transfer for EV charging describe rapid progress in high‑frequency power electronic converters, resonant compensation networks, coil designs, and dynamic wireless charging systems, identifying significant increases in transfer efficiency, misalignment tolerance, and bidirectional capabilities, alongside ongoing challenges in efficiency at distance, safety, and standardization. These findings collectively suggest that over the next five years, EV technologies will advance significantly, but continued investment in charging infrastructure, grid integration, and material innovation will be necessary to overcome remaining constraints.

Smart grids and dynamic grid management techniques are essential to accommodating higher penetrations of renewable energy and electrified transport. A review of dynamic line rating technologies explains how real‑time data on conductor temperature, wind speed, solar irradiance, and ambient temperature can be used to determine the thermal limitations of overhead lines more accurately than static conservative ratings, increasing transmission capacity and grid efficiency.[38] The review underscores the importance of advanced sensor technologies, real‑time optimization algorithms, and machine learning applications for analyzing energy consumption and demand data, and notes that combining dynamic line rating with broader smart grid technologies enhances grid flexibility and resilience through improved demand forecasting, monitoring, and decision‑making.[38] Reviews of passive IoT communication and embedded AI further indicate that low‑cost, battery‑free sensors and edge intelligence will play crucial roles in monitoring infrastructure, optimizing energy distribution, and orchestrating large numbers of devices in future grids and buildings.[35][20]

Sustainable entrepreneurship and green innovation literatures reinforce the view that technological change in energy and environment is intertwined with new forms of enterprise and policy. A bibliometric and integrative review of sustainable entrepreneurship and green innovation in emerging economies finds rapid interdisciplinary expansion, identifies climate change and entrepreneurship integrating AI as central “motor themes,” and highlights green innovation and circular economy as fast‑growing frontiers. The analysis emphasizes that while research output is increasing, careful construct separation between entrepreneurship and innovation outcomes and longitudinal designs are needed to clarify causal relationships, implying that over the next five years, more robust empirical work will be required to translate technological possibilities into sustainable business models. Digital transformation reviews similarly stress that technology‑enabled solutions can enhance organizational performance, financial inclusion, and digital governance, but that realizing national interest benefits requires aligning technological change with institutional capacity and inclusive development strategies.

Space, Resources, and Off‑Earth Habitats: From Concept to Early Implementation

Space technologies occupy a distinctive place in future technology discussions because they combine ambitious long‑term visions—such as space colonies and asteroid mining—with near‑term steps in exploration, robotics, and governance that are already underway.[41][42][48] A report by UNESCO’s World Commission on the Ethics of Scientific Knowledge and Technology notes that space exploration and utilization have entered a new phase, with rapid expansion of satellite activities and emerging projects such as lunar settlements and asteroid mining raising ethical questions about protection of extraterrestrial environments, dual use of space technology, and fair distribution of benefits.[41] The report calls for ethical frameworks and concrete recommendations to guide responsible exploration and utilization, recognizing that the increasing number of public and private actors in space will make governance a central challenge over the next decade.[41] In parallel, legal analyses of the Artemis Accords and safety zones for lunar activities highlight how new soft‑law instruments and proposed operational concepts aim to facilitate commercial and scientific activities, including bases and mining, while remaining consistent with the Outer Space Treaty’s non‑appropriation principle.[50] The proposed algorithms for establishing, altering, managing, and terminating safety zones exemplify the kind of operational detail that will be required to make space resource utilization practically and legally viable in the near term.[50]

Space mining and asteroid resource utilization are prominent focal points in the literature on future space technologies. Studies of trajectories and propulsion for near‑Earth asteroid mining evaluate the economic viability of different mission architectures, comparing high‑thrust chemical propulsion with continuous low‑thrust solar‑sail trajectories for transporting resources to geostationary orbit or lunar gateways.[47] Results indicate that solar sails offer a larger set of suitable target asteroids and higher net present value than chemical propulsion, especially when transporting volatiles to a lunar gateway, suggesting that solar sailing may be a promising technology for economically viable asteroid mining operations in the coming decades.[47] Analyses of multiple asteroid retrieval missions using reusable spacecraft and cislunar infrastructure around Earth–Moon Lagrange points similarly argue that reusable rockets and refueling capabilities can increase delta‑v budgets and mission flexibility, allowing retrieval of dozens of asteroids and thousands of tons of minerals over a thirty‑year period starting in 2030.[46] Earlier conceptual work on an L1 base with linked asteroid mining as an X‑Prize concept reinforces the idea that cislunar infrastructure can serve as a catalyst for broader space enterprise.[49] Meanwhile, contemporary mission profiles, such as the Hayabusa2 extended mission’s planned flyby of near‑Earth asteroid Torifune and rendezvous with 1998 KY26, demonstrate the gradual accumulation of engineering and scientific experience necessary for future resource‑oriented missions.[42]

