Leveraging Generative AI for Clinical Evidence Summarization Needs to Ensure Trustworthiness

TL;DR

This paper discusses the potential and challenges of using generative AI, like large language models, for summarizing medical evidence in healthcare, emphasizing the importance of trustworthiness and accountability.

Contribution

It provides a perspective on the trustworthiness issues of generative AI in medical evidence summarization, highlighting the need for accountability, fairness, and inclusivity.

Findings

Generative AI can assist in medical evidence summarization.

Trustworthiness and fairness are critical challenges.

Developing accountable models is essential for clinical use.

Abstract

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.