Artificial intelligence (AI) in healthcare has the potential to improve patient outcomes, but clinician acceptance remains a critical barrier. We developed a novel decision support interface that provides interpretable treatment recommendations for sepsis, a life-threatening condition in which decisional uncertainty is common, treatment practices vary widely, and poor outcomes can occur even with optimal decisions. This system formed the basis of a mixed-methods study in which 24 intensive care clinicians made AI-assisted decisions on real patient cases. We found that explanations generally increased confidence in the AI, but concordance with specific recommendations varied beyond the binary acceptance or rejection described in prior work. Although clinicians sometimes ignored or trusted the AI, they also often prioritized aspects of the recommendations to follow, reject, or delay in a process we term "negotiation." These results reveal novel barriers to adoption of treatment-focused AI tools and suggest ways to better support differing clinician perspectives.

翻译：人工智能在医疗保健领域具有改善患者预后的潜力，但临床医生的接受度仍是关键障碍。我们开发了一种新型决策支持界面，可为脓毒症提供可解释的治疗建议——这是一种危及生命的疾病，决策不确定性普遍存在，治疗实践差异很大，即使做出最优决策也可能产生不良后果。该系统构成了混合方法研究的基础，24名重症监护临床医生基于真实患者病例进行AI辅助决策。我们发现，解释通常能增强对AI的信心，但针对具体建议的一致性程度超出了先前研究中所述的二元接受或拒绝模式。尽管临床医生有时会忽视或信任AI，但他们也常常优先考虑建议中可遵循、拒绝或延迟的方面，我们将这一过程称为"协商"。这些结果揭示了以治疗为核心的AI工具在临床采纳中的新障碍，并提出了更好支持不同临床医生视角的途径。