Human visual explanations mitigate bias in AI-based assessment of surgeon skills
Open accessAbstract
Artificial intelligence (AI) systems can now reliably assess surgeon skills through videos of intraoperative surgical activity. With such systems informing future high-stakes decisions such as whether to credential surgeons and grant them the privilege to operate on patients, it is critical that they treat all surgeons fairly. However, it remains an open question whether surgical AI systems exhibit bias against surgeon sub-cohorts, and, if so, whether such bias can be mitigated. Here, we examine and mitigate the bias exhibited by a family of surgical AI systems-SAIS-deployed on videos of robotic surgeries from three geographically-diverse hospitals (USA and EU). We show that SAIS exhibits an underskilling bias, erroneously downgrading surgical performance, and an overskilling bias, erroneously upgrading surgical performance, at different rates across surgeon sub-cohorts. To mitigate such bias, we leverage a strategy -TWIX-which teaches an AI system to provide a visual explanation for its skill assessment that otherwise would have been provided by human experts. We show that whereas baseline strategies inconsistently mitigate algorithmic bias, TWIX can effectively mitigate the underskilling and overskilling bias while simultaneously improving the performance of these AI systems across hospitals. We discovered that these findings carry over to the training environment where we assess medical students' skills today. Our study is a critical prerequisite to the eventual implementation of AI-augmented global surgeon credentialing programs, ensuring that all surgeons are treated fairly.
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The SAIS model showed an underskilling or overskilling bias at different rates across surgeon sub-cohorts.
“The SAIS model showed an underskilling or overskilling bias at different rates across surgeon sub-cohort.”
From Bias in AI-based models for medical applications: challenges and mitigation strategies (2023) · cites this paper as [4]
SAIS could reliably assess surgical performance but exhibited bias.
“In applying this AI model, Kiyasseh et al.4 found that it could reliably assess surgical performance but exhibited bias.”
From Bias in AI-based models for medical applications: challenges and mitigation strategies (2023) · cites this paper as [4]
Kiyasseh et al. developed a strategy called TWIX to mitigate bias.
“With their SAIS model, Kiyasseh et al.4 developed a strategy called TWIX to mitigate bias.”
From Bias in AI-based models for medical applications: challenges and mitigation strategies (2023) · cites this paper as [4]
TWIX is an add-on application that taught the SAIS model to add a prediction of the importance of video clips used to assess surgical skill.
“TWIX is an add-on application that taught the SAIS model to add a prediction of the importance of video clips that was used to assess surgical skill.”
From Bias in AI-based models for medical applications: challenges and mitigation strategies (2023) · cites this paper as [4]
TWIX mitigated SAIS model bias, improving model performance both for the disadvantaged surgeon sub-cohorts and for surgical skill assessments overall.
“Kiyasseh et al.4 found that TWIX mitigated SAIS model bias, improving model performance both for the disadvantaged surgeon sub-cohorts and for surgical skill assessments overall.”
From Bias in AI-based models for medical applications: challenges and mitigation strategies (2023) · cites this paper as [4]
Kiyasseh et al. deployed surgical AI systems (SAIS) on videos of robotic surgeries from three hospitals.
“Kiyasseh et al.4 highlight this potential application in their work deploying surgical AI systems (SAIS) on videos of robotic surgeries from three hospitals.”
From Bias in AI-based models for medical applications: challenges and mitigation strategies (2023) · cites this paper as [4]
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Artificial Intelligence in stock broking: A systematic review of strategies and outcomes
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Large-scale self-supervised video foundation model for intelligent surgery
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Smart Operating Room in Digestive Surgery: A Narrative Review
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Representation of Demographics in Otolaryngology by Artificial Intelligence Text‐to‐Image Platforms
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Quality over quantity? The role of data quality and uncertainty for AI in surgery
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The future implementation of artificial intelligence technology in esophageal surgery
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Expanding the Study of Bias in Medical Education Assessment
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Artificial Intelligence in Skill Development
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Ethical Ramifications and Remedial Approaches on Bias in Artificial Intelligence
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- Authors
- Kiyasseh, D., Laca, J., Haque, T. F., Otiato, M., Miles, B. J., Wagner, C., Donoho, D. A., Trinh, Q.-D., Anandkumar, A., & Hung, A. J.
- Affiliations
- California Institute of Technology (US)
- University of Southern California (US)
- Houston Methodist (US)
- St.-Antonius-Hospital Gronau (DE)
- Children's National (US)
- Brigham and Women's Hospital (US)
- Corresponding author
- Kiyasseh, Dani
- Hung, Andrew J.
- ORCID
- Venue
- npj Digital Medicine
- Published
- 2023-03-30
- Volume
- 6
- Issue
- 1
- Pages
- 54-54
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC10063676
- OpenAlex
- W4362506590
- Cited by
- 53
- References
- 54
- Citations by year
- 2026: 15 · 2025: 11 · 2024: 17 · 2023: 10
- FWCI
- 13.22
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- U.S. Department of Health & Human Services | NIH | National Cancer Institute (5R01CA251579-02)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Health Sciences › Medicine › Surgery › Surgical Simulation and Training
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- algorithmic bias, surgical skill assessment, visual explanations, surgeon credentialing, robotic surgery, intraoperative video analysis, algorithmic fairness
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