# Factors that influence the adoption of human-AI collaboration in clinical decision-making

arXiv: https://arxiv.org/abs/2204.09082 · HTML: https://mlsanity.com/paper/2204.09082/

## 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 (most cited first, 13 of 13)

- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance](https://mlsanity.com/paper/2006.14779/) (2020)
- [Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making](https://mlsanity.com/paper/2001.02114/) (2020)
- [Learning to Complement Humans](https://mlsanity.com/paper/2005.00582/) (2020)
- [Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network](https://mlsanity.com/paper/1806.05473/) (2018)
- [Towards Unbiased and Accurate Deferral to Multiple Experts](https://mlsanity.com/paper/2102.13004/) (2021)
- [Hybrid Intelligence](https://mlsanity.com/paper/2105.00691/) (2021)
- [Combining Human Predictions with Model Probabilities via Confusion Matrices and Calibration](https://mlsanity.com/paper/2109.14591/) (2021)
- [The Role of Domain Expertise in User Trust and the Impact of First Impressions with Intelligent Systems](https://mlsanity.com/paper/2008.09100/) (2020)
- ["Brilliant AI Doctor" in Rural China: Tensions and Challenges in AI-Powered CDSS Deployment](https://mlsanity.com/paper/2101.01524/) (2021)
- [DEAL: Deep Evidential Active Learning for Image Classification](https://mlsanity.com/paper/2007.11344/) (2020)
- [Do you comply with AI? -- Personalized explanations of learning algorithms and their impact on employees' compliance behavior](https://mlsanity.com/paper/2002.08777/) (2020)
- [Lessons Learned from Designing an AI-Enabled Diagnosis Tool for Pathologists](https://mlsanity.com/paper/2006.12695/) (2020)

## Built on by (most cited first, 4 of 4)

- [Evaluating Human-AI Collaboration: A Review and Methodological Framework](https://mlsanity.com/paper/2407.19098/) (2024)
- [D-Rax: Domain-specific Radiologic assistant leveraging multi-modal data and eXpert model predictions](https://mlsanity.com/paper/2407.02604/) (2024)
- [The Effect of Explainable AI-based Decision Support on Human Task Performance: A Meta-Analysis](https://mlsanity.com/paper/2504.13858/) (2025)
- [Human-AI Use Patterns for Decision-Making in Disaster Scenarios: A Systematic Review](https://mlsanity.com/paper/2509.12034/) (2025)

## 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.