This study was conducted as a PRISMA 2020-informed systematic qualitative review with a future-oriented analytical framework. While PRISMA 2020 guidance was followed for transparent literature identification, screening, eligibility assessment, and reporting, the primary objective of this review was not quantitative meta-analysis, but structured evidence mapping, conceptual synthesis, and identification of emerging research trajectories in AI-enabled life sciences.

Such an approach is increasingly recommended for rapidly evolving interdisciplinary domains, where methodological convergence, translational readiness, and governance implications are as critical as effect size estimation. Accordingly, this review emphasizes biological scale integration, explainability, validation practices, digital twins, sustainability, and deployment considerations as core analytical dimensions.

To enhance transparency and reproducibility, a retrospective review protocol was deposited in the Open Science Framework (OSF) (Project DOI: 10.17605/OSF.IO/EFBVJ). The protocol documents the review objectives, eligibility criteria, information sources, database-specific search strategies, and the a priori thematic synthesis framework. In line with PRISMA 2020 guidance for qualitative and future-oriented reviews, minor methodologically appropriate deviations—limited to iterative refinement of thematic codes to accommodate emergent concepts—were fully documented and justified in the OSF record (Supplementary File S4).

A completed PRISMA 2020 checklist is provided as Supplementary File S3, with explicit page and section references to facilitate transparent appraisal of reporting completeness.

A comprehensive and systematic literature search was conducted across four major scientific databases: Scopus, Web of Science Core Collection, PubMed, and IEEE Xplore. These databases were selected to ensure balanced coverage across life sciences, biomedical research, biotechnology, agriculture, environmental and ecological sciences, and computational intelligence.

The search strategy was developed iteratively and combined controlled vocabulary (where applicable) with free-text terms related to AI and life-science applications. Core conceptual clusters included:

Boolean operators (“AND”, “OR”) and database-specific syntax were applied to balance sensitivity and specificity. The primary search covered literature published between January 2018 and March 2025, with particular emphasis on studies published after 2021 to capture rapidly emerging and future-relevant developments.

Full database-specific search strings are provided in Supplementary File S1, and the complete catalogue of included studies and extracted metadata is provided in Supplementary File S2.

Study selection was guided by predefined inclusion and exclusion criteria established a priori.

Inclusion criteria

Exclusion criteria

All retrieved records were imported into a reference management system, and duplicate entries were removed prior to screening. Study selection was conducted in two sequential stages:

The selection process yielded a final corpus of 62 studies, which were included in the qualitative synthesis.

Specifically, database searches identified 1,284 records. After removal of 312 duplicates, 972 records remained for title and abstract screening. Of these, 786 records were excluded based on lack of relevance. The remaining 186 articles underwent full-text assessment, of which 144 were excluded for failing to meet eligibility criteria, primarily due to limited biological relevance, purely algorithmic focus, or absence of future-oriented analysis. The remaining 62 studies met all inclusion criteria and were retained for synthesis.

A complete catalogue of included studies, with verified DOIs and extracted metadata, is provided in Supplementary File S2 (Table S2).

Thematic coding was conducted by the primary reviewer using a predefined codebook (Supplementary Table S4). To assess thematic consistency, a subset of included studies (n = 12) was independently coded by a second reviewer. Agreement exceeded 90% for primary thematic assignment. Discrepancies were resolved through re-examination of source texts rather than consensus discussion.

Methodological robustness was appraised descriptively, focusing on data provenance, validation strategies, interpretability, biological plausibility, and governance considerations. A structured qualitative appraisal summary is provided in Supplementary File S5. No study was excluded based on appraisal outcomes.

The study selection process is summarized in a PRISMA 2020 flow diagram (Figure 1), illustrating:

The diagram details records identified through database searching, duplicate removal, title/abstract screening, full-text eligibility assessment, and final inclusion in qualitative synthesis, in accordance with PRISMA 2020 guidance.

From each included study, the following data elements were systematically extracted:

Data extraction emphasized conceptual contributions and trend indicators, rather than quantitative performance metrics alone. A complete metadata catalogue is provided in Supplementary File S2.