Public attitudes appear to be more favorable toward asteroid mining than toward terrestrial frontier mining, which may facilitate the eventual development of space resource industries. A large cross‑national survey of attitudes toward space mining finds “broad support” for asteroid mining, with support levels well above the mid‑point and significantly greater than for mining the ocean floor, Antarctica, Alaskan tundra, or the Moon.[45] Unlike terrestrial mining, support for asteroid mining is largely non‑ideological, not strongly correlated with environmental worldviews or political ideology.[45] The authors argue that mining companies effectively have a “social license to operate” for asteroid mining but less so for lunar mining, indicating that social and political barriers may be lower for certain space activities than for terrestrial analogues.[45] At the same time, techno‑economic studies and ethical reports emphasize that technical feasibility must be matched by careful attention to environmental protection, benefit sharing, and long‑term sustainability to avoid replicating Earth‑bound extractive patterns in space.[41][43][47]

The broader vision of space colonies and off‑Earth habitats remains long‑term, but recent analyses trace how advances in reusable rockets, 3D printing, nuclear fission, and emerging fusion technologies are beginning to address some of the historical limitations of large‑scale space habitats.[48] A review of the technological evolution of space colonies discusses early concepts such as the Stanford Torus and Bernal Sphere and examines how modern innovations can replace dependence on lunar resources with asteroid mining, mitigate ecological fragility through hybrid energy systems, and shift from large‑scale construction to modular design.[48] Complementary work on civilization development frames space colonization as the next stage of civilizational development, contingent on achieving sustainable energy supply through nuclear fusion and developing mining and agricultural technologies adapted to lunar, Martian, and asteroid environments.[43] While these visions extend beyond the five‑year horizon, the technologies under development—reusable launch systems, in‑situ resource utilization, advanced energy systems, and robotic construction—are expected to undergo significant maturation within that period, laying foundations for more ambitious projects in subsequent decades.[42][46][48]

Information, Education, and Societal Systems: Human–Technology Co‑evolution

Future technologies are also transforming information systems, education, work, and social practices, often in less spectacular but more pervasive ways than in biomedical or space domains. In education, the literature documents both the diffusion of digital and AI‑enabled tools and persistent challenges in equitable and pedagogically meaningful integration.[1][19] A study of computer science education in Nigeria, for example, explores the integration of AI, virtual and augmented reality, gamified classrooms, and adaptive learning systems, finding that AI and virtual reality are widely embraced, while gamification and collaborative platforms remain underutilized due to infrastructural and cultural barriers.[1] Respondents cite poor internet connectivity, high costs of digital tools, and limited digital skills among educators as major challenges, underscoring that learner‑centered pedagogy, digital literacy frameworks, and inclusive practices are necessary to bridge the gap between theory and practice.[1] A case study of technological innovation in a Dominican school similarly finds that technological innovation significantly impacts educational management and teaching–learning processes, particularly in planning and organization, but that connectivity limitations, resource availability, teacher training, and lack of systematic integration constrain impact. In higher education, a multi‑country survey of educators, students, administrators, and instructional designers concludes that most stakeholders foresee more blended and hybrid instruction and modest increases in fully online courses post‑pandemic, but little expectation of revolutionary change, with student opinion more skeptical than others. These findings suggest that over the next five years, digital learning will likely continue to expand in a gradual, uneven manner, with the main challenge being meaningful integration rather than technology availability.

In language education, reviews of AI in English teaching highlight both benefits and risks. Chatbots, automated essay grading systems, and pronunciation apps are making lessons more interactive and feedback more immediate and personalized, enabling larger student cohorts and more precise progress tracking.[19] However, teachers face continual demands to adapt to new tools, often without adequate training, while concerns persist about over‑reliance on technology, marginalization of skilled educators, and limitations in assessing authentic conversational skills.[19] The review concludes that the most effective learning occurs when technology is thoughtfully blended with teachers’ expertise, pointing again to a future where human–technology collaboration, rather than replacement, is the dominant pattern.[19] Similar themes appear in qualitative sociology, where reflections on two decades of technological change argue that fears of replacement by digital tools and AI reveal underlying anxieties about disciplinary identity, but that AI can also act as a catalyst for renewal by foregrounding uniquely human capacities such as empathy, intuition, and embodied inquiry. The proposed competency profile for future qualitative sociologists integrates digital literacy and AI collaboration with renewed emphasis on resonance and ontological courage, exemplifying how sociotechnical futures are as much about evolving human roles as about technology per se.

In the future of work, particularly in sectors like digital banking, the literature points to hybrid human–technology systems as the likely configuration. A study on the future of work in digital banking analyzes how HR technology adoption and marketing innovation shape financial performance, finding that HR technologies significantly improve workforce efficiency, service quality, and cost control, which in turn enhance financial outcomes, and that marketing innovation strengthens the impact of HR technology on financial performance.[7] The authors interpret these results as evidence of a shift toward hybrid work systems in which employees collaborate with intelligent tools rather than being replaced, with digitally skilled employees better able to translate technological capabilities into customer value.[7] A scoping review on the future of banks more broadly identifies profitability, regulation, technology, and customer behavior as key challenges, and notes that future prospects vary widely depending on underlying theoretical assumptions, suggesting that narratives about the future of banking are themselves influential “performative” factors. Reviews of digital payment systems reinforce this view, showing that user adoption is influenced by trust, security, ease of use, and perceived risk, and that research is shifting from technical to behavioral and regulatory perspectives, indicating that policy and design choices will largely determine how digital payment technologies evolve.

