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The article presents the discussion on commentators taking the time for commenting on the work. Topics include data sovereignty and collaboration in the research process respecting indigeous forms of knowledge and independence; and facilitating the development of research being sensitive to the harms and benefits of AI/ML research.

Explainable artificial intelligence (XAI) has emerged as a promising solution for addressing the implementation challenges of AI/ML in healthcare. However, little is known about how developers and clinicians interpret XAI and what conflicting goals and requirements they may have. This paper presents the findings of a longitudinal multi-method study involving 112 developers and clinicians co-designing an XAI solution for a clinical decision support system. Our study identifies three key differences between developer and clinician mental models of XAI, including opposing goals (model interpretability vs. clinical plausibility), different sources of truth (data vs. patient), and the role of exploring new vs. exploiting old knowledge. Based on our findings, we propose design solutions that can help address the XAI conundrum in healthcare, including the use of causal inference models, personalized explanations, and ambidexterity between exploration and exploitation mindsets. Our study highlights the importance of considering the perspectives of both developers and clinicians in the design of XAI systems and provides practical recommendations for improving the effectiveness and usability of XAI in healthcare. [ABSTRACT FROM AUTHOR]

The application of artificial intelligence and machine learning (ML) technologies in healthcare have immense potential to improve the care of patients. While there are some emerging practices surrounding responsible ML as well as regulatory frameworks, the traditional role of research ethics oversight has been relatively unexplored regarding its relevance for clinical ML. In this paper, we provide a comprehensive research ethics framework that can apply to the systematic inquiry of ML research across its development cycle. The pathway consists of three stages: (1) exploratory, hypothesis-generating data access; (2) silent period evaluation; (3) prospective clinical evaluation. We connect each stage to its literature and ethical justification and suggest adaptations to traditional paradigms to suit ML while maintaining ethical rigor and the protection of individuals. This pathway can accommodate a multitude of research designs from observational to controlled trials, and the stages can apply individually to a variety of ML applications. [ABSTRACT FROM AUTHOR]

The article presents the discussion on Machine learning (ML) having highly transformative approaches to the automation of numerous clinical tasks. Topics include computer scientists being most concerned with demonstrating the superiority of ML-driven systems at performing classificatory or predictive tasks; and focusing on ensuring the safety and effectiveness of medical devices, software, and drugs before receiving market authorization.

The article presents the discussion on predicting personalized risks for patients considering left-ventricular assist device (LVAD) therapy for heart failure. Topics include raising serious ethical concerns and serving as an unanticipated obstacle for exploratory research in health care ML; and implementing into clinical practice due to the advances in medical and mechanical therapies for heart failure.

The article presents the discussion on ethics of machine learning (ML) in clinical healthcare settings. Topics include guaranteeing an ethically robust design, development, and usage of technology; and incorporating strong encryption strategies in the design stage preventing possible ethical difficulties related to data privacy issues in the use stage.

The article presents the discussion on role of low and middle-income countries (LMICs ) strengthening through effective global governance. Topics include supporting systematic inquiry in the development of artificial intelligence (AI) and machine learning (ML) technologies in healthcare; and accomplishing healthcare goals and the targets under the Sustainable Development Goals (SDGs).

The article offers information on Framework to Evaluate Ethical Considerations with ML-HCA Applications— Valuable, Even Necessary, but Never Comprehensive. It mentions ethical evaluations will be improved if they are explicit about the mental map being used, and that the mental map we propose is useful in many regards.

The article presents the discussion on making a distinctive contribution to the growing literature on the ethics of artificial intelligence in medicine. Topics include benefits of the technology and the dangers of exacerbating existing injustices like health disparities; and ethics of ML in health contributing to scholarship on the ethics of translational research.