ArticleJournal of medical Internet research2026
Informed Consent Disclosures and Minimum Requirements in AI Clinical Trials: Cross-Sectional Analysis.
Article in Journal of medical Internet research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe integration of artificial intelligence (AI) into clinical research challenges traditional informed consent (IC) frameworks due to the algorithmic complexity, opacity, and adaptive nature of AI systems. Although public demand for transparency regarding AI use in health care is high, current ethical guidelines lack specificity, and there has been no assessment of AI representation in IC documentation within clinical trial registries.
objectiveThis study aimed to evaluate the prevalence, clarity, and completeness of AI-related consent disclosures in clinical trials registered on ClinicalTrials.gov and to propose a framework for enhanced patient digital literacy and ethical robustness.
methodsWe conducted a cross-sectional content analysis of 114 AI-involving clinical trials with publicly available IC documents from ClinicalTrials.gov (search conducted on June 21, 2025). We assessed AI-specific disclosures, readability (using the Simple Measure of Gobbledygook index), document length, visual aid use, and data governance protocols against World Health Organization and National Institutes of Health standards. We also refined an AI risk framework encompassing model autonomy, deviation from standards of care, patient-facing interaction, and clinical risk, scoring each trial on a 3-tier scale.
resultsMore than half (66/114, 58%) of ICs failed to disclose the AI type or its intended use, and 18.4% (n=21) omitted risks entirely. Discrepancy was observed between trial registry entries and IC reporting of AI methods. Only 14% (n=16) of ICs met the dual criteria of brevity (<15,000 characters) and readability (Simple Measure of Gobbledygook <13). Higher-risk trials did not demonstrate improved readability (Spearman correlation P>.05). Only 11.4% (n=13) of ICs included visual aids, and their inclusion was not correlated with lower reading difficulty. Data handling protocols after participant withdrawal were inconsistent: 51 (44.7%) ICs provided no information, 30 (26.3%) specified data destruction, 29 (25.4%) allowed continued use, and only 4 (3.5%) offered participants a choice. Cited data protection laws varied widely, with no dominant standard.
conclusionsCurrent IC practices in AI clinical trials registered on ClinicalTrials.gov show a notable disconnect from ethical principles, with deficits in transparency, readability, and participant control over data. Our findings indicate a need for more standardized, participant-centered consent practices. We propose the "Minimum Requirements for Informed Consent in AI‑related Clinical Trials" as a possible framework to improve consent quality. However, it should be noted that these findings are limited to publicly available consent documents in the registry and may differ from final onsite versions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.