TY - JOUR
T1 - What should radiology residency and fellowship training in artificial intelligence include? A Trainee's perspective-radiology in training
AU - Tejani, Ali S.
AU - Fielding, Julia R.
AU - Peshock, Ronald M.
N1 - Publisher Copyright:
© 2021 Radiological Society of North America Inc.. All rights reserved.
PY - 2021
Y1 - 2021
N2 - Artificial intelligence (AI) and machine learning (ML) have captured the imagination of researchers and clinicians alike. Of note, AI is a broad designation encompassing any technique that enhances the ability of machines to mimic human behavior, while ML represents a subset of AI techniques that enable models to improve performance with increasing exposure to data (1). AI has drawn the attention of trainees who prepare to join academic and private practices already implementing AI tools. Although programs have started to introduce technical ML concepts locally (ML theory, data curation, model development, computational methods), a standardized, holistic AI education beyond algorithm-focused lectures is lacking (2,3). Specifically, there is a need for a curriculum that introduces all trainees to factors crucial for clinical integration of AI tools; this will prepare tool deployers and users in addition to tool creators. The absence of a standardized curriculum leaves trainees to navigate AI technology without structure. Acknowledging commentary highlighting the need for an AI curriculum (4), we propose a framework that addresses the basics of ML, AI tool application to common clinical questions, and the regulatory, ethical, and economic implications on clinical practice. This curriculum would be offered in tandem with, or following, residency training either through dedicated rotations or a supplemental scholarly track. Regular content updates from local or national committees will ensure trainees are educated on clinically relevant paradigms and standards in this rapidly advancing arena.
AB - Artificial intelligence (AI) and machine learning (ML) have captured the imagination of researchers and clinicians alike. Of note, AI is a broad designation encompassing any technique that enhances the ability of machines to mimic human behavior, while ML represents a subset of AI techniques that enable models to improve performance with increasing exposure to data (1). AI has drawn the attention of trainees who prepare to join academic and private practices already implementing AI tools. Although programs have started to introduce technical ML concepts locally (ML theory, data curation, model development, computational methods), a standardized, holistic AI education beyond algorithm-focused lectures is lacking (2,3). Specifically, there is a need for a curriculum that introduces all trainees to factors crucial for clinical integration of AI tools; this will prepare tool deployers and users in addition to tool creators. The absence of a standardized curriculum leaves trainees to navigate AI technology without structure. Acknowledging commentary highlighting the need for an AI curriculum (4), we propose a framework that addresses the basics of ML, AI tool application to common clinical questions, and the regulatory, ethical, and economic implications on clinical practice. This curriculum would be offered in tandem with, or following, residency training either through dedicated rotations or a supplemental scholarly track. Regular content updates from local or national committees will ensure trainees are educated on clinically relevant paradigms and standards in this rapidly advancing arena.
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U2 - 10.1148/RADIOL.2021204406
DO - 10.1148/RADIOL.2021204406
M3 - Review article
C2 - 33687289
AN - SCOPUS:85105579650
SN - 0033-8419
VL - 299
SP - E243-E245
JO - RADIOLOGY
JF - RADIOLOGY
IS - 2
ER -