Wearable technologies, augmented reality, and IoT systems further illustrate how human–technology interaction will evolve. A review of wearable technology in computer science portrays wearables as cyber‑physical systems integrating sensors, processors, networks, cloud services, machine learning models, and interfaces for applications in healthcare, sports, safety, education, accessibility, rehabilitation, and extended reality.[36] The central question is how to generate reliable, interpretable, secure, and fair intelligence given energy, latency, comfort, and trust constraints, with persistent challenges in data quality, interoperability, batteries, cybersecurity, consent, validation, and regulation.[36] An analysis of AR‑based training systems for law enforcement shows that immersive, adaptive training environments can significantly improve decision accuracy, decision time, cognitive efficiency, and trainee engagement compared to traditional training, but also raises questions about ethical protections, scenario realism, and integration into existing training regimes.[32] Studies of assistive technologies for people with noise sensitivity, such as the AudioBuddy app, demonstrate how sensing and tracking features can promote joint awareness between individuals and their companions, but also highlight technical limitations and the need for designs that support nuanced understanding of affective experiences.[34] These examples suggest that over the next five years, advances in sensing, feedback, and adaptive interfaces will increasingly support personalized, context‑aware interactions, but their value will depend heavily on careful attention to human factors and ethics.

To summarize this section, the literature across domains converges on a set of key findings about future technologies in science over the next five years. AI and embedded intelligence will become ubiquitous as enabling layers in devices, infrastructures, and scientific workflows; genomics, nanotechnology, and multi‑technology platforms will drive increasingly precise, personalized, and regenerative medicine; renewable energy, smart grids, and electrified mobility will advance toward more intelligent, integrated, and green systems; space technologies will proceed from conceptual visions toward early operationalization of resource utilization and governance frameworks; and digital tools in education, finance, and work will foster hybrid human–technology systems rather than outright replacement. The next section turns to the methods that researchers and policymakers use to study and anticipate these trajectories.

Methods Used in the Field: How Future Technologies Are Studied and Evaluated

Technology Foresight, Forecasting, and External Technology Search

Anticipating future technologies requires systematic methods for scanning, evaluating, and prioritizing emerging developments. The literature on external technology searching describes a family of methods—technology foresight, technology forecasting, technology intelligence, and technology scouting—that organizations use to identify and exploit new technologies for competitive advantage. A literature review of external technology searching methods aims to reduce conceptual confusion by clarifying the differences and overlaps among these terms, noting that inconsistent use in the literature can lead to missed opportunities for innovation and difficulty in communicating strategies. Technology foresight is characterized as a participatory, long‑term process involving multiple stakeholders and methods such as Delphi surveys, scenario planning, and expert panels to envision alternative futures, while technology forecasting is more quantitative and model‑based, aiming to predict trajectories based on trends and data. Technology intelligence and scouting focus more on systematic monitoring and identification of specific technologies and competitors, often using patent analysis, publication monitoring, and expert networks.

Book‑length treatments of technology foresight, such as the *Handbook of Technology Foresight*, and subsequent methodological critiques offer more detailed discussions of foresight concepts and practices. A multiple correspondence analysis of technology foresight methods evaluates different techniques based on criteria such as participation, time horizon, data sources, and analytical sophistication, providing insight into how combinations of methods can be tailored to specific contexts. The analysis suggests that no single method is sufficient; rather, effective foresight exercises blend qualitative and quantitative approaches, expert judgment and data‑driven modeling, and short‑ and long‑term perspectives. For example, combining scenario planning with bibliometric trend analysis and Delphi surveys can help reconcile divergent expert views and reveal hidden assumptions about technological trajectories.

The integration of economic complexity research with traditional foresight methods in small catching‑up countries illustrates how foresight can be grounded in empirical data on industrial capabilities and trade patterns. A study on small countries facing technological revolutions proposes a framework that merges economic complexity, competitiveness, and foresight to analyze opportunities and challenges for science, technology, and economic development. By mapping existing industrial structures and knowledge bases and linking them to potential future technologies, policymakers can identify realistic pathways for industrial upgrading and technological specialization. Such approaches are especially relevant for emerging economies seeking to leverage future technologies such as bioeconomy, green energy, or AI for sustainable development.

In marketing and innovation management, foresight methodologies are being incorporated into adaptive frameworks for evaluating the effectiveness of innovative marketing under uncertain future conditions. An article on transforming the theory of innovative marketing efficiency argues that traditional metrics such as ROI and KPI do not adequately account for the impacts of AI, digital transformation, changing consumer behavior, and global crises, and therefore proposes an adaptive methodology that uses AI, big data analysis, and foresight methods to better predict and assess marketing innovations. This kind of methodological innovation reflects a broader trend in which foresight is not only used to select technologies but also to evaluate and adapt organizational practices in light of technological change.

