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The American Journal of Bioethics Volume 22, 2022 - Issue 5
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A Research Ethics Framework for the Clinical Translation of Healthcare Machine Learning

Melissa D McCraddena Department of Bioethics, The Hospital for Sick Children;b Genetics and Genome Biology, The Hospital for Sick Children, Peter Gilgan Centre for Research and Learning;c Division of Clinical & Public Health, Dalla Lana School of Public HealthCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-6476-2165View further author information
ORCID Icon,
James A Andersona Department of Bioethics, The Hospital for Sick Children;d Institute for Health Management Policy, & Evaluation, University of TorontoView further author information
,
Elizabeth A. Stephensone Labatt Family Heart Centre, The Hospital for Sick Children;f Department of Pediatrics, The Hospital for Sick ChildrenView further author information
,
Erik Drysdaleb Genetics and Genome Biology, The Hospital for Sick Children, Peter Gilgan Centre for Research and LearningView further author information
,
Lauren Erdmanb Genetics and Genome Biology, The Hospital for Sick Children, Peter Gilgan Centre for Research and Learning;g Vector Institute;h Department of Computer Science, University of TorontoView further author information
,
Anna Goldenberga Department of Bioethics, The Hospital for Sick Children;g Vector Institute;h Department of Computer Science, University of Toronto;i CIFARView further author information
&
Randi Zlotnik Shaula Department of Bioethics, The Hospital for Sick Children;f Department of Pediatrics, The Hospital for Sick Children;j Child Health Evaluative Sciences, The Hospital for Sick ChildrenView further author information
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Pages 8-22 | Published online: 20 Jan 2022

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Read on this site (10)

James A. Anderson, Melissa D. McCradden & Elizabeth A. Stephenson. (2022) Response to Open Peer Commentaries: On Social Harms, Big Tech, and Institutional Accountability. The American Journal of Bioethics 22:10, pages W6-W8.
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Kayte Spector-Bagdady, Vasiliki Rahimzadeh, Kaitlyn Jaffe & Jonathan Moreno. (2022) Promoting Ethical Deployment of Artificial Intelligence and Machine Learning in Healthcare. The American Journal of Bioethics 22:5, pages 4-7.
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Jennifer Blumenthal-Barby, Benjamin Lang, Natalie Dorfman, Holland Kaplan, William B. Hooper & Kristin Kostick-Quenet. (2022) Research on the Clinical Translation of Health Care Machine Learning: Ethicists Experiences on Lessons Learned. The American Journal of Bioethics 22:5, pages 1-3.
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Margaret Levi, Michael Bernstein & Quinn Waeiss. (2022) Broadening the Ethical Scope. The American Journal of Bioethics 22:5, pages 26-28.
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Kadija Ferryman. (2022) Rethinking the AI Chasm. The American Journal of Bioethics 22:5, pages 29-30.
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Effy Vayena & Alessandro Blasimme. (2022) A Systemic Approach to the Oversight of Machine Learning Clinical Translation. The American Journal of Bioethics 22:5, pages 23-25.
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Nicole Martinez-Martin & Mildred K. Cho. (2022) Bridging the AI Chasm: Can EBM Address Representation and Fairness in Clinical Machine Learning?. The American Journal of Bioethics 22:5, pages 30-32.
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Calvin Wai-Loon Ho & Rohit Malpani. (2022) Scaling up the Research Ethics Framework for Healthcare Machine Learning as Global Health Ethics and Governance. The American Journal of Bioethics 22:5, pages 36-38.
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Tijs Vandemeulebroucke, Yvonne Denier & Chris Gastmans. (2022) The Need for a Global Approach to the Ethical Evaluation of Healthcare Machine Learning. The American Journal of Bioethics 22:5, pages 33-35.
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James Shaw. (2022) Emerging Paradigms for Ethical Review of Research Using Artificial Intelligence. The American Journal of Bioethics 22:5, pages 42-44.
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Articles from other publishers (14)

Melissa McCradden, Katrina Hui & Daniel Z Buchman. (2023) Evidence, ethics and the promise of artificial intelligence in psychiatry. Journal of Medical Ethics 49:8, pages 573-579.
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Shrikant Mali, Sachinkumar Dahivelkar & G.L. Pradeep. (2023) Artificial intelligence in head neck cancer full of potential BUT filled with landmines. Oral Oncology Reports 6, pages 100035.
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Ben Glocker, Charles Jones, Mélanie Bernhardt & Stefan Winzeck. (2023) Algorithmic encoding of protected characteristics in chest X-ray disease detection models. eBioMedicine 89, pages 104467.
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Mark Sendak, David Vidal, Sylvia Trujillo, Karandeep Singh, Xiaoxuan Liu & Suresh Balu. (2023) Editorial: Surfacing best practices for AI software development and integration in healthcare. Frontiers in Digital Health 5.
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Aditya Gupta & Amritpal Singh. (2023) EDL‐NSGA‐II : Ensemble deep learning framework with NSGA‐II feature selection for heart disease prediction . Expert Systems.
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Florian Funer. 2023. Medizin – Technik – Ethik. Medizin – Technik – Ethik 59 85 .
Mara Ulloa, Blaine Rothrock, Faraz S. Ahmad & Maia Jacobs. (2022) Invisible clinical labor driving the successful integration of AI in healthcare. Frontiers in Computer Science 4.
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Naresh Kumar Trivedi, Raj Gaurang Tiwari, Deden Witarsyah, Vinay Gautam, Alok Misra & Ryan Adhitya Nugraha. (2022) Machine Learning Based Evaluations of Stress, Depression, and Anxiety. Machine Learning Based Evaluations of Stress, Depression, and Anxiety.
Meredith Cox, J. C. Panagides, Azadeh Tabari, Sanjeeva Kalva, Jayashree Kalpathy-Cramer & Dania Daye. (2022) Risk stratification with explainable machine learning for 30-day procedure-related mortality and 30-day unplanned readmission in patients with peripheral arterial disease. PLOS ONE 17:11, pages e0277507.
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Paul Istasy, Wen Shen Lee, Alla Iansavichene, Ross Upshur, Bishal Gyawali, Jacquelyn Burkell, Bekim Sadikovic, Alejandro Lazo-Langner & Benjamin Chin-Yee. (2022) The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review. Journal of Medical Internet Research 24:11, pages e39748.
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Jia You, Ya-Ru Zhang, Hui-Fu Wang, Ming Yang, Jian-Feng Feng, Jin-Tai Yu & Wei Cheng. (2022) Development of a novel dementia risk prediction model in the general population: A large, longitudinal, population-based machine-learning study. eClinicalMedicine 53, pages 101665.
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Suranna R. Monah, Matthias W. Wagner, Asthik Biswas, Farzad Khalvati, Lauren E. Erdman, Afsaneh Amirabadi, Logi Vidarsson, Melissa D. McCradden & Birgit B. Ertl-Wagner. (2022) Data governance functions to support responsible data stewardship in pediatric radiology research studies using artificial intelligence. Pediatric Radiology 52:11, pages 2111-2119.
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Jethro C. C. Kwong, Lauren Erdman, Adree Khondker, Marta Skreta, Anna Goldenberg, Melissa D. McCradden, Armando J. Lorenzo & Mandy Rickard. (2022) The silent trial - the bridge between bench-to-bedside clinical AI applications. Frontiers in Digital Health 4.
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Savitesh Kushwaha, Rachana Srivastava, Harsh Vats & Poonam Khanna. 2022. Machine Learning for Societal Improvement, Modernization, and Progress. Machine Learning for Societal Improvement, Modernization, and Progress 50 70 .

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