Artificial Intelligence in Radiology: A Private Practice Perspective From a Large Health System in Latin America

Seminars in Roentgenology - Tập 58 - Trang 203-207 - 2023
Paulo E.A. Kuriki1, Felipe C. Kitamura1
1DasaInova, Dasa, Av. das Nações Unidas, São Paulo SP, Brazil

Tài liệu tham khảo

Bai, 2020, AI augmentation of radiologist performance in distinguishing COVID-19 from pneumonia of other etiology on chest CT, Radiology, 296, E163

van Leeuwen, 2022, How does artificial intelligence in radiology improve efficiency and health outcomes?, Pediatr Radiol, 52, 2087, 10.1007/s00247-021-05114-8

Kottler, 2020, Artificial intelligence: a private practice perspective, J Am Coll Radiol, 17, 1398, 10.1016/j.jacr.2020.09.029

Chaudhari, 2022, Application of a domain-specific BERT for detection of speech recognition errors in radiology reports, Radiol Artif Intell, 4, 10.1148/ryai.210185

Casey, 2021, A systematic review of natural language processing applied to radiology reports, BMC Med Inform Decis Mak, 21, 179, 10.1186/s12911-021-01533-7

Tiu, 2022, Expert-level detection of pathologies from unannotated chest X-ray images via self-supervised learning, Nat Biomed Eng, 6, 1399, 10.1038/s41551-022-00936-9

Omoumi, 2021, To buy or not to buy—evaluating commercial AI solutions in radiology (the ECLAIR guidelines), Eur Radiol, 31, 3786, 10.1007/s00330-020-07684-x

Available at: https://www.ibm.com/downloads/cas/GVAGA3JP.

Larsen, 2019, Why do 87% of data science projects never make it into production?, VentureBeat, 1

Gichoya, 2022, AI recognition of patient race in medical imaging: a modelling study, Lancet Digit Health, 4, e406, 10.1016/S2589-7500(22)00063-2

Mhasawade, 2021, Machine learning and algorithmic fairness in public and population health, Nat Mach Intell, 3, 659

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Seminars in Roentgenology

Tập 58

203-207

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