Bibliometrics, Scientometrics, and Research Mapping

Bibliometric and scientometric methods have become central tools for mapping emerging research fronts, evaluating the evolution of scientific fields, and identifying future technological directions. A bibliometric and integrative review of sustainable entrepreneurship and green innovation uses co‑citation analysis, thematic mapping, and annual growth rates to reveal rapid interdisciplinary expansion, identify motor themes and emerging niches, and highlight methodological gaps in construct separation and causal inference. Similarly, a bibliometric analysis of microalgae–nanoparticle research maps global trends from 2010 to 2025, identifying publication growth, leading countries and institutions, and four major thematic clusters: nanoparticle toxicity and environmental impacts, nanoparticle‑enhanced microalgae biorefinery for biofuel production, green synthesis of nanoparticles, and wastewater treatment applications. The analysis reveals Asia, particularly China and India, as leading contributors and indicates increasing scientific interest in nanotechnology‑enhanced bioresources, suggesting areas where future technological breakthroughs may occur.

Scientometric mapping of AI applications in cancer research in India provides another example of how bibliometric tools can illuminate national research landscapes and collaboration networks. An analysis of 1424 publications indexed in Web of Science between 2021 and 2025 shows a high annual growth rate in AI–cancer research, identifies leading authors, institutions, and journals, and highlights strong international collaborations, particularly with the United States, Saudi Arabia, the United Kingdom, and China. By mapping co‑authorship and co‑citation networks, the study identifies key thematic areas and influential works, providing a basis for understanding how AI technologies are being applied to cancer diagnosis, imaging, and decision‑making, and where future research may be most impactful. Similarly, a bibliometric analysis of financial literacy among banking product users in Nepal reveals publication trends, leading institutions, and thematic focuses around financial literacy, investment behavior, and financial inclusion, pointing to gaps in empirical, product‑specific, and digital financial literacy studies.

Systematic literature reviews of digital payment systems, algorithm and data structure visualization tools, and digital health technologies also rely heavily on bibliometric and thematic analysis methods. A systematic review of global digital payment systems identifies dominant theoretical frameworks such as the Technology Acceptance Model and Unified Theory of Acceptance and Use of Technology, and highlights trust, security, ease of use, and perceived risk as key factors influencing adoption, while calling for future research on cross‑platform integration, blockchain security, and digital financial literacy. A review of algorithm and data structure visualization tools examines ten recent systems in terms of visualization techniques, interaction methods, technological implementations, and pedagogical frameworks, finding that most tools focus on basic algorithms, lack adaptive learning support, and are not tightly integrated into educational environments, thus pointing to research directions for more holistic, pedagogically informed tools. A scoping review of methodological frameworks for digital health technology assessment identifies 26 frameworks worldwide, extracts domains and dimensions considered, and uses thematic analysis to propose a consolidated methodological framework for digital HTA. In each case, bibliometric and thematic methods provide structured ways to synthesize diverse literatures and identify both convergences and gaps.

### Health Technology Assessment and Evaluation of Digital/AI Technologies

Health technology assessment has emerged as a key methodological domain for evaluating future technologies in healthcare, particularly digital and AI‑based devices. A scoping review on the suitability of current HTA frameworks for AI‑based medical devices finds that data quality and integration are vital aspects of assessing technical characteristics, and that implementing specialized HTA for such devices faces practical challenges related to rapid technological evolution, extensive data requirements, model complexity and transparency, clinical validation and safety standards, regulatory and ethical considerations, and economic evaluation. The review concludes that adapting HTA processes through dedicated methodological frameworks for AI‑based devices can enhance comparability across evaluations and jurisdictions, and that defining necessary expertise is crucial for developing a skilled workforce capable of assessing AI technologies.

Parallel work on digital health technology assessment frameworks surveys 3061 studies and distills 26 methodological frameworks used globally for evaluating digital health technologies, such as mobile apps, telemedicine platforms, and digital therapeutics. Using thematic analysis, the authors identify domains commonly considered in dHTA, including clinical effectiveness, cost‑effectiveness, usability, organizational impact, ethical and legal issues, and patient experience, and propose a methodological framework based on the most frequently described domains. They argue that traditional HTA frameworks, designed primarily for pharmaceuticals and devices, must be adapted to account for the iterative, data‑driven nature of digital technologies, their dependence on user behavior, and their potential to reconfigure care pathways. Complementary work argues for integrating qualitative research into HTA to move “beyond clinical and cost‑effectiveness,” emphasizing the importance of assessing acceptability and subjective value, understanding context and perspectives, reaching groups other methods cannot reach, laying groundwork for quantitative studies, and contributing to economic model development.

Methodological systematic reviews within HTA also reveal weaknesses in current practice. A review of population‑adjusted indirect comparisons in HTA finds that most analyses are conducted or funded by pharmaceutical companies, that alignment of eligibility criteria and assessment of clinical and methodological heterogeneity is often incomplete, and that reporting on model fitting is frequently inadequate. These findings suggest that as AI, genomics, and other advanced technologies are evaluated, methodological standards for comparative effectiveness and indirect comparison will need to be strengthened to ensure robust and transparent decision‑making. An international survey of HTA stakeholders on integrating environmental sustainability into HTA shows that while there is widespread recognition of the importance of environmental considerations, relatively few organizations are actively implementing such integration, and approaches vary widely, with life cycle assessment and environmentally extended input–output analysis cited as promising methods but empirical evidence still scarce. As healthcare and technology systems face increasing pressure to reduce their environmental footprint, integrating sustainability into HTA will likely become more prominent over the next five years.

