Informed Consent Disclosures and Minimum Requirements in AI Clinical Trials: Cross-Sectional Analysis.

BACKGROUND: The 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. OBJECTIVE: This 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. METHODS: We 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. RESULTS: More 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. CONCLUSIONS: Current 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.