ReviewJournal of experimental orthopaedics2024
A practical guide to the implementation of AI in orthopaedic research, Part 6: How to evaluate the performance of AI research?
Review in Journal of experimental orthopaedics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- Large language models show distinct multidimensional performance profiles and variable reproducibility in consensus-based bone stress injury.Journal of experimental orthopaedics · 2026Article
- Beyond Area Under the Receiver Operating Characteristic Curve: Evaluating Predictive Performance Metrics Under Class Imbalance in Real-World Clinical Data.JMIR formative research · 2026Article
- The application of large language models in orthopedic postgraduate education: potentials, challenges, and future prospects.Journal of orthopaedic surgery and research · 2026Review
- Standardised Transparent Orthopaedic Reporting and Modelling for AI (STORM-AI)-Guidelines for reporting artificial intelligence studies in orthopaedics from the ESSKA AI Working Group.Journal of experimental orthopaedics · 2026Article
- Is orthopaedics entering the age of generative AI?-A narrative review of current applications challenges and future directions.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2026Review
- A practical guide to the implementation of AI in orthopaedic research part 8: Resource management checklist for AI-driven research projects in orthopaedics.Journal of experimental orthopaedics · 2026Review
- From de-skilling to up-skilling: How artificial intelligence will augment the modern physician.Journal of experimental orthopaedics · 2026Review
- Current Concepts in Artificial Intelligence-Assisted Arthroplasty: A Review of the Perioperative Pathway.Cureus · 2025Review
- Artificial intelligence agents in orthopaedics: Concepts, capabilities and the road ahead.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2025Review
- A practical guide to the implementation of AI in orthopaedic research-Part 5: Data management.Journal of experimental orthopaedics · 2025Review
- A practical guide to the implementation of artificial intelligence in orthopaedic research-Part 3: How orthopaedic research benefits from the implementation of artificial intelligence.Journal of experimental orthopaedics · 2025Review
- Development of Explainable Machine Learning Models to Predict Outcomes After Platelet-Rich Plasma Injections for Knee Osteoarthritis.Orthopaedic journal of sports medicine · 2025Article
- 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
- Revolutionizing total hip arthroplasty: The role of artificial intelligence and machine learning.Journal of experimental orthopaedics · 2025Article
- Artificial intelligence and the diagnosis of oral cavity cancer and oral potentially malignant disorders from clinical photographs: a narrative review.Frontiers in oral health · 2025Review
- A practical guide to the implementation of AI in orthopaedic research, Part 6: How to evaluate the performance of AI research?Journal of experimental orthopaedics · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence's (AI) accelerating progress demands rigorous evaluation standards to ensure safe, effective integration into healthcare's high-stakes decisions. As AI increasingly enables prediction, analysis and judgement capabilities relevant to medicine, proper evaluation and interpretation are indispensable. Erroneous AI could endanger patients; thus, developing, validating and deploying medical AI demands adhering to strict, transparent standards centred on safety, ethics and responsible oversight. Core considerations include assessing performance on diverse real-world data, collaborating with domain experts, confirming model reliability and limitations, and advancing interpretability. Thoughtful selection of evaluation metrics suited to the clinical context along with testing on diverse data sets representing different populations improves generalisability. Partnering software engineers, data scientists and medical practitioners ground assessment in real needs. Journals must uphold reporting standards matching AI's societal impacts. With rigorous, holistic evaluation frameworks, AI can progress towards expanding healthcare access and quality. Level of Evidence: Level V.
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.