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Artificial intelligence in musculoskeletal imaging: a perspective on value propositions, clinical use, and obstacles

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Abstract

Artificial intelligence and deep learning (DL) offer musculoskeletal radiology exciting possibilities in multiple areas, including image reconstruction and transformation, tissue segmentation, workflow support, and disease detection. Novel DL-based image reconstruction algorithms correcting aliasing artifacts, signal loss, and noise amplification with previously unobtainable effectiveness are prime examples of how DL algorithms deliver promised value propositions in musculoskeletal radiology. The speed of DL-based tissue segmentation promises great efficiency gains that may permit the inclusion of tissue compositional-based information routinely into radiology reports. Similarly, DL algorithms give rise to a myriad of opportunities for workflow improvements, including intelligent and adaptive hanging protocols, speech recognition, report generation, scheduling, precertification, and billing. The value propositions of disease-detecting DL algorithms include reduced error rates and increased productivity. However, more studies using authentic clinical workflow settings are necessary to fully understand the value of DL algorithms for disease detection in clinical practice. Successful workflow integration and management of multiple algorithms are critical for translating the value propositions of DL algorithms into clinical practice but represent a major roadblock for which solutions are critically needed. While there is no consensus about the most sustainable business model, radiology departments will need to carefully weigh the benefits and disadvantages of each commercially available DL algorithm. Although more studies are needed to understand the value and impact of DL algorithms on clinical practice, DL technology will likely play an important role in the future of musculoskeletal imaging.

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Authors and Affiliations

  1. New York University Grossman School of Medicine, New York University, New York, NY, 10016, USA

    Jan Fritz, Richard Kijowski & Michael P. Recht

  2. Department of Radiology, Musculoskeletal Radiology, New York University Grossman School of Medicine, 660 1st Ave, 3rd Floor, Rm #313, New York, NY, 10016, USA

    Jan Fritz

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Correspondence to Jan Fritz.

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Conflict of interest

Jan Fritz received institutional research support from Siemens AG, BTG International, Zimmer Biomed, DePuy Synthes, QED, and SyntheticMR; is a scientific advisor for Siemens AG, SyntheticMR, GE Healthcare, QED, BTG, ImageBiopsy Lab, Boston Scientific, and Mirata Pharma; and has shared patents with Siemens Healthcare and Johns Hopkins University. Richard Kijowski: none. Michael Recht: none.

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Fritz, J., Kijowski, R. & Recht, M.P. Artificial intelligence in musculoskeletal imaging: a perspective on value propositions, clinical use, and obstacles. Skeletal Radiol 51, 239–243 (2022). https://doi.org/10.1007/s00256-021-03802-y

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