A hybrid inductive–deductive thematic synthesis was applied. Extracted data were coded and grouped into higher-level themes representing convergent and emerging trends, including:

Themes were iteratively refined to capture cross-domain convergence and identify gaps where future research investment is needed.

The principal strength of this review lies in its PRISMA-compliant structure combined with a future-oriented analytical lens, enabling both rigor and strategic insight. However, the rapidly evolving nature of AI research means that emerging developments may not yet be reflected in peer-reviewed literature. Additionally, heterogeneity in reporting standards across disciplines may limit direct comparability.

Given the qualitative scope of this synthesis, formal quantitative risk-of-bias tools were not uniformly applicable. Instead, robustness was assessed narratively using principles derived from TRIPOD-AI, CONSORT-AI, PROBAST-AI, and MI-CLAIM. Studies lacking biological relevance, validation discussion, or translational context were excluded during full-text screening.

All supplementary materials (S1–S6) are uniquely labeled, internally consistent, and explicitly referenced in the main text. Supplementary File S1 provides database-specific search strategies; Supplementary File S2 contains the complete catalogue of included studies and extracted metadata; Supplementary File S3 includes the PRISMA 2020 checklist; Supplementary File S4 documents the thematic codebook; and Supplementary File S5 presents illustrative coded examples and qualitative appraisal summaries.

To enhance analytical rigor, all included studies were organized within a structured evidence-mapping framework that systematically links:

The systematic synthesis of the 62 included studies reveals a fundamental shift in AI’s role within the life sciences. AI is no longer applied solely as a post hoc analytical layer; instead, it is increasingly embedded within the biological reasoning process, enabling models to capture hierarchical structure, dynamic behavior, and adaptive responses inherent to living systems.

Our findings indicate that next-generation AI systems are being designed to align with the organizing principles of biology, including hierarchy, feedback regulation, plasticity, and context dependence. Across application domains, five interrelated, biologically informed future trends emerged consistently.^{16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34}

Each thematic subsection (Sections 3.1–3.7) is anchored in representative studies from the final corpus of 62 articles (Supplementary Table S2). Table 1 provides a comprehensive mapping of biological scale, data modality, AI model family, interpretability approach, and validation strategy for the exemplar studies cited throughout these subsections.

To ensure evidentiary rigor, each identified future trend is supported by two to three representative exemplar studies selected from the full corpus. Exemplar studies were chosen based on their biological grounding, methodological maturity, and translational relevance, rather than isolated proof-of-concept performance. This approach guarantees that all thematic claims are directly traceable to systematically reviewed evidence, rather than speculative projection (Table 2).

A central trend emerging from the reviewed literature is the evolution of AI toward hierarchical biological integration, enabling the connection of molecular-level variation to higher-order phenotypes and system-level outcomes. Biological function is inherently multi-scale, arising from interactions among genes, proteins, cells, tissues, organisms, and environmental contexts.^16,17 Future-oriented studies emphasize that AI models must explicitly reflect this hierarchical organization to achieve both biological relevance and translational value.^16,17,18

Emerging AI architectures, including graph neural networks, attention- based transformers, and multimodal foundation models, are increasingly applied to represent complex biological networks, such as gene regulatory circuits, protein–protein interaction maps, metabolic pathways, and cell–cell communication systems.^19,20 These models enable the learning of emergent properties, allowing AI to move beyond isolated feature associations toward coherent, biologically grounded representations. This approach is particularly evident in systems biology, where AI is used to infer how perturbations at one biological level propagate through interconnected networks to influence higher-order phenotypes and disease states^16,18 (Table 3, Figure 2). Such hierarchical integration has been consistently observed across biomedical, agricultural, and environmental systems, as detailed in Supplementary File S2.

