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

The commenter, representing an industry entity, supports the proposed pilot program as a way to drive efficiency and industry-wide adoption of real-time clinical trials. They argue that the pilot builds on successful previous standards-based regulatory advancements and will significantly reduce sponsor burden while improving data quality and integrity through AI-enabled automation.

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.

OmniScience, Inc. expresses strong support for the FDA's AI-Enabled Optimization of Early-Phase Clinical Trials Pilot Program, highlighting its potential to shorten drug development timelines and reduce costs. The company offers to serve as a technology partner and provides specific recommendations on pilot design, including prioritizing high-value use cases, ensuring diverse participant representation, and establishing interoperability standards.

ZS Associates Inc., a management consulting and technology firm, supports the FDA's proposed pilot program to assess AI-enabled technologies in early-phase clinical trials. They argue that the pilot will improve efficiency, speed, and decision-making quality by helping sponsors and the FDA stress-test critical assumptions and mitigate risks of late-stage regulatory failures.

DrKumo Inc., a connected health technology company, submitted joint comments with the Baim Institute for Clinical Research in support of the proposed AI-Enabled Optimization of Early-Phase Clinical Trials Pilot Program. They argue that the pilot should focus on continuous safety-signal detection and dose-escalation decision support, while providing specific recommendations on participant selection, collaboration models, and evaluation metrics to ensure the program's success and scalability.

BullFrog AI, Inc. strongly supports the proposed pilot program for AI-enabled optimization of early-phase clinical trials. The company argues that integrating advanced AI technologies, such as automated data harmonization and causal inference, can significantly reduce development timelines, minimize patient risk, and improve regulatory decision-making.

The Sylvester Comprehensive Cancer Center, an NCI-designated cancer center, supports the proposed AI-enabled pilot program to improve clinical trial operations, safety monitoring, and data quality. They advocate for a focus on high-risk therapeutic areas like oncology, the inclusion of robust data infrastructure and governance requirements, and the use of pre-competitive collaboration models.

Three academic researchers and industry experts (from Columbia University, Tulane University, and NouStarX LLC) support the FDA's pilot program for AI-enabled clinical trials. They argue that successful real-time clinical trials require a "Computational Evidence Infrastructure" that prioritizes data interoperability, human oversight, and continuous validation over just the implementation of AI technology.

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.

Sthenic Labs is submitting a formal response to the RFI, proposing a hardware-rooted architecture called the "Sthenic Protocol" to ensure data integrity in decentralized clinical trials. They argue that their technology provides a "physical root of trust" to prevent synthetic fraud and AI-generated data injection, enabling safer and more reliable AI-driven dose optimization.