Researchers from the National Institutes of Health are making significant strides in the field of healthcare by leveraging large language models to develop an artificial intelligence framework. This innovative approach aims to streamline the clinical trial matching process and facilitate the quicker linking of potential volunteers to relevant trials listed on ClinicalTrials.gov. According to an NIH announcement this month, when benchmarked against three human clinicians, the tool, TrialGPT, achieved nearly the same level of accuracy. This discovery holds great promise for improving the efficiency and effectiveness of clinical trials.

Revolutionizing Clinical Trial Matching with AI

Streamlining the Clinical Trial Matching Process

Researchers at the National Library of Medicine and National Cancer Institute have developed the TrialGPT framework to address the time and resource-intensive nature of finding the right clinical trial for a patient. The new clinical trial matching algorithm analyzes patient summaries for relevant medical and demographic information. It then identifies clinical trials for which a patient is eligible and excludes those for which they are not. This annotated list of clinical trials, ranked by relevance and eligibility, allows clinicians to discuss trial opportunities with their patients more effectively. The AI tool also explains how a person meets the study enrollment criteria, which is crucial for its efficacy.

To assess the tool's performance, researchers compared TrialGPT's results to those of three human clinicians who evaluated over 1,000 patient-criterion pairs. This comparison showed that TrialGPT could help clinicians connect their patients to clinical trial opportunities more efficiently, saving precious time that can be better spent on harder tasks that require human expertise.

Advancing Health Equity through AI in Clinical Trials

The use of AI to improve patient recruitment, retention, and outcomes of clinical trials began before the launch of OpenAI's ChatGPT generative AI model. During the COVID-19 pandemic, oncology organizations sought ways to find patients across the country who would qualify for trials, even if they weren't physically present. Through healthcare data, increased AI adoption helped drive decentralized clinical trials and advance health equity and trial diversity.

Jeff Elton, CEO of ConcertAI, a vendor of data and AI SaaS platforms for clinical trial optimization, emphasized the importance of integrated digital trials. "With integrated digital trials, clinical studies are integral to the care process itself, versus being imposed on it," he said. "Trials don't need to place a higher burden on providers and patients than the standard of care."

Reducing Friction in the Clinical Trial Lifecycle

Reducing friction throughout the clinical trial lifecycle is critical to helping patients access trial therapies. Epic, an electronic health record vendor, implemented data-driven clinical trial matchmaking two years ago. Using its de-identified Cosmos data set, Epic allows providers to match clinical trial opportunities from sponsors and count their organization's eligible patients.

Many health systems have also tested using analytical applications that can surface clinical trial opportunities for patients using their organizations' EHR data. In October, Microsoft announced new AI tools that will enable health systems to build their custom AI tools for various administrative needs, including clinical trial matching. However, bias in AI remains a concern for clinical outcomes. Yale School of Medicine researchers have highlighted how bias can surface in any algorithm development pipeline and worsen healthcare disparities.

Conclusion

The research conducted by the National Institutes of Health and their collaborators has shown the potential of AI in clinical trial matching. TrialGPT has demonstrated its ability to save time and improve the efficiency of connecting patients to relevant trials. As AI continues to advance, it holds the promise of further improving healthcare outcomes and addressing the challenges faced in clinical trials.