Factors that influence the adoption of human-AI collaboration in clinical decision-making
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Abstract (arXiv)
Recent developments in Artificial Intelligence (AI) have fueled the emergence of human-AI collaboration, a setting where AI is a coequal partner. Especially in clinical decision-making, it has the potential to improve treatment quality by assisting overworked medical professionals. Even though research has started to investigate the utilization of AI for clinical decision-making, its potential benefits do not imply its adoption by medical professionals. While several studies have started to analyze adoption criteria from a technical perspective, research providing a human-centered perspective with a focus on AI's potential for becoming a coequal team member in the decision-making process remains limited. Therefore, in this work, we identify factors for the adoption of human-AI collaboration by conducting a series of semi-structured interviews with experts in the healthcare domain. We identify six relevant adoption factors and highlight existing tensions between them and effective human-AI collaboration.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 13 of its 65 references that are in the map. First posted on arXiv 19 Apr 2022.
Builds on
13 of 65 references.
References · 13
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Citing papers per year: 2024: 2; 2025: 2.
4 papers.
- 2024Evaluating Human-AI Collaboration: A Review and Methodological Frameworkcited by 27
- 2024D-Rax: Domain-specific Radiologic assistant leveraging multi-modal data and eXpert model predictionscited by 7
- 2025The Effect of Explainable AI-based Decision Support on Human Task Performance: A Meta-Analysiscited by 1
- 2025Human-AI Use Patterns for Decision-Making in Disaster Scenarios: A Systematic Reviewcited by 0
- 2025Human-AI Use Patterns for Decision-Making in Disaster Scenarios: A Systematic Review15 Sep 2025
- 2025The Effect of Explainable AI-based Decision Support on Human Task Performance: A Meta-Analysis22 Mar 2025
- 2024Evaluating Human-AI Collaboration: A Review and Methodological Framework9 Jul 2024
- 2024D-Rax: Domain-specific Radiologic assistant leveraging multi-modal data and eXpert model predictions2 Jul 2024