Frontiers in Artificial Intelligence | |
Humanizing AI in medical training: ethical framework for responsible design | |
Artificial Intelligence | |
Mukhammadjon Gafurov1  Begali Aslonov2  Mohammed Tahri Sqalli3  Shokhrukhbek Nurmatov3  | |
[1] Department of Business Administration, Carnegie Mellon University in Qatar, Doha, Qatar;Department of Control and Computer Engineering, Politecnico di Torino, Turin, Italy;Department of Economics, School of Foreign Services, Georgetown University in Qatar, Doha, Qatar; | |
关键词: human-AI interaction; Human-Computer Interaction; digital health; XAI; artificial intelligence; | |
DOI : 10.3389/frai.2023.1189914 | |
received in 2023-03-21, accepted in 2023-04-24, 发布年份 2023 | |
来源: Frontiers | |
【 摘 要 】
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.
【 授权许可】
Unknown
Copyright © 2023 Tahri Sqalli, Aslonov, Gafurov and Nurmatov.
【 预 览 】
Files | Size | Format | View |
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RO202310104346023ZK.pdf | 339KB | download |