ArticleNPJ digital medicine2023
Human visual explanations mitigate bias in AI-based assessment of surgeon skills.
Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 citations in OpenAlex.
- Evaluation of objective tools and artificial intelligence in robotic surgery technical skills assessment: a systematic review.The British journal of surgery · 2024Pooled it
- Real-Time multifaceted artificial intelligence vs In-Person instruction in teaching surgical technical skills: a randomized controlled trial.Scientific reports · 2024Trial
- Current state of artificial intelligence applications in assisted reproductive technology: A narrative review.Clinical and experimental reproductive medicine · 2026Article
- Explainable machine learning for automated robotic surgical skill assessment using rich kinematic features.Journal of robotic surgery · 2026Article
- Privacy-Preserving Surgical Video Analysis with Swarm Learning - Results from a Multinational Appendectomy Cohort.NEJM AI · 2026Article
- Artificial intelligence in urology training Enhancing annotation, feedback, and evaluation in robotic, laparoscopic, and endoscopic surgery.Canadian Urological Association journal = Journal de l'Association des urologues du Canada · 2026Review
- Large-scale self-supervised video foundation model for intelligent surgery.NPJ digital medicine · 2026Article
- Towards accurate and interpretable competency-based assessment: enhancing clinical competency assessment through multimodal AI and anomaly detection.NPJ digital medicine · 2026Article
- Decentralized, privacy-preserving surgical video analysis with Swarm Learning.medRxiv : the preprint server for health sciences · 2025Article
- Article
- A scoping review and evidence gap analysis of clinical AI fairness.NPJ digital medicine · 2025Article
- Representation of Demographics in Otolaryngology by Artificial Intelligence Text-to-Image Platforms.Laryngoscope investigative otolaryngology · 2025Article
- AI-driven transformation of precision medicine: a comprehensive narrative review of key application areas, emerging paradigms, and future directions.Frontiers in public health · 2025Review
- Artificial intelligence in plastic surgery, where do we stand?JPRAS open · 2024Review
- Smart Operating Room in Digestive Surgery: A Narrative Review.Healthcare (Basel, Switzerland) · 2024Review
- A Beginner's Guide to Artificial Intelligence for Ophthalmologists.Ophthalmology and therapy · 2024Review
- Artificial intelligence in surgery.Nature medicine · 2024Review
- Clinical applications of artificial intelligence in robotic surgery.Journal of robotic surgery · 2024Review
- Competency in Robotic Surgery: Standard Setting for Robotic Suturing Using Objective Assessment and Expert Evaluation.Journal of surgical education · 2024Article
- Expanding the Study of Bias in Medical Education Assessment.Journal of graduate medical education · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 5 institutions in 2 countries.
Funding
Abstract
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.