AI-Enabled Optimization of Early- Phase Clinical Trials Pilot Program; Request for Information

Panoptive, a clinical trial oversight technology company, supports the FDA's pilot program for AI-enabled clinical trials. They argue that the pilot should focus on the "full oversight loop"—measuring how AI-surfaced signals are converted into accountable human decisions and inspection-ready records—rather than just signal detection speed. They provide specific recommendations on metrics, participant selection, and the need for human-in-the-loop governance to ensure reliability and regulatory compliance.

Any Biosignal, Inc. supports the proposed pilot program but recommends that it explicitly include continuous real-time biosignal monitoring and device-derived endpoints as eligible data sources. The company argues for a vendor-neutral infrastructure layer to allow for multi-vendor evaluation, uniform governance, and the establishment of public reference standards for data provenance and quality.

The commenter proposes a specific technical framework called RT-MSCCS to support the FDA's pilot program for AI-enabled clinical trial optimization. They argue that this system improves safety and efficiency by providing real-time dose escalation monitoring, mitigating alert fatigue, and aligning with NIST AI Risk Management Framework principles while maintaining human oversight.

Presentient Technologies Ltd, a biostatistics technology company, supports the proposed pilot program and proposes using its BRAKES system as a primary tool for early futility detection and real-time safety monitoring. They argue that their technology can improve ethical outcomes for participants and increase trial efficiency while maintaining data integrity through a secure, deterministic algorithm.

NexTrial.ai, an operating company developing AI infrastructure for clinical trial activation, supports the proposed pilot program and proposes a specific "proof certificate" architecture as a shared infrastructure standard. They argue that trustworthy AI in regulated environments requires a deterministic verification layer that produces verifiable, reproducible artifacts rather than relying on probabilistic confidence scores.

Amadeus Brandes, an independent researcher, supports the pilot program by recommending that the FDA require a lightweight, machine-readable "decision and amendment evidence record" for participating trials. The commenter argues that this record would provide a model-agnostic way to ensure transparency, auditability, and reconstructability of AI-informed decisions without requiring access to proprietary model internals.

Arclio LLC, the editor of the Proof of Insight (PoI) verification protocol, supports the pilot program but recommends that the FDA structure it around mechanically verifiable evidence rather than narrative audit logs. They argue for a distinction between process fidelity and analytical correctness, proposing a minimum evidence package that allows independent parties to verify AI-supported decisions.

Folia Health, a health informatics organization, supports the FDA's AI-enabled clinical trial pilot program but argues that it must prioritize the integration of real-time, patient-generated experience data. They advocate for specific evaluation metrics, governance requirements, and the inclusion of patient-reported outcomes to ensure the AI models provide a complete picture of drug impact and patient experience.

Burna AI, Inc., a company developing safety and data quality platforms for oncology drug development, supports the proposed pilot program for AI-enabled optimization of early-phase clinical trials. They recommend specific focus areas such as real-time safety monitoring, human-in-the-loop collaboration models, and the inclusion of workforce burden reduction as a key success metric.

FERZ, Inc., a company that builds runtime authorization infrastructure for AI governance, supports the pilot program but argues that the evaluation framework must distinguish between post hoc monitoring and pre-execution authorization. They recommend including specific metrics and criteria to ensure that AI-supported decisions in clinical trials are evaluated against governing constraints before they are acted upon.