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  1. Are clinicians ethically obligated to disclose their use of medical machine learning systems to patients?Joshua Hatherley - forthcoming - Journal of Medical Ethics.
    It is commonly accepted that clinicians are ethically obligated to disclose their use of medical machine learning systems to patients, and that failure to do so would amount to a moral fault for which clinicians ought to be held accountable. Call this ‘the disclosure thesis.’ Four main arguments have been, or could be, given to support the disclosure thesis in the ethics literature: the risk-based argument, the rights-based argument, the materiality argument and the autonomy argument. In this article, I argue (...)
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  • Should the use of adaptive machine learning systems in medicine be classified as research?Robert Sparrow, Joshua Hatherley, Justin Oakley & Chris Bain - 2024 - American Journal of Bioethics 24 (10):58-69.
    A novel advantage of the use of machine learning (ML) systems in medicine is their potential to continue learning from new data after implementation in clinical practice. To date, considerations of the ethical questions raised by the design and use of adaptive machine learning systems in medicine have, for the most part, been confined to discussion of the so-called “update problem,” which concerns how regulators should approach systems whose performance and parameters continue to change even after they have received regulatory (...)
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  • Evidence, ethics and the promise of artificial intelligence in psychiatry.Melissa McCradden, Katrina Hui & Daniel Z. Buchman - 2023 - Journal of Medical Ethics 49 (8):573-579.
    Researchers are studying how artificial intelligence (AI) can be used to better detect, prognosticate and subgroup diseases. The idea that AI might advance medicine’s understanding of biological categories of psychiatric disorders, as well as provide better treatments, is appealing given the historical challenges with prediction, diagnosis and treatment in psychiatry. Given the power of AI to analyse vast amounts of information, some clinicians may feel obligated to align their clinical judgements with the outputs of the AI system. However, a potential (...)
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  • Bridging the AI Chasm: Can EBM Address Representation and Fairness in Clinical Machine Learning?Nicole Martinez-Martin & Mildred K. Cho - 2022 - American Journal of Bioethics 22 (5):30-32.
    McCradden et al. propose to close the “AI chasm” between algorithms and clinically meaningful application using the norms of evidence-based medicine and clinical research, with the rat...
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  • Non-empirical methods for ethics research on digital technologies in medicine, health care and public health: a systematic journal review.Frank Ursin, Regina Müller, Florian Funer, Wenke Liedtke, David Renz, Svenja Wiertz & Robert Ranisch - 2024 - Medicine, Health Care and Philosophy 27 (4):513-528.
    Bioethics has developed approaches to address ethical issues in health care, similar to how technology ethics provides guidelines for ethical research on artificial intelligence, big data, and robotic applications. As these digital technologies are increasingly used in medicine, health care and public health, thus, it is plausible that the approaches of technology ethics have influenced bioethical research. Similar to the “empirical turn” in bioethics, which led to intense debates about appropriate moral theories, ethical frameworks and meta-ethics due to the increased (...)
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  • Emerging Paradigms for Ethical Review of Research Using Artificial Intelligence.James Shaw - 2022 - American Journal of Bioethics 22 (5):42-44.
    The ethical review of research using methods of artificial intelligence and machine learning in health care contexts has become an important challenge for Research Ethics Boards (also refer...
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  • A Systemic Approach to the Oversight of Machine Learning Clinical Translation.Effy Vayena & Alessandro Blasimme - 2022 - American Journal of Bioethics 22 (5):23-25.
    Machine learning heralds highly transformative approaches to the automation of numerous clinical tasks, from diagnosis to risk assessment, and from prognosis to informing treatment decisions....
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  • Adaptive Machine Learning as Research: Does the Cure Fit the Disease?Matthew DeCamp & David Kao - 2024 - American Journal of Bioethics 24 (10):70-72.
    Volume 24, Issue 10, October 2024, Page 70-72.
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  • Broadening the Ethical Scope.Quinn Waeiss, Michael Bernstein & Margaret Levi - 2022 - American Journal of Bioethics 22 (5):26-28.
    McCradden and colleagues' (2022) argues that machine learning in health care poses new challenges to appropriate evaluation for safe use in clinical care. It also claims that “the longstanding syst...
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  • The Need for a Global Approach to the Ethical Evaluation of Healthcare Machine Learning.Tijs Vandemeulebroucke, Yvonne Denier & Chris Gastmans - 2022 - American Journal of Bioethics 22 (5):33-35.
    In their article “A Research Ethics Framework for the Clinical Translation of Healthcare Machine Learning,” McCradden et al. highlight the various gaps that emerge when artificial intelligen...
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  • Promoting Ethical Deployment of Artificial Intelligence and Machine Learning in Healthcare.Kayte Spector-Bagdady, Vasiliki Rahimzadeh, Kaitlyn Jaffe & Jonathan Moreno - 2022 - American Journal of Bioethics 22 (5):4-7.
