在时间受限的医疗决策中为信任和协作设计AI：社会技术视角
Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens
摘要：
重度抑郁症是一种使人衰弱的疾病，影响着全世界2.64亿人。虽然有许多抗抑郁药物可用，但很少有临床指南支持选择它们。包含机器学习模型的决策支持工具（DST）可能有助于改善治疗选择过程，但由于系统集成不良，在临床实践中往往失败。我们使用一个迭代的协同设计过程来调查临床医生对在抗抑郁治疗决策中使用DST的看法。我们确定的方式，DST需要与医疗社会技术系统，包括临床流程，病人的喜好，资源限制和领域知识。我们的研究结果表明，临床DST应设计为多用户系统，支持患者-提供者的合作，并提供按需解释，解决预测和当前护理标准之间的差异。通过这项工作，我们展示了当前可解释人工智能的趋势可能不适合临床环境，并考虑为现实世界的医疗系统设计这些工具的路径。
英文摘要：
Major depressive disorder is a debilitating disease affecting 264 million people worldwide. While many antidepressant medications are available, few clinical guidelines support choosing among them. Decision support tools (DSTs) embodying machine learning models may help improve the treatment selection process, but often fail in clinical practice due to poor system integration. We use an iterative, co-design process to investigate clinicians' perceptions of using DSTs in antidepressant treatment decisions. We identify ways in which DSTs need to engage with the healthcare sociotechnical system, including clinical processes, patient preferences, resource constraints, and domain knowledge. Our results suggest that clinical DSTs should be designed as multi-user systems that support patient-provider collaboration and offer on-demand explanations that address discrepancies between predictions and current standards of care. Through this work, we demonstrate how current trends in explainable AI may be inappropriate for clinical environments and consider paths towards designing these tools for real-world medical systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
