ReviewThe Journal of arthroplasty2023
An Overview of Machine Learning in Orthopedic Surgery: An Educational Paper.
Review in The Journal of arthroplasty, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
15 citing papers in PubMed, 28 citations in OpenAlex.
- Artificial Intelligence and its Current Role in Clinical Outcome Prediction, Musculoskeletal Imaging, and Economic and Ethical Considerations within Orthopedics and Sports Medicine.Current reviews in musculoskeletal medicine · 2026Review
- Who Is the Surgeon Now: Human Hands or Machine Minds? Artificial Intelligence in Orthopedics from Diagnosis to Follow-Up-A Structured Narrative Review.Journal of clinical medicine · 2026Review
- Evaluating the methodological suitability of partial dependence plots and Shapley additive explanations for population-level interpretation of machine learning models in total joint arthroplasty.Arthroplasty (London, England) · 2026Article
- Intelligent Attention-Driven Deep Learning for Hip Disease Diagnosis: Fusing Multimodal Imaging and Clinical Text for Enhanced Precision and Early Detection.Medicina (Kaunas, Lithuania) · 2026Article
- Self-supervised learning with BYOL for anterior cruciate ligament tear detection from knee MRI.MethodsX · 2025Article
- Artificial Intelligence Has Varied Diagnostic and Predictive Performance in Diagnosing Patellofemoral Osteoarthritis, Trochlear Dysplasia, and Patellofemoral Tracking Abnormalities: A Systematic Review.Arthroscopy, sports medicine, and rehabilitation · 2025Review
- From data to precision: The transformative role of AI and machine learning in modern orthopaedic practice.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
- The role of artificial intelligence in predicting injured structures based on clinical images of lacerations in the volar aspect of the hand and forearm.Journal of hand and microsurgery · 2025Article
- Interpretable machine learning for predicting optimal surgical timing in polytrauma patients with TBI and fractures to reduce postoperative infection risk.Scientific reports · 2025Article
- Article
- Artificial Intelligence for Clinically Meaningful Outcome Prediction in Orthopedic Research: Current Applications and Limitations.Current reviews in musculoskeletal medicine · 2024Review
- Predicting Functional Outcomes of Total Hip Arthroplasty Using Machine Learning: A Systematic Review.Journal of clinical medicine · 2024Review
- Arthrosis diagnosis and treatment recommendations in clinical practice: an exploratory investigation with the generative AI model GPT-4.Journal of orthopaedics and traumatology : official journal of the Italian Society of Orthopaedics and Traumatology · 2023Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
The growth of artificial intelligence combined with the collection and storage of large amounts of data in the electronic medical record collection has created an opportunity for orthopedic research and translation into the clinical environment. Machine learning (ML) is a type of artificial intelligence tool well suited for processing the large amount of available data. Specific areas of ML frequently used by orthopedic surgeons performing total joint arthroplasty include tabular data analysis (spreadsheets), medical imaging processing, and natural language processing (extracting concepts from text). Previous studies have discussed models able to identify fractures in radiographs, identify implant type in radiographs, and determine the stage of osteoarthritis based on walking analysis. Despite the growing popularity of ML, there are limitations including its reliance on "good" data, potential for overfitting, long life cycle for creation, and ability to only perform one narrow task. This educational article will further discuss a general overview of ML, discussing these challenges and including examples of successfully published models.
Indexed as
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.