Systematic Reviews, Scoping Reviews, and Cross‑Domain Surveys

Beyond specialized methodologies, systematic and scoping reviews have become foundational tools for synthesizing evidence on emerging technologies across domains. In AI, comprehensive surveys examine applications across healthcare, finance, security, and sustainability, organizing studies by technique (deep learning, NLP, reinforcement learning, federated learning) and application area, and using structured taxonomies to identify common technical foundations and emerging trends.[11] In embedded AI, systematic surveys of literature from 2023 to 2026 examine hardware architectures, algorithmic lightweighting, and deployment paradigms, and provide structured perspectives on core advances and challenges.[20] In renewable energy integration, prospective‑based reviews synthesize trends across solar, wind, geothermal, bioenergy, and energy storage, while emphasizing future pathways and implications for policy and practice.[5] In electric vehicles, reviews systematically cover vehicle architectures, battery technologies, charging infrastructure, and market trends, offering meta‑analytical insights into technological and economic prospects.[10]

In health and biomedicine, systematic and scoping reviews of AI in healthcare, nanomedicine, and digital phenotyping for diseases like Parkinson’s disease provide structured assessments of current applications, benefits, challenges, and future directions.[31][24][18] For instance, the review of digital phenotyping and AI technologies in Parkinson’s disease systematically explores speech analysis, facial expression quantification, motion monitoring, and integration of AI with rehabilitation, identifying opportunities for early diagnosis and personalized therapy as well as challenges in technology implementation, clinical validation, and ethical regulation.[18] Similarly, reviews of nanotechnology in drug delivery and tissue engineering trace the evolution from discovery to applications, highlighting both advances and current challenges and projecting likely developments in the near future.[25][29]

Scoping reviews of digital learning and education, such as the post‑COVID future of digital learning in higher education, combine interviews and surveys to capture stakeholder views and identify expected trajectories for blended and online learning. Reviews of emerging trends in ethnomusicology and Chinese musical heritage research adopt thematic analysis of selected studies to reveal how digital technologies, AI, virtual platforms, and computational tools are reshaping the documentation, analysis, and transmission of musical traditions. Across these domains, systematic and scoping reviews serve both to consolidate knowledge and to signal which technologies and applications appear most promising or problematic, thereby influencing research funding, policy priorities, and industrial strategy.

Finally, some methodological work focuses directly on technology validation processes in startups and innovation ecosystems. A desk‑research‑based analysis of technology validation in startups identifies key trends, barriers, and research gaps, finding that lack of standardized tools for innovation assessment and limited managerial competencies are significant obstacles to commercialization. The study notes that Technology Readiness Levels, though a recognized standard, require adaptation to early‑stage startup contexts and that the “valley of death” between prototype and commercialization remains a critical barrier. It suggests that future research should aim to develop standardized yet flexible validation methodologies that can be applied across diverse technology domains, thereby improving the translation of emerging technologies from laboratory to market. This focus on validation underscores that methods for assessing and maturing technologies are as important as the technologies themselves in shaping future trajectories.

Contradictions or Open Questions: Tensions, Risks, and Unresolved Debates

The Promise–Reality Gap: From Breakthroughs to System‑Level Impact

One of the most prominent themes in the literature is the gap between technological promise and realized, system‑level impact, especially in domains like nanomedicine, AI in healthcare, and digital learning. Critical reflections on nanomedicine argue that while contributions to medicine have been impactful, expectations that it would rapidly revolutionize treatment have not been fully met, in part because early emphasis on nanoparticle therapeutics and imaging may have overshadowed broader, systems‑level opportunities.[29] The authors contend that deeper impact will depend on using intrinsic nanomaterial properties to improve diagnostics, imaging, and therapies across immunotherapy, neurology, cardiovascular and infectious diseases, regenerative medicine, and anti‑aging applications, leveraging lessons from the first wave of therapeutic applications.[29] This perspective suggests that the next five years may see a shift from a focus on individual nano‑drugs to integrated nanotechnology‑based platforms, but also underscores the risk that translational and regulatory bottlenecks could continue to slow progress.[24][27][29]

In AI‑enabled healthcare, systematic reviews emphasize similar tensions. The review of AI in healthcare applications underscores that while AI systems show promise in surgical care, clinical decision support, and specialized domains like genomic interpretation, persistent challenges around interpretability, data representativeness, real‑world validation, and ethical and regulatory questions hinder widespread deployment.[31] Radiology report generation is a particularly revealing case: although vision–language models have improved rapidly, current models are not yet capable of consistently producing high‑quality reports and thus remain tools for augmenting radiologists rather than replacing them.[13] Reviews of AI in pancreatic cancer immunotherapy and digital phenotyping for Parkinson’s disease highlight impressive proof‑of‑concept results but point out that complex disease heterogeneity, limitations in available datasets, and integration into clinical workflows pose significant hurdles.[16][18] These assessments raise an open question about the pace and extent to which AI will move from research prototypes to routine clinical tools over the next five years, and what institutional changes will be required to support this transition.

