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Comment
. 2020 Jul 29;2(4):e200150.
doi: 10.1148/ryai.2020200150. eCollection 2020 Jul.

Taking Matters into Your Own Hands

Safwan S Halabi  1
Affiliations
Comment

Taking Matters into Your Own Hands

Safwan S Halabi. Radiol Artif Intell. .
No abstract available

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Conflict of interest statement

Disclosures of Conflicts of Interest: S.H. disclosed no relevant relationships.

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Safwan S. Halabi, MD, is a clinical associate professor of radiology at the Stanford University School of Medicine and serves as the medical director for radiology informatics at Stanford Children’s Health. Dr Halabi’s clinical and administrative leadership roles are directed at improving quality of care, efficiency, and patient safety. His current academic and research interests include imaging informatics, deep/machine learning in imaging, artificial intelligence in medicine, clinical decision support, and patient-centric health care delivery.
Safwan S. Halabi, MD, is a clinical associate professor of radiology at the Stanford University School of Medicine and serves as the medical director for radiology informatics at Stanford Children’s Health. Dr Halabi’s clinical and administrative leadership roles are directed at improving quality of care, efficiency, and patient safety. His current academic and research interests include imaging informatics, deep/machine learning in imaging, artificial intelligence in medicine, clinical decision support, and patient-centric health care delivery.
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References

    1. Lee H, Tajmir S, Lee J, et al. Fully automated deep learning system for bone age assessment. J Digit Imaging 2017;30(4):427–441. - PMC - PubMed
    1. Larson DB, Chen MC, Lungren MP, Halabi SS, Stence NV, Langlotz CP. Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs. Radiology 2018;287(1):313–322. - PubMed
    1. Halabi SS, Prevedello LM, Kalpathy-Cramer J, et al. The RSNA pediatric bone age machine learning challenge. Radiology 2019;290(2):498–503. - PMC - PubMed
    1. Pan I, Baird G, Mutasa S, et al. Rethinking Greulich and Pyle: A deep learning approach to pediatric bone age assessment. Radiol Artif Intell 2020;2(4):e190198. - PMC - PubMed
    1. Greulich W, Pyle S. Radiographic Atlas of Skeletal Development of the Hand and Wrist. Stanford, Calif: Stanford University Press, 1999.

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