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Artificial intelligence (AI) in healthcare has the potential to improvepatient outcomes, but clinician acceptance remains a critical barrier. Wedeveloped a novel decision support interface that provides interpretabletreatment recommendations for sepsis, a life-threatening condition in whichdecisional uncertainty is common, treatment practices vary widely, and pooroutcomes can occur even with optimal decisions. This system formed the basis ofa mixed-methods study in which 24 intensive care clinicians made AI-assisteddecisions on real patient cases. We found that explanations generally increasedconfidence in the AI, but concordance with specific recommendations variedbeyond the binary acceptance or rejection described in prior work. Althoughclinicians sometimes ignored or trusted the AI, they also often prioritizedaspects of the recommendations to follow, reject, or delay in a process we term"negotiation." These results reveal novel barriers to adoption oftreatment-focused AI tools and suggest ways to better support differingclinician perspectives.

Ignore, Trust, or Negotiate: Understanding Clinician Acceptance of AI-Based Treatment Recommendations in Health Care