ReviewKnee surgery, sports traumatology, arthroscopy : official journal of the ESSKA2025
Beyond traditional orthopaedic data analysis: AI, multimodal models and continuous monitoring.
Review in Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA, 2025. 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
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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
15 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
- Predictive accuracy of a general-purpose artificial intelligence model for cut-out following proximal femoral nailing.Archives of orthopaedic and trauma surgery · 2026Article
- Artificial Intelligence-Guided Precision Orthobiologics in Musculoskeletal Conditions.Journal of orthopaedic case reports · 2026Review
- A practical guide to the implementation of AI in orthopaedic research-Part 4: Prerequisites for a successful orthopedics AI-driven project in terms of interdisciplinary collaboration, data management, ethical approval and technology.Journal of experimental orthopaedics · 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
- Review
- From de-skilling to up-skilling: How artificial intelligence will augment the modern physician.Journal of experimental orthopaedics · 2026Review
- 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
- Transforming endoscopic retrograde cholangiopancreatography: the role of artificial intelligence in pre-operative planning, intraoperative navigation, and post-operative risk prediction.Frontiers in medicine · 2026Review
- Multimodal AI fusion: integrating MRI with PET/CT, histopathology, and liquid biopsy for bone tumor diagnosis.Frontiers in oncology · 2026Review
- Review
- Review
- Artificial Intelligence in Trauma and Orthopaedic Surgery: A Comprehensive Review From Diagnosis to Rehabilitation.Cureus · 2025Review
- Beyond traditional orthopaedic data analysis: AI, multimodal models and continuous monitoring.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2025Review
- Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multimodal artificial intelligence (AI) has the potential to revolutionise healthcare by enabling the simultaneous processing and integration of various data types, including medical imaging, electronic health records, genomic information and real-time data. This review explores the current applications and future potential of multimodal AI across healthcare, with a particular focus on orthopaedic surgery. In presurgical planning, multimodal AI has demonstrated significant improvements in diagnostic accuracy and risk prediction, with studies reporting an Area under the receiving operator curve presenting good to excellent performance across various orthopaedic conditions. Intraoperative applications leverage advanced imaging and tracking technologies to enhance surgical precision, while postoperative care has been advanced through continuous patient monitoring and early detection of complications. Despite these advances, significant challenges remain in data integration, standardisation, and privacy protection. Technical solutions such as federated learning (allowing decentralisation of models) and edge computing (allowing data analysis to happen on site or closer to site instead of multipurpose datacenters) are being developed to address these concerns while maintaining compliance with regulatory frameworks. As this field continues to evolve, the integration of multimodal AI promises to advance personalised medicine, improve patient outcomes, and transform healthcare delivery through more comprehensive and nuanced analysis of patient data. Level of Evidence: Level V.
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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.