Editorial: Responsible AI in healthcare: opportunities, challenges, and best practices

Abstract

Responsible AI in healthcare: opportunities, challenges, and best practices As Artificial Intelligence (AI) makes its way into healthcare, it promises to revolutionize clinical decision-making processes.AI-powered Clinical Decision Support Systems (AI-CDSS) offer the potential to augment clinicians' decision-making abilities, improve diagnosis accuracy, and personalize treatment plans (Magrabi et al., 2019;Montani and Striani, 2019;Giordano et al., 2021).However, with this transformative potential come significant ethical challenges, such as issues of bias, transparency, accountability, and privacy (Keskinbora, 2019;Wang et al., 2021).These challenges have accelerated research on responsible AI, which seeks to ensure that AI systems are developed and deployed in a manner that is ethical, fair, transparent, accountable, and beneficial to all users (Dignum, 2019;Floridi et al., 2021;Floridi and Cowls, 2022).These ethical aspects gain heightened significance in high-stakes domains such as healthcare.This Research Topics features four articles that delve into different aspects of responsible AI in healthcare, including data biases, transparency in uncertainty communication, integration of AI into healthcare, and evaluation of AI-CDSS.In this editorial, we introduce these four articles and provide a brief overview of these critical areas, highlighting the necessity to address these issues to ensure responsible and effective use of AI in healthcare.

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