mlsanityData to 7 Oct 2026

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

arXiv 2302.00096 ↗First posted 31 Jan 2023cs.HCcited by 37

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Abstract (arXiv)

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.

Key facts. Cited by 37 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Jan 2023.

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Citing papers per year: 2023: 8; 2024: 6; 2025: 13; 2026: 10.

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  1. 2023Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven decision supportcited by 65
  2. 2023In Search of Verifiability: Explanations Rarely Enable Complementary Performance in AI-Advised Decision Makingcited by 50
  3. 2024Towards Human-AI Deliberation: Design and Evaluation of LLM-Empowered Deliberative AI for AI-Assisted Decision-Makingcited by 38
  4. 2023Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosiscited by 30
  5. 2023A Systematic Review on Fostering Appropriate Trust in Human-AI Interactioncited by 21
  6. 2023Accuracy-Time Tradeoffs in AI-Assisted Decision Making under Time Pressurecited by 17
  7. 2024Position: Towards Bidirectional Human-AI Alignmentcited by 15
  8. 2023NLP for Maternal Healthcare: Perspectives and Guiding Principles in the Age of LLMscited by 14
  9. 2025Artificial Intelligence Should Genuinely Support Clinical Reasoning and Decision Making To Bridge the Translational Gapcited by 13
  10. 2025TAMA: A Human-AI Collaborative Thematic Analysis Framework Using Multi-Agent LLMs for Clinical Interviewscited by 9
  11. 2024ClinicRealm: Re-evaluating Large Language Models with Conventional Machine Learning for Non-Generative Clinical Prediction Taskscited by 8
  12. 2023Mind the Gap! Bridging Explainable Artificial Intelligence and Human Understanding with Luhmann's Functional Theory of Communicationcited by 7
25 more
  1. 2025Tempo: Helping Data Scientists and Domain Experts Collaboratively Specify Predictive Modeling Taskscited by 6
  2. 2023The Impact of Explanations on Fairness in Human-AI Decision-Making: Protected vs Proxy Featurescited by 3
  3. 2024Tell me more: Intent Fulfilment Framework for Enhancing User Experiences in Conversational XAIcited by 3
  4. 2024Beyond Recommendations: From Backward to Forward AI Support of Pilots' Decision-Making Processcited by 3
  5. 2025A Survey on Human-Centered Evaluation of Explainable AI Methods in Clinical Decision Support Systemscited by 3
  6. 2026A Framework for Longitudinal Health AI Agentscited by 3
  7. 2025Can AI Explanations Make You Change Your Mind?cited by 2
  8. 2025Reduced AI Acceptance After the Generative AI Boom: Evidence From a Two-Wave Survey Studycited by 2
  9. 2024"It is there, and you need it, so why do you not use it?" Achieving better adoption of AI systems by domain experts, in the case study of natural science researchcited by 1
  10. 2025Adaptive Human-Agent Teaming: A Review of Empirical Studies from the Process Dynamics Perspectivecited by 1
  11. 2025Data-Prompt Co-Evolution: Growing Test Sets to Refine LLM Behaviorcited by 1
  12. 2026Augmenting Clinical Decision-Making with an Interactive and Interpretable AI Copilot: A Real-World User Study with Clinicians in Nephrology and Obstetricscited by 1
  13. 2026Learning from Disagreement: Clinician Overrides as Implicit Preference Signals for Clinical AI in Value-Based Carecited by 1
  14. 2026Towards an Argumentative Foundation for Evaluative AIcited by 1
  15. 2025Over-Relying on Reliance: Towards Realistic Evaluations of AI-Based Clinical Decision Supportcited by 0
  16. 2025Factors That Influence the Adoption of AI-enabled Conversational Agents (AICAs) as an Augmenting Therapeutic Tool by Frontline Healthcare Workers: From Technology Acceptance Model 3 (TAM3) Lens -- A Systematic Mapping Reviewcited by 0
  17. 2025What Do People Want to Know About Artificial Intelligence (AI)? The Importance of Answering End-User Questions to Explain Autonomous Vehicle (AV) Decisionscited by 0
  18. 2025Survey for Categorising Explainable AI Studies Using Data Analysis Task Frameworkscited by 0
  19. 2025SimClinician: A Multimodal Simulation Testbed for Reliable Psychologist AI Collaboration in Mental Health Diagnosiscited by 0
  20. 2026Intelligent Reasoning Cues: A Framework and Case Study of the Roles of AI Information in Complex Decisionscited by 0
  21. 2026Within the MDT Room: Situated in Multidisciplinary Team-Grounded Agent Debate for Clinical Diagnosiscited by 0
  22. 2026Rethinking XAI Evaluation: A Human-Centered Audit of Shapley Benchmarks in High-Stakes Settingscited by 0
  23. 2026Designing AI-Supported Focus Groups: A Role x Modality Playbookcited by 0
  24. 2026From Continuous Predictors to Clinical Thresholds: Early Evidence on Performance Trade-offs of Guideline-Based Categorisation for Ischaemic Stroke Outcome Predictioncited by 0
  25. 2026Epistemic Trustworthiness in Generative AI: A Normative Framework for Warranted Reliance in High-Stakes Workflowscited by 0