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.

翻译：循证医学承诺通过利用最佳可用证据支撑医疗决策与实践，从而提升医疗服务质量。然而，来自不同来源的医学证据快速增长，给证据信息的收集、评估与综合带来了挑战。近期以大型语言模型为代表的生成式AI技术进步，有望推动这一艰巨任务的实现。但开发负责任、公平且包容的模型仍是一项复杂工程。本文从这一视角出发，探讨了生成式AI在医学证据自动总结场景中的可信度问题。