ArticleScientific reports2024
Fair AI-powered orthopedic image segmentation: addressing bias and promoting equitable healthcare.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Technology Review: The Hidden Influence of AI in Orthopaedic Surgery.Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews · 2026Review
- Trustworthy AI models to predict prolonged hospital length-of-stay in orthopedic surgery.npj health systems · 2026Article
- KneeXNet-2.5D: a clinically-oriented and explainable deep learning framework for MRI-based knee cartilage and meniscus segmentation.npj health systems · 2026Article
- Applications of 3D Printing and Artificial Intelligence in Healthcare Management: A Narrative Review.Bioengineering (Basel, Switzerland) · 2026Review
- An investigation of race bias in deep learning-based segmentation of prostate MRI images.Scientific reports · 2025Article
- Artificial intelligence agents in orthopaedics: Concepts, capabilities and the road ahead.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2025Review
- Uncertainty quantification and explainable AI in orthopaedic imaging: A timely call to action.Journal of clinical orthopaedics and trauma · 2025Article
- Artificial intelligence algorithms in orthopaedics: A narrative review of methods and clinical applications.Journal of experimental orthopaedics · 2025Review
- A practical guide to the implementation of AI in orthopaedic research-Part 5: Data management.Journal of experimental orthopaedics · 2025Review
- Artificial intelligence-assisted analysis of musculoskeletal imaging-A narrative review of the current state of machine learning models.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2025Review
- Integrating artificial intelligence into orthopedics: Opportunities, challenges, and future directions.Journal of hand and microsurgery · 2025Review
- Generative AI in orthopedics: an explainable deep few-shot image augmentation pipeline for plain knee radiographs and Kellgren-Lawrence grading.Journal of the American Medical Informatics Association : JAMIA · 2024Article
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
AI-powered segmentation of hip and knee bony anatomy has revolutionized orthopedics, transforming pre-operative planning and post-operative assessment. Despite the remarkable advancements in AI algorithms for medical imaging, the potential for biases inherent within these models remains largely unexplored. This study tackles these concerns by thoroughly re-examining AI-driven segmentation for hip and knee bony anatomy. While advanced imaging modalities like CT and MRI offer comprehensive views, plain radiographs (X-rays) predominate the standard initial clinical assessment due to their widespread availability, low cost, and rapid acquisition. Hence, we focused on plain radiographs to ensure the utilization of our contribution in diverse healthcare settings, including those with limited access to advanced imaging technologies. This work provides insights into the underlying causes of biases in AI-based knee and hip image segmentation through an extensive evaluation, presenting targeted mitigation strategies to alleviate biases related to sex, race, and age, using an automatic segmentation that is fair, impartial, and safe in the context of AI. Our contribution can enhance inclusivity, ethical practices, equity, and an unbiased healthcare environment with advanced clinical outcomes, aiding decision-making and osteoarthritis research. Furthermore, we have made all the codes and datasets publicly and freely accessible to promote open scientific research.
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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.