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Titel:

How AI May Transform Musculoskeletal Imaging.

Dokumenttyp:
Journal Article; Review
Autor(en):
Guermazi, Ali; Omoumi, Patrick; Tordjman, Mickael; Fritz, Jan; Kijowski, Richard; Regnard, Nor-Eddine; Carrino, John; Kahn, Charles E; Knoll, Florian; Rueckert, Daniel; Roemer, Frank W; Hayashi, Daichi
Abstract:
While musculoskeletal imaging volumes are increasing, there is a relative shortage of subspecialized musculoskeletal radiologists to interpret the studies. Will artificial intelligence (AI) be the solution? For AI to be the solution, the wide implementation of AI-supported data acquisition methods in clinical practice requires establishing trusted and reliable results. This implementation will demand close collaboration between core AI researchers and clinical radiologists. Upon successful clini...     »
Zeitschriftentitel:
Radiology
Jahr:
2024
Band / Volume:
310
Heft / Issue:
1
Volltext / DOI:
doi:10.1148/radiol.230764
PubMed:
http://view.ncbi.nlm.nih.gov/pubmed/38165245
Print-ISSN:
0033-8419
TUM Einrichtung:
Institut für KI und Informatik in der Medizin (Prof. Rückert)
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