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Uncertainty quantification (UQ) seeks to bridge this gap by providing each DL prediction with a calibrated estimate of uncertainty, thereby fostering clinician trust and safer deployment.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We conducted a targeted narrative review, performing expert\u2010driven searches in PubMed, Scopus, and arXiv and mining references from relevant publications in MSK imaging utilizing UQ, and a thematic synthesis was used to derive a cohesive taxonomy of UQ methodologies.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>UQ approaches encompass multi\u2010pass methods (e.g., test\u2010time augmentation, Monte Carlo dropout, and model ensembling) that infer uncertainty from variability across repeated inferences; single\u2010pass methods (e.g., conformal prediction, and evidential deep learning) that augment each individual prediction with uncertainty metrics; and other techniques that leverage auxiliary information, such as inter\u2010rater variability, hidden\u2010layer activations, or generative reconstruction errors, to estimate confidence. Applications in MSK imaging, include highlighting uncertain areas in cartilage segmentation and identifying uncertain predictions in joint implant design detections; downstream applications include enhanced clinical utility and more efficient data annotation pipelines.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Embedding UQ into DL workflows is essential for translating high\u2010performance models into clinical practice. Future research should prioritize robust out\u2010of\u2010distribution handling, computational efficiency, and standardized evaluation metrics to accelerate the adoption of trustworthy AI in MSK medicine.<\/jats:p><\/jats:sec><jats:sec><jats:title>Level of Evidence<\/jats:title><jats:p>Not applicable.<\/jats:p><\/jats:sec>","DOI":"10.1002\/ksa.12737","type":"journal-article","created":{"date-parts":[[2025,7,28]],"date-time":"2025-07-28T13:06:18Z","timestamp":1753707978000},"page":"3418-3437","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Towards trustworthy artificial intelligence in musculoskeletal medicine: A narrative review on uncertainty quantification"],"prefix":"10.1002","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6196-1329","authenticated-orcid":false,"given":"Amir M.","family":"Vahdani","sequence":"first","affiliation":[{"name":"Tehran University of Medical Sciences, School of Medicine Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3940-8359","authenticated-orcid":false,"given":"Moein","family":"Shariatnia","sequence":"additional","affiliation":[{"name":"Tehran University of Medical Sciences, School of Medicine Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8030-3727","authenticated-orcid":false,"given":"Pranav","family":"Rajpurkar","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics Harvard Medical School Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8683-1697","authenticated-orcid":false,"given":"Ayoosh","family":"Pareek","sequence":"additional","affiliation":[{"name":"Hospital for Special Surgery New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,7,28]]},"reference":[{"key":"e_1_2_15_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3163384"},{"key":"e_1_2_15_3_1","doi-asserted-by":"crossref","unstructured":"AbutalipK SaeedN SobirovI AndrearczykV DepeursingeA YaqubM. 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