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Humanizing AI in medical training: ethical framework for responsible design

The increasing use of artificial intelligence (AI) in healthcare has brought about numerous ethical considerations that push for reflection. Humanizing AI in medical training is crucial to ensure that the design and deployment of its algorithms align with ethical principles and promote equitable hea...

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Autores principales: Tahri Sqalli, Mohammed, Aslonov, Begali, Gafurov, Mukhammadjon, Nurmatov, Shokhrukhbek
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10227566/
https://www.ncbi.nlm.nih.gov/pubmed/37261331
http://dx.doi.org/10.3389/frai.2023.1189914
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author Tahri Sqalli, Mohammed
Aslonov, Begali
Gafurov, Mukhammadjon
Nurmatov, Shokhrukhbek
author_facet Tahri Sqalli, Mohammed
Aslonov, Begali
Gafurov, Mukhammadjon
Nurmatov, Shokhrukhbek
author_sort Tahri Sqalli, Mohammed
collection PubMed
description The increasing use of artificial intelligence (AI) in healthcare has brought about numerous ethical considerations that push for reflection. Humanizing AI in medical training is crucial to ensure that the design and deployment of its algorithms align with ethical principles and promote equitable healthcare outcomes for both medical practitioners trainees and patients. This perspective article provides an ethical framework for responsibly designing AI systems in medical training, drawing on our own past research in the fields of electrocardiogram interpretation training and e-health wearable devices. The article proposes five pillars of responsible design: transparency, fairness and justice, safety and wellbeing, accountability, and collaboration. The transparency pillar highlights the crucial role of maintaining the explainabilty of AI algorithms, while the fairness and justice pillar emphasizes on addressing biases in healthcare data and designing models that prioritize equitable medical training outcomes. The safety and wellbeing pillar however, emphasizes on the need to prioritize patient safety and wellbeing in AI model design whether it is for training or simulation purposes, and the accountability pillar calls for establishing clear lines of responsibility and liability for AI-derived decisions. Finally, the collaboration pillar emphasizes interdisciplinary collaboration among stakeholders, including physicians, data scientists, patients, and educators. The proposed framework thus provides a practical guide for designing and deploying AI in medicine generally, and in medical training specifically in a responsible and ethical manner.
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spelling pubmed-102275662023-05-31 Humanizing AI in medical training: ethical framework for responsible design Tahri Sqalli, Mohammed Aslonov, Begali Gafurov, Mukhammadjon Nurmatov, Shokhrukhbek Front Artif Intell Artificial Intelligence The increasing use of artificial intelligence (AI) in healthcare has brought about numerous ethical considerations that push for reflection. Humanizing AI in medical training is crucial to ensure that the design and deployment of its algorithms align with ethical principles and promote equitable healthcare outcomes for both medical practitioners trainees and patients. This perspective article provides an ethical framework for responsibly designing AI systems in medical training, drawing on our own past research in the fields of electrocardiogram interpretation training and e-health wearable devices. The article proposes five pillars of responsible design: transparency, fairness and justice, safety and wellbeing, accountability, and collaboration. The transparency pillar highlights the crucial role of maintaining the explainabilty of AI algorithms, while the fairness and justice pillar emphasizes on addressing biases in healthcare data and designing models that prioritize equitable medical training outcomes. The safety and wellbeing pillar however, emphasizes on the need to prioritize patient safety and wellbeing in AI model design whether it is for training or simulation purposes, and the accountability pillar calls for establishing clear lines of responsibility and liability for AI-derived decisions. Finally, the collaboration pillar emphasizes interdisciplinary collaboration among stakeholders, including physicians, data scientists, patients, and educators. The proposed framework thus provides a practical guide for designing and deploying AI in medicine generally, and in medical training specifically in a responsible and ethical manner. Frontiers Media S.A. 2023-05-16 /pmc/articles/PMC10227566/ /pubmed/37261331 http://dx.doi.org/10.3389/frai.2023.1189914 Text en Copyright © 2023 Tahri Sqalli, Aslonov, Gafurov and Nurmatov. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Artificial Intelligence
Tahri Sqalli, Mohammed
Aslonov, Begali
Gafurov, Mukhammadjon
Nurmatov, Shokhrukhbek
Humanizing AI in medical training: ethical framework for responsible design
title Humanizing AI in medical training: ethical framework for responsible design
title_full Humanizing AI in medical training: ethical framework for responsible design
title_fullStr Humanizing AI in medical training: ethical framework for responsible design
title_full_unstemmed Humanizing AI in medical training: ethical framework for responsible design
title_short Humanizing AI in medical training: ethical framework for responsible design
title_sort humanizing ai in medical training: ethical framework for responsible design
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10227566/
https://www.ncbi.nlm.nih.gov/pubmed/37261331
http://dx.doi.org/10.3389/frai.2023.1189914
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