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

arXiv: https://arxiv.org/abs/2302.00096 · HTML: https://mlsanity.com/paper/2302.00096/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2023.

## Built on by (most cited first, 30 of 37)

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- [TAMA: A Human-AI Collaborative Thematic Analysis Framework Using Multi-Agent LLMs for Clinical Interviews](https://mlsanity.com/paper/2503.20666/) (2025)
- [ClinicRealm: Re-evaluating Large Language Models with Conventional Machine Learning for Non-Generative Clinical Prediction Tasks](https://mlsanity.com/paper/2407.18525/) (2024)
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- [Beyond Recommendations: From Backward to Forward AI Support of Pilots' Decision-Making Process](https://mlsanity.com/paper/2406.08959/) (2024)
- [A Survey on Human-Centered Evaluation of Explainable AI Methods in Clinical Decision Support Systems](https://mlsanity.com/paper/2502.09849/) (2025)
- [A Framework for Longitudinal Health AI Agents](https://mlsanity.com/paper/2604.12019/) (2026)
- [Can AI Explanations Make You Change Your Mind?](https://mlsanity.com/paper/2508.08158/) (2025)
- [Reduced AI Acceptance After the Generative AI Boom: Evidence From a Two-Wave Survey Study](https://mlsanity.com/paper/2510.23578/) (2025)
- ["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 research](https://mlsanity.com/paper/2403.16895/) (2024)
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- [Augmenting Clinical Decision-Making with an Interactive and Interpretable AI Copilot: A Real-World User Study with Clinicians in Nephrology and Obstetrics](https://mlsanity.com/paper/2602.00726/) (2026)
- [Learning from Disagreement: Clinician Overrides as Implicit Preference Signals for Clinical AI in Value-Based Care](https://mlsanity.com/paper/2604.28010/) (2026)
- [Towards an Argumentative Foundation for Evaluative AI](https://mlsanity.com/paper/2608.07473/) (2026)
- [Over-Relying on Reliance: Towards Realistic Evaluations of AI-Based Clinical Decision Support](https://mlsanity.com/paper/2504.07423/) (2025)
- [Factors 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 Review](https://mlsanity.com/paper/2504.13183/) (2025)
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- [Survey for Categorising Explainable AI Studies Using Data Analysis Task Frameworks](https://mlsanity.com/paper/2507.10208/) (2025)

## 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.