    The ethics of artificial intelligence and machine learning exemplify the conceptual struggle between applying familiar pathways of ethical analysis versus generating novel strategies. Mel...
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  • Broadening the Ethical Scope.Margaret Levi, Michael Bernstein & Charla Waeiss - 2022 - American Journal of Bioethics 22 (5):26-28.
    McCradden and colleagues' argues that machine learning in health care poses new challenges to appropriate evaluation for safe use in clinical care. It also claims that “the longstanding syst...
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  • Prudently Evaluating Medical Adaptive Machine Learning Systems.Andreas Kuersten - 2024 - American Journal of Bioethics 24 (10):76-79.
    Volume 24, Issue 10, October 2024, Page 76-79.
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  • Scaling up the Research Ethics Framework for Healthcare Machine Learning as Global Health Ethics and Governance.Calvin Wai-Loon Ho & Rohit Malpani - 2022 - American Journal of Bioethics 22 (5):36-38.
    The research ethics framework put forward by McCradden et al. to support systematic inquiry in the development of artificial intelligence and machine learning technologies in healt...
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  • Multi-Level Ethical Considerations of Artificial Intelligence Health Monitoring for People Living with Parkinson’s Disease.Anita Ho, Itai Bavli, Ravneet Mahal & Martin J. McKeown - 2024 - AJOB Empirical Bioethics 15 (3):178-191.
    Artificial intelligence (AI) has garnered tremendous attention in health care, and many hope that AI can enhance our health system’s ability to care for people with chronic and degenerative conditions, including Parkinson’s Disease (PD). This paper reports the themes and lessons derived from a qualitative study with people living with PD, family caregivers, and health care providers regarding the ethical dimensions of using AI to monitor, assess, and predict PD symptoms and progression. Thematic analysis identified ethical concerns at four intersecting (...)
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  • Research on the Clinical Translation of Health Care Machine Learning: Ethicists Experiences on Lessons Learned.Jennifer Blumenthal-Barby, Benjamin Lang, Natalie Dorfman, Holland Kaplan, William B. Hooper & Kristin Kostick-Quenet - 2022 - American Journal of Bioethics 22 (5):1-3.
    The application of machine learning in health care holds great promise for improving care. Indeed, our own team is collaborating with experts in machine learning and statistical modeling to bu...
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  • Rethinking the AI Chasm.Kadija Ferryman - 2022 - American Journal of Bioethics 22 (5):29-30.
    McCradden et al.’s article makes a distinctive contribution to the growing literature on the ethics of artificial intelligence in medicine. Not only do the authors raise important ethical is...
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  • Scoping Review Shows the Dynamics and Complexities Inherent to the Notion of “Responsibility” in Artificial Intelligence within the Healthcare Context.Sarah Bouhouita-Guermech & Hazar Haidar - 2024 - Asian Bioethics Review 16 (3):315-344.
    The increasing integration of artificial intelligence (AI) in healthcare presents a host of ethical, legal, social, and political challenges involving various stakeholders. These challenges prompt various studies proposing frameworks and guidelines to tackle these issues, emphasizing distinct phases of AI development, deployment, and oversight. As a result, the notion of responsible AI has become widespread, incorporating ethical principles such as transparency, fairness, responsibility, and privacy. This paper explores the existing literature on AI use in healthcare to examine how it addresses, (...)
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  • Discerning the Nature of MAMLS: Research, Quality Improvement, or Both?Charles Binkley, Rohan Meda & Joel de Lara - 2024 - American Journal of Bioethics 24 (10):98-100.
    Volume 24, Issue 10, October 2024, Page 98-100.
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  • The Fourth Industrial Revolution: Its Impact on Artificial Intelligence and Medicine in Developing Countries.Thalia Arawi, Joseph El Bachour & Tala El Khansa - 2024 - Asian Bioethics Review 16 (3):513-526.
    Artificial intelligence (AI) is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. Artificial intelligence can be both a blessing and a curse, and potentially a double-edged sword if not carefully wielded. While it holds massive potential benefits to humans—particularly in healthcare by assisting in treatment of diseases, surgeries, record keeping, and easing the lives of both patients and doctors, its misuse has potential for harm through impact of biases, unemployment, breaches of (...)
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  • Response to Open Peer Commentaries: On Social Harms, Big Tech, and Institutional Accountability.James A. Anderson, Melissa D. McCradden & Elizabeth A. Stephenson - 2022 - American Journal of Bioethics 22 (10):6-8.
    The authors offer their sincere thanks to all of the commentators for taking the time to comment on our work ; one of the advantages of the AJOB format is immediate feedback,...
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