Pitt AI PathConnect: Bridging Innovation in Computational Pathology 2025

Regulatory and Ethical Gaps in AI-Based FDA Investigational Drug and Device Clinical Trials: A Systematic Review

Artificial Intelligence (AI) is revolutionizing FDA-regulated investigational drug and device clinical trials by optimizing patient recruitment, refining trial design, and accelerating data analysis. However, the rapid integration of AI into clinical trials has outpaced regulatory and ethical frameworks, creating challenges in bias mitigation, transparency, accountability, and compliance with FDA-specific guidelines such as 21 CFR Part 312 (Investigational New Drug Applications) and 21 CFR Part 812 (Investigational Device Exemptions). This systematic review identifies, categorizes, and analyzes regulatory and ethical gaps in AI-driven investigational drug and device trials under FDA oversight. Utilizing the PRISMA methodology, we examined sources including PubMed, IEEE Xplore, Scopus, Web of Science, and key regulatory documents from the FDA, NIH, and WHO. Key areas of concern include (1) the absence of standardized AI regulatory guidelines for investigational drug/device trials, (2) bias in AI-driven patient recruitment, (3) challenges in privacy and data security under HIPAA and GDPR, (4) transparency and explainability issues in AI decision-making, and (5) accountability and liability concerns for AI-related errors in clinical research. The findings underscore the need for harmonized AI regulatory policies, ethical frameworks, and interdisciplinary collaboration among regulators, sponsors, and researchers to ensure ethical AI adoption in FDA-regulated clinical trials.