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Requirements for Trustworthy Artificial Intelligence and its Application in Healthcare

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Objectives: Artificial intelligence (AI) technologies are developing very rapidly in the medical field, but have yet to be activelyused in actual clinical settings. Ensuring reliability is essential to disseminating technologies, necessitating a wide rangeof research and subsequent social consensus on requirements for trustworthy AI. Methods: This review divided the requirementsfor trustworthy medical AI into explainability, fairness, privacy protection, and robustness, investigated research trendsin the literature on AI in healthcare, and explored the criteria for trustworthy AI in the medical field. Results: Explainabilityprovides a basis for determining whether healthcare providers would refer to the output of an AI model, which requires thefurther development of explainable AI technology, evaluation methods, and user interfaces. For AI fairness, the primary taskis to identify evaluation metrics optimized for the medical field. As for privacy and robustness, further development of technologiesis needed, especially in defending training data or AI algorithms against adversarial attacks. Conclusions: In the future,detailed standards need to be established according to the issues that medical AI would solve or the clinical field wheremedical AI would be used. Furthermore, these criteria should be reflected in AI-related regulations, such as AI developmentguidelines and approval processes for medical devices.

Abstract

Objectives: Artificial intelligence (AI) technologies are developing very rapidly in the medical field, but have yet to be activelyused in actual clinical settings. Ensuring reliability is essential to disseminating technologies, necessitating a wide rangeof research and subsequent social consensus on requirements for trustworthy AI. Methods: This review divided the requirementsfor trustworthy medical AI into explainability, fairness, privacy protection, and robustness, investigated research trendsin the literature on AI in healthcare, and explored the criteria for trustworthy AI in the medical field. Results: Explainabilityprovides a basis for determining whether healthcare providers would refer to the output of an AI model, which requires thefurther development of explainable AI technology, evaluation methods, and user interfaces. For AI fairness, the primary taskis to identify evaluation metrics optimized for the medical field. As for privacy and robustness, further development of technologiesis needed, especially in defending training data or AI algorithms against adversarial attacks. Conclusions: In the future,detailed standards need to be established according to the issues that medical AI would solve or the clinical field wheremedical AI would be used. Furthermore, these criteria should be reflected in AI-related regulations, such as AI developmentguidelines and approval processes for medical devices.

참고문헌 (53)

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