Digital learning and educational technologies exhibit a comparable promise–reality gap. While numerous studies document positive effects of technology on motivation, planning, and management, they also reveal partial realization due to infrastructural limitations, inadequate teacher training, and lack of systematic integration.[1] The post‑COVID digital learning review notes that despite the rapid pivot to emergency remote instruction, stakeholders largely anticipate only modest increases in blended and online learning, not a wholesale transformation of higher education, reflecting skepticism about both the desirability and the feasibility of more radical change. These findings suggest that technological potential is necessary but not sufficient; pedagogical models, institutional incentives, and cultural norms will likely be decisive in determining whether educational technologies produce transformative or incremental change.

More broadly, evaluations of digital and AI‑based health technologies through HTA frameworks indicate that methodological and regulatory adaptation lags behind technological innovation. The lack of tailored HTA frameworks for AI‑based devices, the limited integration of environmental sustainability into HTA, and the heterogeneous and suboptimal conduct of population‑adjusted indirect comparisons all point to an institutional lag that could slow or distort the adoption of promising technologies. Over the next five years, the key open question is whether methodological innovations in evaluation can keep pace with technological innovations, and whether regulators and health systems can adapt processes quickly enough to support timely but safe adoption.

Data, Privacy, Ethics, and Governance

Across domains, data governance, privacy, and ethics emerge as critical, unresolved issues that may either enable or constrain future technologies. In AI‑based medical devices and digital health technologies, scoping reviews identify data quality and integration, model complexity and transparency, and regulatory and ethical considerations as central challenges. The iterative, data‑driven nature of AI systems, particularly those that learn from real‑world use, complicates traditional notions of product stability and accountability, raising questions about how to assess safety and effectiveness over time and how to allocate responsibility among developers, deployers, and clinicians.[31] Privacy concerns are particularly acute for genomic and digital phenotyping data, where re‑identification risks and potential misuse of sensitive information necessitate robust governance frameworks.[18] Federated learning is often proposed as a way to mitigate privacy risks by keeping data localized while enabling collaborative model training, yet reviews highlight challenges related to device heterogeneity, communication latency, and resilience to device failures, indicating that technical solutions must be matched by clear legal and ethical guidelines.[33]

In environmental and urban domains, the integration of technologies such as smart grids, IoT sensors, and city‑scale digital twins raises questions about surveillance, data ownership, and democratic control. While studies of agentic AI in smart grids and dynamic line rating emphasize operational benefits, they generally pay less attention to governance questions about who controls data and algorithms, how decisions are made transparent, and how risks are managed.[14][38] Similarly, passive IoT communication technologies promise low‑cost, battery‑free sensing for industrial and environmental monitoring, but systematic reviews note that integration with cellular networks and security considerations require careful design to prevent vulnerabilities.[35] As these technologies spread, the next five years will likely see intensified debates about how to regulate data flows and algorithmic decision‑making in critical infrastructure.

Space exploration and space resource utilization bring their own distinctive ethical and governance challenges. UNESCO’s report on the ethics of outer space emphasizes potential tensions between expanding satellite and mining activities and the protection of extraterrestrial environments, as well as issues of dual use and fair distribution of benefits.[41] Legal analyses of safety zones for lunar activities under the Artemis Accords explicitly grapple with concerns that safety zones could be perceived as de facto appropriation, potentially conflicting with the Outer Space Treaty.[50] While proposed algorithms for establishing and managing safety zones aim to ensure consistency with international law, the legitimacy of these arrangements will depend on broad international acceptance, which is far from guaranteed.[50][41] Public attitudes toward space mining are generally favorable for asteroids but less so for lunar mining, yet it remains unclear how these attitudes will evolve as specific projects move from concept to implementation.[45] A key open question is whether inclusive, multilateral governance mechanisms can be developed quickly enough to keep pace with technological capabilities.

Ethical debates also surround future assistive and augmentation technologies. In the field of augmentative and alternative communication (AAC), discussions about brain‑to‑speech implants and other advanced interfaces highlight concerns that dominant narratives about highly invasive technologies may crowd out consideration of more diverse and user‑driven futures. The ISAAC conference panel on “alternative futures for AAC” emphasizes the importance of centering the perspectives and preferences of people who use AAC in shaping technological trajectories, raising broader questions about who gets to imagine and define “desirable” futures for assistive technologies. Similar issues arise in qualitative sociology’s debates about AI, where fears of replacement coexist with recognition that AI can spur renewal by revealing what is uniquely human in social research.

Inequality, Access, and Global Perspectives

The literature repeatedly underscores that the benefits and burdens of future technologies are likely to be unevenly distributed across regions, institutions, and social groups, and that addressing inequality and access is a central open challenge. In education, studies in Nigeria and the Dominican Republic demonstrate that while AI, VR, and other technologies can support creativity, critical thinking, and improved educational management, infrastructural limitations, high costs, and limited digital skills among educators severely constrain adoption, leading to underutilization of more advanced tools such as gamification and collaborative platforms.[1] Digital learning reviews also highlight disparities in students’ access to connectivity and devices, suggesting that expanding blended and online education without addressing these disparities could exacerbate inequalities.