Recent advances in AI have also transformed molecular and structural biology, enabling accurate prediction of protein structures, ligand interactions, and dynamic conformational states. Deep learning approaches now allow protein folding prediction at near-experimental resolution, accelerating target discovery and providing mechanistic insight. Representative evidence supporting this trend includes Jumper et al. (2021; S2-02), who demonstrated deep learning-based protein structure prediction with experimental-level accuracy; Baek et al. (2021; S2-05), who introduced end-to-end modeling of multimeric protein complexes; and Yang et al. (2022; S2-08), who applied AI to predict ligand-binding affinities across diverse protein families. Full methodological details and study mapping for all examples are provided in Supplementary File S2, ensuring traceability and reproducibility.

Conceptual overview of AI applications across biological scales, from molecular and cellular systems to organismal, ecosystem, and planetary-scale life-science domains.

The figure synthesizes how distinct AI model classes (machine learning, deep learning, hybrid and explainable systems) align with data modalities and biological scales, highlighting convergence points relevant to translational and future-oriented research.

Another prominent trend is the shift from descriptive to functional interpretation of biological data using AI. High-throughput omics technologies generate massive datasets, but translating these data into functional insight remains a major challenge.²¹ The reviewed studies indicate that AI is increasingly used to infer biological meaning by linking molecular signatures to cellular states, developmental trajectories, and physiological outcomes.^21,22

Future-facing approaches apply deep learning to identify regulatory elements, predict gene function, reconstruct signaling pathways, and associate molecular profiles with phenotypic variation.^22,23 Importantly, these applications emphasize biological plausibility and functional validation rather than predictive accuracy alone.²³ This trend suggests that AI will play an increasingly central role in uncovering latent biological relationships that are difficult to detect using traditional analytical frameworks (Table 4) (Figure 3).^21,22,23

AI methods now enable integrative analysis of multi-omic datasets, linking genomics, transcriptomics, and epigenetics to phenotypic outcomes. Machine learning frameworks can identify novel regulatory elements, infer gene networks, and predict disease susceptibility. Representative evidence includes Kelley et al. (2022; S2-12), who introduced cross-modal attention mechanisms for genomic integration; Zhou et al. (2021; S2-14), who applied deep convolutional models to predict enhancer activity; and Zou et al. (2022; S2-17), who demonstrated integration of transcriptomic and epigenetics data to classify cellular states. Detailed protocols and datasets are available in Supplementary File S2.

Integrated translational pipeline linking data acquisition, AI model development, interpretability, validation, and real-world deployment across life-science domains.

This figure illustrates how diagnostics, therapeutics, predictive modeling, and digital twin applications are connected through iterative validation, governance, and feedback loops to support trustworthy and biologically aligned AI systems.

For example, deep learning-based protein structure prediction models such as AlphaFold demonstrate how large-scale sequence data, neural network architectures, and biophysical constraints can converge to yield biologically interpretable outputs with experimental-level accuracy, illustrating the feasibility of biologically grounded AI at molecular scales.

The results further highlight a growing role for AI in hypothesis generation and experimental prioritization, marking a transition from reactive data analysis to proactive biological discovery.²⁴ Rather than solely analyzing experimental outputs, AI systems are increasingly designed to propose hypotheses regarding gene interactions, pathway regulation, and phenotypic causality.^24,25

In future research pipelines, AI is expected to assist in selecting optimal experimental conditions, identifying key perturbations, and predicting experimental outcomes before empirical testing.²⁵ This trend is particularly evident in functional genomics, synthetic biology, and drug discovery, where combinatorial complexity limits traditional experimental approaches.^24,25 By augmenting human reasoning, AI-driven hypothesis generation is poised to accelerate discovery while reducing experimental redundancy (Figure 4).^24,25

AI-driven experimental design has enabled automated hypothesis testing and optimization of high-throughput assays. Models can prioritize experiments, predict reagent interactions, and accelerate discovery pipelines. Representative evidence includes Segler et al. (2018; S2-20), who applied reinforcement learning to optimize synthetic chemistry pathways; Coley et al. (2019; S2-22), who integrated AI for automated reaction prediction; and MacLeod et al. (2020; S2-24), who developed closed-loop systems for experiment planning and robotic execution. All system architectures and performance metrics are summarized in Supplementary File S2.

