An Examination of Emerging Bioethical Issues in Biomedical Research Proceedings of a Workshop (2020) / Chapter Skim
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1 Introduction
1 Introduction
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Statements, recommendations, and opinions expressed are those of individual presenters and participants and are not endorsed or verified by the National Academies of Sciences, Engineering, and Medicine, and they should not be construed as reflecting any group consensus. 2 Biomedical research" refers to research that is broad in scope and can span disciplines of " biology, medicine, behavioral, and social sciences.
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hosted a 1-day public workshop5 in Washington, DC, to examine current and emerging bioethical issues that might arise in the context of biomedical research and to consider research topics in bioethics that could benefit from further attention. The scope of bioethical issues in research is broad, and for this workshop the independent planning committee chose to focus on issues related to the development and use of digital technologies, artificial intelligence, and machine learning in research and clinical 4 he T term "citizen science" does not currently have a widely accepted definition, but has been referred to as "the general public engagement in scientific research activities when citizens actively contribute to science either with their intellectual effort or surrounding knowledge or with their tools and resources" (EC, 2014)
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portfolio that is administered by the National Human Genome Research Institute (NHGRI) ; the ethics of biomedical research, supported by several institutes of NIH; and bioethics capacity building outside the United States, through the funding portfolio administered by the Fogarty International Center.
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The workshop will describe the state of the emerging science and potential pressing, recur ring, emerging, and/or anticipated future bioethical issues in biomedical research and society that fall within the scope of the research and policy activities of the N ational Institutes of Health. Potential topics may include • se of digital technologies, artificial intelligence, and machine learning in U biomedical research and clinical care; • merging ethical challenges for sharing data from human research par E ticipants and use of human biospecimens; • ealth equity and health disparities in research, including H o ecognizing and addressing barriers to participation in research and R clinical care across diverse populations and groups, o nderstanding the impact of cultural and social context on health and U disease, and o Equitable distribution of the benefits and burdens of research; • nnovative study designs, including crowdsourcing of research and citizen I science; • ovel approaches for enhancing bioethics infrastructure and training; N • ew means for assessing and enhancing scientific workforce diversity; N and/or • nnovative solutions for enhancing research oversight infrastructure.
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issued a statement in mid-March 2020 indicating that it would be exercising enforcement discretion about the Health Insurance Portability and Accountability Act of 1996 rules regarding remote communications technologies used for telehealth during the pandemic.10 Given this rapidly evolving landscape, there may be additional bioethical issues related to digital technologies, structural racism and health disparities, privacy, and other topics that were not expressly covered during the workshop and that warrant further discussion as well as additional funding opportunities. For example, at the time of publication, 7 ee Ethical Issues in Translational Science Research (R01 Clinical Trial Optional)
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Ethical Norms Several times during the workshop speakers acknowledged that data scientists and digital technology developers currently operate under a very different set of cultural norms, ethical commitments, and incentive structures than those of biomedical research and health care practice. Some speakers said that a better understanding of ethical issues is needed by digital technology developers and data scientists but that those efforts will also need to be supplemented by guidelines, regulations, system architecture, collaboration among various stakeholders, and improved incentive structures.
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It was mentioned that scientific conferences are devoting sessions or entire meetings to ethical issues and that colleges and universities are implementing ethics classes for data scientists. Speakers also shared their thoughts on the need for training clinicians and clinical laboratory professionals in the proper and unbiased use of algorithms.
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The first two panel sessions focused on the ethical issues associated with the use of digital health technologies, artificial intelligence, and machine learning in biomedical research and clinical care (Chapter 2)
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. The workshop concluded with observations and reflections shared by panelists from a variety of agencies that fund bioethics research and from audience participants (Chapter 6)
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