Leveraging Generative AI for Clinical Evidence Summarization Needs to Ensure Trustworthiness
This perspective discusses trustworthiness issues in AI for clinical evidence summarization, which is incremental as it builds on existing concerns without introducing new methods or data.
The paper addresses the challenge of summarizing rapidly growing medical evidence using generative AI, highlighting the need for trustworthy models to ensure accountability, fairness, and inclusivity in clinical applications.
Evidence-based medicine promises to improve the quality of healthcare by empowering medical decisions and practices with the best available evidence. The rapid growth of medical evidence, which can be obtained from various sources, poses a challenge in collecting, appraising, and synthesizing the evidential information. Recent advancements in generative AI, exemplified by large language models, hold promise in facilitating the arduous task. However, developing accountable, fair, and inclusive models remains a complicated undertaking. In this perspective, we discuss the trustworthiness of generative AI in the context of automated summarization of medical evidence.