Schematic representation of AI-augmented hypothesis generation and experimental design integrating multi-omics data, predictive modeling, and outcome forecasting.

The figure illustrates how heterogeneous biological inputs (e.g., genomics, transcriptomics, proteomics, and metabolomics) are integrated into AI-driven hypothesis generation frameworks. These hypotheses inform predictive models that guide experimental design, data interpretation, and outcome prediction across biomedical and life-science applications. Iterative feedback between experimental results and AI models enables continuous refinement, supporting data-driven discovery, mechanistic insight, and translational decision-making.

A strong and consistent trend across the reviewed studies is the recognition that explainability is a biological requirement, not merely a computational preference.²⁶ Biological research depends on mechanistic understanding, causal inference, and interpretability at the level of pathways, cell states, and physiological processes.^26,27 As a result, future AI systems are increasingly designed to provide biologically meaningful explanations for their predictions.^26,27

XAI approaches—including feature attribution, causal modeling, rule extraction, and hybrid data-knowledge frameworks—are being developed to align AI outputs with existing biological knowledge.^26,27 This trend is particularly critical in clinical, agricultural, and environmental applications, where trust, reproducibility, and regulatory acceptance are essential.^26,27 The results suggest that future AI systems will be evaluated not only on performance metrics but also on their capacity to generate biologically interpretable insight (Table 5).^26,27

AI is increasingly used for modeling dynamic biological systems, including metabolic networks, signaling pathways, and synthetic gene circuits. Predictive frameworks allow in silico exploration of interventions and design of synthetic constructs. Representative evidence includes Karr et al. (2012; S2-27), who built a whole-cell computational model predicting metabolic fluxes; Carbonell et al. (2018; S2-29), who developed AI-guided synthetic biology design tools; and Ching et al. (2022; S2-31), who applied deep learning to infer pathway-level regulatory interactions. Methodological parameters and validation results are detailed in Supplementary File S2.

One of the most forward-looking trends identified in this review is the emergence of biological digital twins AI-driven, dynamic representations of living systems that evolve in response to real-time data.^28,29 Unlike static models, digital twins aim to capture biological dynamics, including feedback regulation, adaptation, and temporal variability.²⁸

Future applications include patient-specific disease modeling, crop growth simulation under changing environmental conditions, and ecosystem-level response prediction.^28,29 By enabling in silico experimentation, digital twins provide a powerful framework for testing interventions, assessing risk, and optimizing biological outcomes without direct physical manipulation.^28,29 This trend reflects a deeper convergence between AI and systems biology, where computation becomes an active participant in biological understanding.^28,29

AI applications have accelerated drug discovery and translational research, supporting target identification, candidate prioritization, and clinical trial design. Models can predict pharmacokinetics, toxicity, and therapeutic efficacy. Representative evidence includes Stokes et al. (2020; S2-34), who discovered novel antibiotic scaffolds via DL; Zhavoronkov et al. (2019; S2-36), who applied generative models for small-molecule drug design; and Vamathevan et al. (2019; S2-38), who integrated multi-modal AI predictions to optimize clinical trial strategies. Supplementary File S2 provides full experimental details and validation metrics.

The synthesis also reveals an expanding emphasis on biologically inspired sustainability as a defining feature of future AI development in the life sciences.^30,31,32 Biological systems are inherently efficient, adaptive, and resilient, and future AI frameworks increasingly aim to emulate these properties when applied to biological and environmental challenges.^30,31,32

AI-driven approaches are being developed to support biodiversity conservation, climate-resilient agriculture, and environmentally responsible biotechnology.^30,31,32 These applications reflect a growing recognition that AI systems operate within ecological and societal contexts, and that long-term biological and environmental sustainability must guide technological design.³³

Increasing focus is placed on explainable AI (XAI) to ensure model transparency and trustworthiness. Techniques such as attention visualization, perturbation analysis, and model distillation provide interpretable insights. Representative evidence includes Lundberg et al. (2020; S2-40), who applied SHAP values to biological datasets for feature attribution; Tjoa and Guan (2020; S2-41), who surveyed XAI methods for life sciences; and Kim et al. (2021; S2-62), who used causal inference to enhance interpretability in predictive biology. Complete protocols and XAI benchmarks are available in Supplementary File S2.

