AI Do: Why the FDA Wants Your Input on AI in Clinical Trials (and Why You Should Give It)

The agency is asking how the pilot should be designed, what success should look like, and what guardrails are needed. Whatever standards emerge from this process may become the template for how AI is used in clinical development well beyond the pilot itself. This is a 30-day window to influence the regulatory framework before it is set. Traditional pharmaceutical companies, emerging longevity and healthspan biotech firms, digital health companies, AI vendors, clinics, patient groups, and investors all have a stake in the outcome.

What the FDA Is Proposing

The pilot program, titled “AI-Enabled Optimization of Early-Phase Clinical Trials,” would recruit sponsors pursuing early-phase trials through applications submitted to the Center for Drug Evaluation and Research, the Center for Biologics Evaluation and Research, and the Oncology Center of Excellence. The FDA describes early-phase trials as a “critical bottleneck” in drug development, characterized by high uncertainty, small patient populations, and inefficient decision making. It believes AI may improve patient recruitment, optimize dose escalation, enhance safety monitoring, enable adaptive designs, support earlier Phase 1 to Phase 2 decisions, and improve biomarker-based patient selection. 

The pilot will be guided by “trustworthy AI” principles aligned with the NIST AI Risk Management Framework, which emphasizes that AI systems should be valid, safe, secure, accountable, explainable, privacy-protective, and fair. (NIST is the National Institute of Standards and Technology, a federal agency within the US Department of Commerce that develops technical standards and frameworks used across government and industry. It does not regulate or enforce, but its frameworks often become the benchmark that regulators like the FDA adopt when setting expectations for emerging technologies.) The FDA is asking how to measure all of this: how to evaluate AI performance over time, how to assess fairness across demographic and clinical subgroups, how to handle model drift, and how to compare AI-enabled trials against traditional approaches. 

Why This Matters to You

If you are a traditional pharma or biotech sponsor, this pilot could help define the documentation, governance, and oversight standards the FDA expects whenever AI influences trial decisions. The RFI asks how to structure sponsor-FDA engagement, what support the agency should provide, and what infrastructure is needed. The answers may shape your regulatory interactions for years to come.

If you are in the longevity, healthspan, or wellness space, the relevance is equally direct. The RFI asks which trial types benefit most from AI, offering “first-in-human, oncology dose escalation, rare disease trials” as examples. Companies targeting age-related indications (frailty, sarcopenia, cognitive decline, metabolic dysfunction) rely heavily on these exact trial types and face distinctive challenges around non-traditional endpoints, novel biomarkers, and patient populations that may not fit conventional disease categories. If standards are designed only with traditional drug targets in mind, they may be poorly suited to healthspan-oriented research. 

If you make wearables, biosensors, or digital health tools, you are building the technology that generates the data AI systems will analyze. The FDA’s trustworthiness requirements (around drift, explainability, fairness, and privacy) will flow down to technology vendors through procurement and qualification requirements imposed by sponsors. 

If you build AI tools for patient matching, dose optimization, safety signal detection, or predictive analytics, this pilot could help establish the certification standards your products must meet to participate in FDA-regulated research. 

If you are a clinic, provider network, or compounding pharmacy in the longevity space, you interact with patient populations and hold real-world perspectives on recruitment, safety monitoring, and trial logistics that the FDA has specifically asked about. 

If you are an investor, the way AI is permitted to accelerate (or required to slow down) early-phase trials directly affects how quickly portfolio companies hit milestones. Understanding this framework early is a strategic advantage.

If you are a patient advocacy or consumer health organization, the FDA explicitly asks what role patient groups should play in AI governance. 

The Key Insight: ‘Performance’ Is No Longer Enough

The most significant signal in this RFI is the shift from evaluating AI tools on accuracy alone to evaluating them on governance. Companies will likely need to demonstrate how their AI is managed over its lifecycle, how it performs across populations and sites, how risks are mitigated, and what oversight processes exist. The FDA asks what evidence should demonstrate validity and reliability, how to evaluate safety and risk mitigation, what metrics assess transparency and explainability (including for proprietary systems), how to evaluate privacy and data governance, and how to assess fairness across subgroups. 

For large organizations with established compliance infrastructure, this is manageable. For smaller companies, these requirements could be burdensome, which is why the FDA also asks how the pilot can “accommodate varying levels of AI maturity across participants.” The standards can be scalable, but only if the FDA hears from organizations of different sizes about what is practical.

Bottom Line

The rules for AI in clinical trials are being shaped now. This is not a final rule or even a proposed regulation. It is the FDA asking what the future of AI in clinical trials should look like. Every organization that develops therapies, builds enabling technology, runs trials, enrolls patients, or funds clinical-stage companies has a reason to respond. The comment window is short. Companies that engage will help shape a framework they can work within. Companies that do not may be governed by standards designed without their input. 

For questions about this alert or assistance evaluating how the FDA’s evolving approach to AI in clinical trials may impact your organization, please contact one of the authors.