FDA and Department of Veteran Affairs Set to Launch Virtual Lab Testing AI Tools, Subject to Trump’s Plans

On December 12, 2024, FDA’s Center for Drug Evaluation and Research (CDER) announced a new CDER Center for Real-World Evidence Innovation (CCRI). The CCRI is intended to serve as the focal point to promote more efficient and consistent communications across CDER offices regarding real-world data (RWD) and real-world evidence (RWE).

On December 5, 2024, FDA unveiled the draft guidance for industry regarding accelerated approval for drugs and biologics. This guidance provides additional information regarding the development of drugs and biologics to treat serious conditions for which there is an unmet need, and for which the sponsor is seeking accelerated approval. In particular, the guidance details the conditions for confirmatory study or studies that sponsors are required to conduct under the Federal Food, Drug, and Cosmetic Act (FD&C Act), as amended by the Consolidated Appropriations Act, 2023. This guidance also elaborates on the process for the expedited withdrawal of an accelerated approval. In announcing the availability of the draft guidance, the agency has requested comments be submitted by February 4, 2025.

Today FDA issued final guidance to provide recommendations for predetermined change control plans (PCCPs) tailored to artificial intelligence (AI) enabled device software functions. FDA recognizes that development of AI-enabled devices is an iterative process, and PCCPs are intended to allow developers to plan for modifications, while continuing to provide a reasonable assurance of safety and effectiveness. FDA provides that a PCCP should include planned modifications, a methodology to develop, validate and implement those modifications, and an assessment of an impact of those modifications. FDA initially introduced the concept of PCCPs in a 2019 white paper, and the Food and Drug Omnibus Reform Act of 2022 created provisions regarding PCCPs. For example, a supplemental application for a device that received Pre-Market Approval (PMA) or a new 510(k) is not required for a change to a device that would otherwise require a PMA supplement or a new 510(k) if the change is consistent with a PCCP approved or cleared by FDA. This final guidance is specific to AI-enabled devices, although PCCPs may be submitted for devices other than AI-enabled devices, and FDA has issued draft guidance that applies more broadly to all devices.

In this edition of Three Questions, health care & life sciences partner Nate Brown spoke with Jeremy Schutz, director of business development, recall & remediation at Sedgwick, to explore key strategies that pharmaceutical companies and medical device manufacturers can adopt to mitigate risks related to supply chain disruptions, cybersecurity threats and increased FDA enforcement of quality control and compliance.

On November 19, 2024, FDA issued a request for information (RFI) on per-and polyfluoroalkyl substances (PFAS) in seafood. Specifically, the agency is seeking scientific data and information from the seafood industry and other stakeholders and experts (e.g., academia, state and other federal agencies), on PFAS concentrations in seafood, the surrounding environment and processing water. The agency is also looking for mitigation strategies for reducing exposure to PFAS in seafood.

AdvaMed, the world’s largest medical technology association representing device, diagnostics and digital technology companies, released a white paper that reviews the current landscape of artificial intelligence (AI)-based applications and products in the health care sector, and identifies steps to accelerate the use of AI in medical technologies. The trade association highlights two main types of tasks that AI is uniquely well suited to tackle (1) identifying and analyzing patterns in patient charts that practitioners might miss; and (2) automating repetitive routine tasks. AI is being incorporated into a range of technologies in the health care sector, and AdvaMed’s white paper focuses primarily on AI/machine learning (ML)-enabled medical devices, which are regulated by the FDA. AdvaMed anticipates that FDA will likely need to issue additional guidance to keep pace with development of AI models, including for adaptive models and approaches to mitigating bias. AdvaMed endorses FDA’s use of “Predetermined Change Control Plans” (PCCPs), which permit manufacturers to outline approaches to future modifications as part of an initial submission, and states that PCCPs should evolve to allow for greater post-market modifications for adaptive algorithms. The trade association also calls for domestic and international harmonization of requirements, including development of common AI standards to advance safe, secure and trustworthy use of AI.

On November 21, 2024, FDA’s Center for Devices and Radiological Health (CDRH) announced a pilot program aimed at improving public notice about potentially high‑risk medical device recalls.

Researchers from the National Institutes of Health (NIH) have developed an algorithm that harnesses AI to help accelerate the process of matching potential volunteers for relevant clinical research trials. The algorithm, called TrialGPT, is intended to help clinicians navigate the vast range of clinical trials available to patients by identifying potential matches and providing a summary of how that person meets the criteria for study enrollment. The team of researchers used a large language model (LLM) to develop an innovative framework for TrialGPT and compared the algorithm to the results of three human clinicians who analyzed over 1,000 patient-criterion pairs. The team also conducted a pilot user study, where two human clinicians reviewed six anonymous patient summaries and matched them to potentially suitable clinical trials. When clinicians used TrialGPT as a starting point, they spent 40% less time screening patients and maintained the same level of accuracy. The research team was selected for the Director’s Challenge Innovation Award, which will allow the team to further assess the model’s performance and fairness in real-world clinical settings. The researchers “anticipate that this work could make clinical trial recruitment more effective and help reduce barriers to participation for populations underrepresented in clinical research.”