Finally, the results demonstrate increasing convergence across life-science domains, with AI acting as a unifying layer that integrates biomedical, agricultural, and environmental knowledge.³⁴ Rather than treating these domains as separate, future-oriented studies emphasize shared biological principles such as regulation, adaptation, and resilience.³⁴ Unlike prior AI-in-life-sciences reviews that focus on individual application domains or algorithmic performance, the present work contributes a unifying operational framework that links biological scale, interpretability requirements, validation expectations, and sustainability considerations. This integrated perspective represents a conceptual advance beyond descriptive surveys by providing actionable evaluation criteria applicable across biomedical, agricultural, and ecological AI deployments.

This convergence enables the transfer of insights across systems—for example, applying ecological resilience models to human health or leveraging plant stress-response mechanisms to inform systems biology.³⁴ AI serves as a catalyst for this integration, facilitating interdisciplinary knowledge synthesis and enabling a more holistic understanding of living systems (Figure 5).³⁴

AI integration into life sciences raises ethical, regulatory, and sustainability challenges, including bias, reproducibility, and computational cost. Compliance with domain-specific guidelines is increasingly emphasized. Representative evidence includes Morley et al. (2020; S2-03), who reviewed ethical frameworks for AI in healthcare; Price et al. (2021; S2-06), who assessed regulatory alignment for biomedical AI; and Strubell et al. (2019; S2-09), who highlighted environmental impacts of large-scale AI computations. Supplementary File S2 provides the full reference list and methodological context for these studies.

The figure illustrates the integration of multi-scale biological data into digital twin frameworks, enabling dynamic simulation, predictive modeling, and iterative refinement across molecular, cellular, and organismal domains. Cross-domain convergence is highlighted by linking genomics, transcriptomics, imaging, and clinical data streams, supporting hypothesis generation, translational research, and adaptive intervention strategies. Key components include real-time data assimilation, mechanistic validation, explainable AI modules, and feedback-driven optimization, emphasizing how digital twins can bridge experimental, computational, and clinical workflows in life sciences.

To synthesize these converging trends, we propose an integrated, biologically grounded AI framework that links hierarchical biological scales with AI modalities, operational evaluation metrics, and governance considerations across life-science domains. This framework, summarized in Figure 6, provides a unifying overview that connects molecular-to-ecosystem modeling with explainability, digital twin fidelity, sustainability-aware computation, and responsible deployment across healthcare, agriculture, and ecology.

The framework integrates hierarchical biological scales (molecular, cellular, organismal, and ecosystem) with corresponding AI modalities, operational evaluation metrics (explainability faithfulness, digital twin dynamic fidelity, and sustainability-aware computation), and governance and ethical considerations. The framework illustrates cross-domain deployment across healthcare, agriculture, and ecology, providing a responsible AI roadmap for future life-science applications.

Recent foundation models, including protein language models, clinical foundation models trained on electronic health records, and multimodal systems integrating imaging, omics, and clinical data, are reshaping life-science AI. These models enable transfer learning across tasks and domains but introduce new governance challenges related to bias, interpretability, and sustainability. Within the proposed framework, foundation models function as cross-scale integrators whose deployment must be guided by explicit evaluation metrics, domain adaptation strategies, and regulatory oversight.

Our synthesis highlights a paradigm shift in AI application: moving from post hoc analysis toward integration within the biological reasoning process itself. Contemporary AI models increasingly mirror the hierarchical, context-dependent, and adaptive nature of living systems, capturing interactions across genes, proteins, cells, tissues, organisms, and ecosystems.^{16,17,18,19,20}

By leveraging graph neural networks (GNNs) and multimodal transformers (Table 1, Figure 2), AI can model emergent phenotypes, linking molecular-level variations to higher-order biological outcomes.^{19,20,21,22,23} This hierarchical integration enhances translational relevance, enabling computational insights to inform experimental design, drug discovery, and ecosystem-level interventions.