In health and biomedicine, access to advanced technologies like NGS, AI‑enabled diagnostics, and nanomedicine varies widely by country and health system. Scientometric analyses of AI in cancer research show strong growth and collaboration in countries like India, but also reveal concentration in certain institutions and journals. Bibliometric studies of microalgae–nanoparticle research reveal a leading role for countries such as China and India, with African contributions growing but still limited. These patterns suggest that while some emerging economies are becoming significant players in research, many low‑income countries remain largely peripheral, raising concerns about global inequities in access to future health and environmental technologies.[9]

Economic and industrial analyses further indicate that technological upgrading and green innovation may benefit countries with existing industrial and institutional capacities more quickly than those without. The study of medium‑ and high‑tech industries in BRICS economies finds long‑term environmental benefits but also implies that the benefits of technological transformation may accrue with a time lag and that policy coordination is needed to ensure competitiveness and sustainability.[6] Reviews of sustainable entrepreneurship and green innovation in emerging economies identify strong growth in research and policy interest but also call for stricter construct separation and more robust empirical designs to clarify how entrepreneurial activity translates into environmental and social outcomes. Digital transformation reviews emphasize that national economic interests and technological competitiveness depend on both technological and institutional capacities, suggesting that countries that fail to develop robust digital governance and educational systems may struggle to capture the benefits of future technologies.

Within countries, inequalities in financial literacy, digital skills, and regulatory protection also shape who benefits from technologies such as digital payments and online banking. Bibliometric analysis of financial literacy research in Nepal finds growing attention to financial inclusion and investment behavior, but identifies gaps in empirical, product‑specific, and digital financial literacy studies, implying that many users may adopt digital financial services without sufficient understanding of risks and rights. Studies on digital payments emphasize the importance of trust, security, and perceived risk in adoption, pointing to the need for policies that protect vulnerable users and build digital financial literacy. Over the next five years, a central open question is whether policy and educational initiatives can keep pace with technological diffusion to prevent widening gaps in access and capability.

Human Identity, Work, and the Role of Expertise

Finally, the literature raises profound questions about how future technologies will reshape human identity, work, and expertise. In primary care, qualitative studies of UK general practitioners’ views on AI indicate that many GPs believe that communication, empathy, clinical reasoning, and value‑based care cannot be fully automated and that AI will mainly reduce administrative burdens and support decision‑making rather than replace physicians. Ethical and sociological analyses echo this view, arguing that while AI can perform many analytic tasks, it also highlights the enduring importance of resonance, empathy, and ontological courage in fields like qualitative sociology and patient care. At the same time, reviews of AI in healthcare and embedded AI in wearables and IoT suggest that many routine tasks and data‑intensive analyses may indeed be increasingly automated, raising questions about how professional roles and training should adapt.[31][36]

In work and organizational settings, studies in digital banking and broader debates about the future of banks show that narratives about technological change strongly influence expectations and strategies.[7] The scoping review on the future of banks notes that overemphasis on particular theories, such as intermediation theory, can lead to overly negative future prospects, suggesting that the assumptions underlying future narratives deserve more explicit scrutiny. The digital banking study, by contrast, emphasizes how HR technology and marketing innovation can enhance performance when aligned, portraying a future of hybrid human–technology work systems where employees collaborate with intelligent tools.[7] These divergent narratives reveal an open question about how organizations will choose to deploy technologies: whether primarily to reduce labor and cut costs or to augment human capabilities and create new forms of work.

Assistive technologies and AAC futures further foreground questions of ownership and agency in technology design. The AAC panel discussion highlights how dominant narratives about brain–computer interfaces and implants can marginalize the preferences and values of people who use existing AAC tools, and calls for more participatory processes in imagining and designing future AAC technologies. Similarly, the AudioBuddy study on noise sensitivity shows how technologies can facilitate joint awareness and understanding between individuals and their companions, suggesting that future technologies can support not only functional tasks but also relational and affective dimensions of human experience.[34] The open question is whether such relational and participatory approaches will remain niche or become more central in the design of future technologies.

Taken together, these contradictions and open questions indicate that the trajectory of future technologies in science over the next five years is not predetermined. Outcomes will depend heavily on how societies address promise–reality gaps, data governance and ethics, inequalities, and evolving conceptions of human work and identity. The next section synthesizes cross‑cutting themes and implications from the reviewed literature.

Cross‑Cutting Synthesis and Implications for Science and Policy

Across the diverse domains surveyed in this review, several cross‑cutting themes emerge that help translate the literature’s detailed findings into broader implications for scientific practice and policy over the next five years. First, convergence and integration appear to be the dominant patterns of technological evolution. AI is not developing in isolation but is increasingly intertwined with genomics, imaging, nanotechnology, renewable energy systems, and space operations, creating complex socio‑technical assemblages that require interdisciplinary research and governance.[11][24][14][5][48] Multi‑technological platforms in musculoskeletal regeneration, where organoids, 3D/4D bioprinting, single‑cell omics, and AI are combined, exemplify this convergence and highlight the need for integrative experimental and analytical methods.[12] Embedded AI in wearables, smart grids, and industrial IoT further illustrate how computing is dissolving into material infrastructures, making it difficult to treat “technology” as a separate layer that can be governed independently.[20][14][36][35]

Second, the literature emphasizes that the locus of innovation is shifting from isolated devices or algorithms to systems‑level integration, validation, and governance. In smart grids, for example, the key challenges are not only algorithmic performance but ensuring stability, safety, interpretability, and regulatory compliance in real‑time, agentic control of critical infrastructure.[14] In AI‑based medical devices, the main barriers to adoption include data quality, integration with clinical workflows, interpretability, and regulatory frameworks, not merely model accuracy.[31] In digital learning, the effectiveness of technology depends on pedagogical integration, teacher training, and institutional support rather than on the technical sophistication of tools alone.[1] This systems‑orientation implies that future scientific and engineering work will increasingly involve co‑design of technologies with institutional processes, regulations, and human practices.

