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Tempering Expectations on the Medical Artificial Intelligence Revolution: The Medical Trainee Viewpoint

The rapid development of artificial intelligence (AI) in medicine has resulted in an increased number of applications deployed in clinical trials. AI tools have been developed with goals of improving diagnostic accuracy, workflow efficiency through automation, and discovery of novel features in clin...

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Autores principales: Hu, Zoe, Hu, Ricky, Yau, Olivia, Teng, Minnie, Wang, Patrick, Hu, Grace, Singla, Rohit
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9425164/
https://www.ncbi.nlm.nih.gov/pubmed/35969464
http://dx.doi.org/10.2196/34304
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author Hu, Zoe
Hu, Ricky
Yau, Olivia
Teng, Minnie
Wang, Patrick
Hu, Grace
Singla, Rohit
author_facet Hu, Zoe
Hu, Ricky
Yau, Olivia
Teng, Minnie
Wang, Patrick
Hu, Grace
Singla, Rohit
author_sort Hu, Zoe
collection PubMed
description The rapid development of artificial intelligence (AI) in medicine has resulted in an increased number of applications deployed in clinical trials. AI tools have been developed with goals of improving diagnostic accuracy, workflow efficiency through automation, and discovery of novel features in clinical data. There is subsequent concern on the role of AI in replacing existing tasks traditionally entrusted to physicians. This has implications for medical trainees who may make decisions based on the perception of how disruptive AI may be to their future career. This commentary discusses current barriers to AI adoption to moderate concerns of the role of AI in the clinical setting, particularly as a standalone tool that replaces physicians. Technical limitations of AI include generalizability of performance and deficits in existing infrastructure to accommodate data, both of which are less obvious in pilot studies, where high performance is achieved in a controlled data processing environment. Economic limitations include rigorous regulatory requirements to deploy medical devices safely, particularly if AI is to replace human decision-making. Ethical guidelines are also required in the event of dysfunction to identify responsibility of the developer of the tool, health care authority, and patient. The consequences are apparent when identifying the scope of existing AI tools, most of which aim to be physician assisting rather than a physician replacement. The combination of the limitations will delay the onset of ubiquitous AI tools that perform standalone clinical tasks. The role of the physician likely remains paramount to clinical decision-making in the near future.
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spelling pubmed-94251642022-08-31 Tempering Expectations on the Medical Artificial Intelligence Revolution: The Medical Trainee Viewpoint Hu, Zoe Hu, Ricky Yau, Olivia Teng, Minnie Wang, Patrick Hu, Grace Singla, Rohit JMIR Med Inform Viewpoint The rapid development of artificial intelligence (AI) in medicine has resulted in an increased number of applications deployed in clinical trials. AI tools have been developed with goals of improving diagnostic accuracy, workflow efficiency through automation, and discovery of novel features in clinical data. There is subsequent concern on the role of AI in replacing existing tasks traditionally entrusted to physicians. This has implications for medical trainees who may make decisions based on the perception of how disruptive AI may be to their future career. This commentary discusses current barriers to AI adoption to moderate concerns of the role of AI in the clinical setting, particularly as a standalone tool that replaces physicians. Technical limitations of AI include generalizability of performance and deficits in existing infrastructure to accommodate data, both of which are less obvious in pilot studies, where high performance is achieved in a controlled data processing environment. Economic limitations include rigorous regulatory requirements to deploy medical devices safely, particularly if AI is to replace human decision-making. Ethical guidelines are also required in the event of dysfunction to identify responsibility of the developer of the tool, health care authority, and patient. The consequences are apparent when identifying the scope of existing AI tools, most of which aim to be physician assisting rather than a physician replacement. The combination of the limitations will delay the onset of ubiquitous AI tools that perform standalone clinical tasks. The role of the physician likely remains paramount to clinical decision-making in the near future. JMIR Publications 2022-08-15 /pmc/articles/PMC9425164/ /pubmed/35969464 http://dx.doi.org/10.2196/34304 Text en ©Zoe Hu, Ricky Hu, Olivia Yau, Minnie Teng, Patrick Wang, Grace Hu, Rohit Singla. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 15.08.2022. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on https://medinform.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Viewpoint
Hu, Zoe
Hu, Ricky
Yau, Olivia
Teng, Minnie
Wang, Patrick
Hu, Grace
Singla, Rohit
Tempering Expectations on the Medical Artificial Intelligence Revolution: The Medical Trainee Viewpoint
title Tempering Expectations on the Medical Artificial Intelligence Revolution: The Medical Trainee Viewpoint
title_full Tempering Expectations on the Medical Artificial Intelligence Revolution: The Medical Trainee Viewpoint
title_fullStr Tempering Expectations on the Medical Artificial Intelligence Revolution: The Medical Trainee Viewpoint
title_full_unstemmed Tempering Expectations on the Medical Artificial Intelligence Revolution: The Medical Trainee Viewpoint
title_short Tempering Expectations on the Medical Artificial Intelligence Revolution: The Medical Trainee Viewpoint
title_sort tempering expectations on the medical artificial intelligence revolution: the medical trainee viewpoint
topic Viewpoint
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9425164/
https://www.ncbi.nlm.nih.gov/pubmed/35969464
http://dx.doi.org/10.2196/34304
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