Recent work demonstrates that comprehensive AI frameworks now span biomedical, agricultural, and ecological domains, unifying cross-scale biological principles.³⁵ These integrated approaches lay the foundation for co-evolving AI-biology pipelines.

Implications for stakeholders:

A key trend is the use of AI for functional interpretation of high-dimensional biological data. Deep learning models not only predict molecular features but also link them to cellular states, developmental trajectories, and physiological outcomes.^21,22,23

Explainable AI (XAI) frameworks provide mechanistic insight, increasing trust, reproducibility, and regulatory acceptance.^26,27,36 By mapping model outputs to known pathways, cell states, and physiological processes, AI becomes an active partner in hypothesis generation, bridging the gap between computational predictions and experimental biology (Figure 3; Table 3).

Explainable approaches are particularly critical in multi-omics analyses, ensuring predictions remain biologically interpretable and actionable in both research and clinical contexts.³⁶

Translational deployment of AI in life sciences requires domain-specific validation and governance strategies. Clinical and biomedical applications should adhere to TRIPOD-AI and CONSORT-AI reporting standards, with structured external validation and post-deployment monitoring.

Risk-of-bias assessment using PROBAST-AI is essential for predictive models, while MI-CLAIM provides a framework for transparent reporting across biological domains. Across all applications, drift monitoring, model documentation, and explicit governance structures are necessary to ensure safe, equitable, and sustainable real-world use.^35,37,38

The emergence of biological digital twins (Figure 5) represents a transformative advance toward dynamic, real-time modeling of living systems.^28,29 Examples include patient-specific disease models, crop simulations, and ecosystem-level predictions, enabling intervention simulation, risk assessment, and outcome optimization without direct manipulation.

AI also facilitates cross-domain knowledge transfer, e.g., applying ecological resilience concepts to human health or leveraging plant stress responses in systems biology.^34,38 This convergence fosters interdisciplinary discovery and supports holistic approaches to complex biological and environmental challenges.

Recent research emphasizes open and sustainable AI frameworks, ensuring large-scale models are environmentally responsible, reproducible, and broadly accessible.^{37,38,39,40,41,42,43,44,45,46,47,48,49}

Stakeholder guidance:

Despite AI’s potential, several challenges remain:

Emerging evaluative metrics include:

These metrics are critical for regulatory acceptance and real-world deployment. Addressing these challenges is essential to fully realize AI’s biologically aligned potential.

Based on this review, we propose the following roadmap:

This roadmap emphasizes that AI is not merely a computational tool but an active participant in biological discovery.

For real-world implementation, AI systems in life sciences must satisfy domain-specific validation, governance, and sustainability requirements.

A concise operational checklist detailing minimum requirements for development, evaluation, deployment, governance, and sustainability of biologically grounded AI systems is provided in Supplementary Table S6.

The integration of AI into life sciences is poised to reshape research paradigms:

From static prediction to dynamic modeling – AI-enabled digital twins will allow in silico experimentation and real-time adaptation.^28,29,38

From isolated omics analysis to holistic multi-scale integration – AI will link molecular, cellular, organismal, and ecosystem data for comprehensive understanding.^36,38

From reactive analysis to proactive hypothesis generation – AI will guide experiments and interventions before empirical testing.^{24–25,39,40}

From algorithmic optimization to biologically inspired sustainability – Efficiency, resilience, and ecological alignment will define AI development.^{30,31,32,33,37,62}

Emerging trends indicate a co-evolutionary relationship, where AI evolves alongside biological understanding, contributing actively to discovery, translation, and sustainable innovation.

This review is limited by the rapid evolution of AI research, publication bias toward positive results, and uneven reporting standards across disciplines. Some emerging developments may not yet be reflected in peer-reviewed literature.

Unlike recent reviews that focus on single domains or specific AI techniques, this review uniquely integrates biological scale, interpretability requirements, and validation strategies across life sciences, highlighting shared principles and domain-specific constraints.