Third, methodological innovation in technology assessment and foresight is both a response to and a driver of technological change. As technologies become more complex, dynamic, and intertwined, traditional HTA frameworks and evaluation methods must evolve to account for iterative updates, environmental impacts, user behaviors, and socio‑ethical dimensions. Foresight methodologies that combine qualitative and quantitative approaches, and that are grounded in empirical data on economic complexity and research trends, can help policymakers and organizations identify realistic and socially desirable technology pathways. Bibliometric and scientometric mapping, by revealing research frontiers, collaboration networks, and thematic clusters, not only describe the state of science but also influence funding priorities and strategic decisions, thereby subtly shaping the future of technologies they map. Over the next five years, further development and institutionalization of these methods will be crucial for steering technological change.

Fourth, equity, inclusion, and environmental sustainability are increasingly recognized as integral to technology futures, but practical integration remains limited and uneven. Foresight frameworks in renewable energy and construction explicitly incorporate social justice components, yet real‑world deployment often lags behind normative aspirations.[5] HTA stakeholders acknowledge the importance of integrating environmental sustainability into technology assessment, but only a minority of organizations are actually doing so, and methodological approaches are still experimental. Educational and financial technology studies document significant disparities in access to connectivity, devices, digital skills, and financial literacy, suggesting that without targeted policies, future technologies may deepen rather than reduce inequalities.[1] Addressing these issues will require not only technical solutions but also investments in infrastructure, education, and governance capacities, particularly in low‑ and middle‑income contexts.[9]

Finally, the literature underscores that future technologies will profoundly reshape—but not simply replace—human work, expertise, and identity. In healthcare, education, social science, and finance, studies converge on the view that AI and digital tools are most likely to function as augmentative complements, taking over routine, data‑intensive, or administrative tasks while leaving relational, empathetic, and judgment‑based functions to humans.[31][19][7] This does not mean that all jobs are safe or that transitions will be smooth; rather, it highlights the importance of proactive re‑skilling, participatory design, and reconfiguration of professional roles.[7] The next five years will be a critical period for developing models of human–technology collaboration that enhance rather than erode human capabilities, dignity, and agency.

## Conclusion

Drawing on a broad array of systematic reviews, foresight studies, bibliometric analyses, and technology assessments, this literature review has examined future technologies in science over approximately the next five years, focusing on key technological trajectories, methods used to study and evaluate them, and contradictions and open questions that will shape their impact. The evidence indicates that AI—especially embedded, agentic, and privacy‑aware forms—will be a pervasive enabling technology across domains, from healthcare and genomics to energy systems, mobility, and space operations.[11][20][14][10][42] Biomedical innovation is poised to be driven by the convergence of AI, NGS, nanotechnology, and multi‑technological platforms, promising more precise, personalized, and regenerative medicine but also facing significant translational, ethical, and equity challenges.[24][29][12][16] Energy and environmental technologies will advance toward intelligent green systems integrating renewable energy, smart grids, dynamic infrastructure management, and electrified mobility, contributing to decarbonization while raising questions about governance, equity, and long‑term industrial transformation.[5][9][6][10][38] Space technologies will move from conceptual visions of mining and colonies toward practical steps involving mission design, cislunar infrastructure, legal frameworks, and ethical governance.[41][47][46][50][48]

Methodologically, the field is characterized by the growing use of foresight and external technology searching methods, bibliometrics and scientometrics, advanced HTA and digital health assessment frameworks, and systematic and scoping reviews across disciplines.[5][11][20] These methods not only describe technological trajectories but also play constitutive roles in shaping them by informing policy, funding, and organizational strategy. At the same time, significant contradictions and open questions remain regarding the promise–reality gap between laboratory breakthroughs and system‑level impact, governance of data and AI, integration of environmental sustainability into technology assessment, persistent inequalities in access and capacity, and the evolving role of human expertise and labor in AI‑enriched systems.[29][31][1][7]

For scientists, policymakers, and practitioners, the literature suggests several practical implications for the next five years. Interdisciplinary collaboration will be essential, as future technologies increasingly cross traditional domain boundaries. Investment in methodological innovation for technology assessment, foresight, and governance is as important as investment in the technologies themselves. Equity, sustainability, and human‑centered design need to be integral components of technology development, not afterthoughts. And perhaps most importantly, the ways in which societies choose to deploy and regulate future technologies—whether as tools for augmentation and collective flourishing or primarily as instruments of efficiency and control—will profoundly influence the scientific and social landscapes of the early 2030s and beyond.

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