以人为本的可解释智能医疗综述

Review of Human-Centered Explainable AI in Healthcare

随着人工智能(artificial intelligence,AI)的高速发展,“黑盒”模型已逐渐展示出逼近甚至超越人类的能力,尤其在智能医疗等高风险应用场景中,其可解释性是用户在应用中信任和理解AI的关键基础.虽然已有工作提供了大量事前和事后的AI可解释方法,但大都采用通用型解决思路,未考虑不同用户在不同场景下多维度的理解和信任需求.以人为本的AI可解释方法能够针对用户实际需求对AI模型进行可解释分析,近年来逐渐受到国内外学者的关注.文中聚焦智能医疗应用,对近5年人机交互国际顶级会议的文献进行分析,回顾现有辅助诊断、辅助用药、未病预警方面以人为本的AI可解释方法及系统,从决策时间花费、用户专业度和诊疗工作流程3个维度梳理和定位可解释需求的系统性方法,得出4类典型用户画像和对应案例;并从考虑资源受限、不同用户的多样需求、与现有流程结合3个方面,为如何设计可解释的医疗辅助诊断系统提出建议.

With the development of Artificial Intelligence(AI),“black box”models have demonstrated significant capabilities that now approach,or even surpass,human performance.However,ensuring the explainability of AI is crucial for users to trust and understand its applications in their daily lives,particularly in high-risk scenarios like healthcare.Although previous research has introduced numerous direct and post-hoc explainable AI methods,many of them adhere to a“one-fits-all”approach,disregarding the multidimensional understanding and trust requirements of diverse users in different contexts.In recent years,there has been growing attention from researchers worldwide towards human-centered explainable AI,which aims to provide explainable analyses of AI models based on the specific needs of users.This article examines literature reviews published over the last five years at top-tier global conferences in the field of human-computer interaction,with a specific emphasis on healthcare.It reviews existing human-centered,explainable AI methods and systems used for computer-aided diagnosis,com puter-aided treatment,and preventive disease warning.Based on this review,it explores and identifies explainability needs from three perspectives:decision time constraints,user expertise levels,and diagnosis workflow processes.Additionally,the article lists four classic user persona types along with respective examples and provides suggestions for designing explainable medical diagnostic systems,considering resource constraints,varying user needs across different stakeholders,and integration with existing clinical workflows.