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  1. Artificial intelligence for good health: a scoping review of the ethics literature.Jennifer Gibson, Vincci Lui, Nakul Malhotra, Jia Ce Cai, Neha Malhotra, Donald J. Willison, Ross Upshur, Erica Di Ruggiero & Kathleen Murphy - 2021 - BMC Medical Ethics 22 (1):1-17.
    BackgroundArtificial intelligence has been described as the “fourth industrial revolution” with transformative and global implications, including in healthcare, public health, and global health. AI approaches hold promise for improving health systems worldwide, as well as individual and population health outcomes. While AI may have potential for advancing health equity within and between countries, we must consider the ethical implications of its deployment in order to mitigate its potential harms, particularly for the most vulnerable. This scoping review addresses the following question: (...)
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  • Epistemic Rights and Responsibilities of Digital Simulacra for Biomedicine.Mildred K. Cho & Nicole Martinez-Martin - 2022 - American Journal of Bioethics 23 (9):43-54.
    Big data and artificial intelligence (“AI”) promise to transform virtually all aspects of biomedical research and health care (Matheny et al. 2019), through facilitation of drug development, diagno...
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  • A Research Ethics Framework for the Clinical Translation of Healthcare Machine Learning.Melissa D. McCradden, James A. Anderson, Elizabeth A. Stephenson, Erik Drysdale, Lauren Erdman, Anna Goldenberg & Randi Zlotnik Shaul - 2022 - American Journal of Bioethics 22 (5):8-22.
    The application of artificial intelligence and machine learning technologies in healthcare have immense potential to improve the care of patients. While there are some emerging practices surro...
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  • Accountability in the Machine Learning Pipeline: The Critical Role of Research Ethics Oversight.Melissa D. McCradden, James A. Anderson & Randi Zlotnik Shaul - 2020 - American Journal of Bioethics 20 (11):40-42.
    Char and colleagues provide a useful conceptual framework for the proactive identification of ethical issues arising throughout the lifecycle of machine learning applications in healthcare. Th...
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  • Inclusion of Clinicians in the Development and Evaluation of Clinical Artificial Intelligence Tools: A Systematic Literature Review.Stephanie Tulk Jesso, Aisling Kelliher, Harsh Sanghavi, Thomas Martin & Sarah Henrickson Parker - 2022 - Frontiers in Psychology 13.
    The application of machine learning and artificial intelligence in healthcare domains has received much attention in recent years, yet significant questions remain about how these new tools integrate into frontline user workflow, and how their design will impact implementation. Lack of acceptance among clinicians is a major barrier to the translation of healthcare innovations into clinical practice. In this systematic review, we examine when and how clinicians are consulted about their needs and desires for clinical AI tools. Forty-five articles met (...)
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  • How Can Law and Policy Advance Quality in Genomic Analysis and Interpretation for Clinical Care?Barbara J. Evans, Gail Javitt, Ralph Hall, Megan Robertson, Pilar Ossorio, Susan M. Wolf, Thomas Morgan & Ellen Wright Clayton - 2020 - Journal of Law, Medicine and Ethics 48 (1):44-68.
    Delivering high quality genomics-informed care to patients requires accurate test results whose clinical implications are understood. While other actors, including state agencies, professional organizations, and clinicians, are involved, this article focuses on the extent to which the federal agencies that play the most prominent roles — the Centers for Medicare and Medicaid Services enforcing CLIA and the FDA — effectively ensure that these elements are met and concludes by suggesting possible ways to improve their oversight of genomic testing.
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