ArticleOrthopadie (Heidelberg, Germany)2026
[Artificial intelligence in musculoskeletal radiology : Current advances and emerging frontiers].
Article in Orthopadie (Heidelberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThe integration of artificial intelligence (AI) into musculoskeletal radiology has the potential to revolutionize diagnostic precision and efficiency. At the beginning of the radiological diagnostic workflow, AI algorithms lead to a significant reduction in examination time, for example in image acquisition in MRI. DIAGNOSTICS: Certain AI algorithms achieve a diagnostic accuracy in fracture detection that is comparable to that of board-certified radiologists and perform time-consuming diagnostic analyses such as joint and axis measurements fully automatically. Generative AI based on large language models (LLM) can contribute to automation in structured reporting, with first clinical applications already available. CHALLENGES: While AI algorithms for accelerating MRI acquisition already contribute significantly to efficiency, challenges in the clinical translation of AI algorithms for automated reading support lie primarily in the low number of commercially available AI algorithms that offer a significant clinical value to quality and efficiency in the diagnostic workflow. Accepted key performance indices (KPI) for quantitatively measuring the return on investment (ROI) for the high running costs are also largely unresolved internationally.
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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 Socratic graph.