The Use of Large Language Models to Accelerate Literature Review Towards Digital Health Equity and Inclusiveness

Digital health technologies (DHTs) have revolutionized clinical trials, offering unprecedented opportunities to streamline processes, enhance patient engagement, and improve data quality. Growing technology device and broadband access are contributing to the increasing number of DHT-enabled trials. Ideally, DHTs have the potential to make clinical research more inclusive and diverse. However, while the variety in digital technologies and implementations present a strong display of healthcare innovation, major challenges arise concerning DHT generalizability and translation into real-world medical practice. In this study, we report our efforts in accelerating the literature review process related to the use of DHTs in randomized controlled trials (RCTs) by leveraging large language models (LLMs); identified in existing LLM task evaluations as possible tools supporting evidence harvesting scalability. We designed three tasks for automating title screening and information extraction of DHT-enabled RCTs using multiple LLMs, which yielded promising results towards large scale literature review.