A key contribution of the review is a conceptual distinction the authors argue has been chronically blurred in the literature: the difference between AI-as-intervention and AI-for-trial-operations. When an algorithm is itself the treatment being tested—say, a diagnostic tool randomised against standard care—the relevant standard is prospective clinical evaluation, with explicit estimands, protocol-level oversight of model behaviour, and reporting under extensions such as SPIRIT-AI and CONSORT-AI. When AI instead supports trial logistics—feasibility modelling, prescreening, monitoring, or adjudication—the central questions become operational: Does it change workflow? Is it transportable across sites? Is it safe under real-world constraints? The two categories overlap, but they carry different burdens of proof, and conflating them has allowed retrospectively validated models to be promoted on evidence that would never suffice for a drug or device.

The review organises AI’s potential around three intersecting pillars: mechanism, measurement, and efficiency. Mechanism refers to network-medicine approaches that map disease pathways and link multi-omics data, phenotypes, and exposures to define rational patient stratification and biologically grounded endpoints. Measurement centres on digital health technologies—wearables and sensors that capture passive gait speed, respiratory patterns, arrhythmic burden, or step-level fatigue—offering dense longitudinal data on how patients actually function between clinic visits. Efficiency, the most commercially advanced pillar, involves large language models and natural language processing systems that convert free-text eligibility criteria into computable logic, match patients to trials, and extract structured evidence from unstructured records. The authors stress that these pillars are complementary rather than interchangeable, and that a system strong in one dimension cannot substitute for weakness in another.

The recruitment domain offers the most concrete evidence of benefit. Tools such as Criteria2Query and its successors translate eligibility criteria into database queries, while newer systems like TrialMatchAI use retrieval-augmented generation to process structured and unstructured patient data, retrieve candidate trials through hybrid search, and apply chain-of-thought reasoning at the level of individual criteria. In real-world evaluation, TrialMatchAI placed 92 percent of oncology patients somewhere within its top twenty trial recommendations, with expert assessment confirming accuracy above 90 percent. Even more striking, a randomised evaluation of a retrieval-augmented GPT-4 prescreening system for heart failure trials found that AI-assisted eligibility review matched the accuracy of experienced coordinators, reduced false-negative screening errors, and more than doubled prescreening throughput while maintaining transparent, citation-linked reasoning. Crucially, however, those gains materialised only when the AI was coupled to clinician review, site-specific workflow integration, and clear error-mitigation steps.

The authors repeatedly caution that operational AI cannot repeal biology or logistics. Machine learning models can now forecast trial accrual, duration, and the probability of early termination using protocol features, disease epidemiology, and historical site performance, and uncertainty-aware deep learning systems can generate interval-based enrolment estimates rather than single numbers. Digital twins—patient-specific counterfactual simulations—can even test protocol variations and enrichment strategies before a site is activated. Yet if a study depends on slow event accrual, long follow-up, or minimum safety exposure, no algorithm can eliminate the required observation time. A scoping review of 142 studies cited in the analysis found that most feasibility models remain retrospective and confined to individual risk domains, meaning that prospective decision-impact—the demonstration that a model changes what trial teams actually do, early enough to matter—has yet to be shown for most applications.

Endpoint assessment presents a subtler challenge. Digitally derived measures can reduce participant burden and increase measurement density, but the review warns that technological novelty often outpaces endpoint validity. An ambiguous clinical concept does not become more scientific when measured by a sensor; instead, uncertainty simply shifts from coordinators and endpoint committees to the algorithm. The remedy is rigorous endpoint specification: predefining the clinical construct, the derivation from raw data, the assessment threshold and window, and the handling of missing or poor-quality data. Natural language processing pipelines for adjudicating heart failure hospitalisations in cardiovascular trials have already reduced manual review while preserving agreement with clinician committees, and emerging LLM-enabled adjudication may scale this further—provided such systems are validated against adjudicated reference standards, tested for calibration and subgroup performance, and kept subordinate to predefined human review pathways.

In the inferential realm, machine learning methods for estimating heterogeneous treatment effects are spreading rapidly. A scoping review of 32 randomised trials found that causal-forest and related algorithms are now the most common tools for this purpose, and a causal-tree analysis of the DECAAF II trial illustrated how such methods can generate structured, hypothesis-generating insights about age-based differential benefit from fibrosis-guided ablation even when the overall intention-to-treat result was null. But the review insists these analyses be anchored to explicit ICH E9(R1) estimands, with pre-specification in statistical analysis plans, multiplicity control, overlap diagnostics, calibration checks, and Data and Safety Monitoring Board oversight. The same discipline applies to external control arms, where hidden confounding and unclear comparability can silently invalidate an entire comparison. These are not optional regulatory formalities, the authors argue; they are the mechanisms by which trials protect inference from operational complexity.

Governance gaps loom largest for continuously learning and agentic systems. The protocol should state whether a model is locked, periodically recalibrated, or adaptively updated, with defined update cadences, approval pathways, shadow-mode evaluation, drift thresholds, rollback triggers, and version freezes around interim analyses and database lock. Agentic AI—systems that plan, retrieve, and revise outputs across multiple steps—remains, in the authors’ assessment, an emerging architectural pattern rather than a mature standard of practice. Because failure can arise in intermediate steps such as retrieval, normalisation, ranking, or tool invocation, generic benchmark performance is insufficient for trial-critical use. Until stronger evidence exists, the review recommends confining such systems to bounded decision support in shadow mode: drafting eligibility logic, summarising protocol deviations, or prioritising safety review, never autonomously determining enrolment or altering endpoint definitions. Algorithmic bias compounds these concerns, since a prescreening tool with lower sensitivity in underrepresented populations produces not merely uneven performance but inequitable access to trials and a less generalisable evidence base.

The authors close with a practical agenda: prospective, randomised operational trials comparing AI-assisted and standard recruitment, site selection, adjudication, and monitoring; evaluation metrics that capture decision consequences—time saved, screen-failure burden, missed eligibility—rather than discrimination statistics alone; fairness monitoring embedded in the protocol itself, with denominator tracking at every stage from screening to outcome ascertainment; and data infrastructure capturing device versions, sampling cadence, and missingness patterns alongside raw streams. Privacy-preserving techniques such as federated learning earn a pointed caveat: they do not preserve privacy by default, since model updates can leak information, and they solve none of the deeper problems of inconsistent definitions, site heterogeneity, and subgroup imbalance. The bottom line is measured but firm. AI may accelerate learning and lighten workloads today, but trustworthy trials still depend on careful design, explicit human accountability, and—for better or worse—time for outcomes to emerge.

Subject of Research: The state of evidence, gaps, and governance for artificial intelligence across the clinical-trial lifecycle

Article Title: Artificial intelligence in clinical trials—state of the evidence, gaps, and next steps

Article References: Armoundas, A. A., Tarabanis, C., & Loscalzo, J. (2026). Artificial intelligence in clinical trials—state of the evidence, gaps, and next steps. eClinicalMedicine, 100, Article 104196. https://doi.org/10.1016/j.eclinm.2026.104196

Image Credits: AI Generated

DOI: 10.1016/j.eclinm.2026.104196

Keywords: artificial intelligence, clinical trials, large language models, eligibility screening, digital health endpoints, event adjudication, heterogeneous treatment effects, algorithmic bias, regulatory governance, federated learning, agentic AI, pharmacovigilance