AI 中文总结
针对成人自闭症诊断复杂且缺专业医生的问题，通过采访临床医生确定挑战与策略，开发SIT-CARE系统，经评估发现其能使临床医生决策路径不同，在自闭症评估诊断中展现潜力，可助力经验不足医生。
AI 中文摘要
人工智能方法有望支持成人自闭症谱系障碍（ASC）的诊断，这一过程复杂且耗时，且缺乏专业临床医生。目前，临床医生的需求以及他们与基于人工智能的支持之间的互动尚未得到充分探索。我们的工作旨在开发和评估一个用于ASC评估的基于人工智能的临床决策支持系统（CDSS），并研究其如何影响临床医生的决策。通过采访不同经验水平的临床医生，我们确定了五个挑战并得出了设计策略。在此基础上，我们开发了SIT-CARE，一个CDSS，它提供基于人工智能的建议和临床相关非语言行为的数据可视化。通过对新招募临床医生的评估研究，我们发现SIT-CARE在ASC评估方面导致了不同的决策路径，这反映在临床医生的心理模型和决策变化中。总体而言，SIT-CARE在改善初始诊断评估、支持深入诊断和增强经验不足的临床医生能力方面显示出潜力。
英文摘要
AI methods promise to support autism spectrum condition (ASC) diagnostics in adults, a complex and time-consuming process, that is characterized by a shortage of specialized clinicians. To date, clinicians' needs and their interaction with such AI-based support remain underexplored. Our work aims to develop and evaluate an AI-based clinical decision support system (CDSS) for ASC assessment, and to investigate how it impacts clinicians' decision-making. By interviewing clinicians of varying experience levels, we identified five challenges and derived design strategies. Based on that, we developed SIT-CARE, a CDSS, which provides AI-based recommendations and data visualizations of clinically relevant nonverbal behavior. Through an evaluation study with newly recruited clinicians, we found that SIT-CARE led to different decision paths in regard to the ASC assessment, which are reflected in clinicians' mental models and decision changes. Overall, SIT-CARE demonstrated potential in improving initial diagnostic assessments, supporting in-depth diagnosis and empowering less experienced clinicians.
Comments29 pages, 9 figures
