Charles E. Binkley, MD, FACS
Surgeon • Bioethicist • AI Expert
About Dr. Binkley
As a surgeon, bioethicist, and academic health system leader, Dr. Charles E. Binkley drives strategic initiatives to responsibly integrate AI into care delivery systems, enhance clinical decision-making, improve patient outcomes, and deliver measurable impact. He is chair of the AI Task Force at Hackensack Meridian School of Medicine (HMSOM), where he leads academic policy implementation, curricular integration, faculty development, and research involving AI.
He brings a rare combination of clinical, ethical, and practical operational expertise, combined with dynamic relational leadership, to the evaluation, integration, and governance of AI. His work bridges practice and theory, and he advises on local and national AI implementation strategies. His scholarship, including the book Encoding Bioethics: AI in Clinical Decision Making, describes how AI can support trustworthy, clinically grounded, and strategically sustainable AI in health systems.
Selected Publications
Books
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Encoding Bioethics: AI in Clinical Decision-Making
Peer-Reviewed Articles
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Ethical Rationing of Personal Protective Equipment to Minimize Moral Residue During the COVID-19 Pandemic.
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Ethical Centralization of High-risk Surgery Requires Racial and Economic Justice.
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Does Intraoperative Artificial Intelligence Decision Support Pose Ethical Issues?
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Who, If Not the FDA, Should Regulate Implantable Brain-Computer Interface Devices?
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How Should Surgeons Communicate About Palliative and Curative Intentions, Purposes, and Outcomes?
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The Complex Relationship between Disability Discrimination and Frailty Scores.
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Disproof of Concept: Resolving Ethical Dilemmas Using Algorithms.
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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients.
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Should We Rely on AI to Help Avoid Bias in Patient Selection for Major Surgery?
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From the Eyeball Test to the Algorithm - Quality of Life, Disability Status, and Clinical Decision Making in Surgery.
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The Actionless Agent: An Account of Human-CAI Relationships.
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Informed Consent for Clinician-AI Collaboration and Patient Data Sharing: Substantive, Illusory, or Both.
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Respecting the Value-Laden Nature of Participant Preferences: AI, Digital Phenotyping, and Psychiatry.
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A Holistic, Multi-Level, and Integrative Ethical Approach to Developing Machine Learning-Driven Decision Aids.
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Discerning the Nature of MAMLS: Research, Quality Improvement, or Both?
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Health AI poses distinct harms and potential benefits for disabled people.
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Clarifying When Consent Might Be Illusory in Notice and Explanation Rights.
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Predicting the Future: Informational Agency and the Right to Notice and Explanation in the Use of Personal Information.
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Governance of Direct-to-User Digital Mental Health Tools: Emphasizing Transparency over Paternalism.
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An early pipeline framework for assessing vendor AI solutions to support return on investment.
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A Matter of Trust: Recovering Lost Principles to Ethically Assess AI in Health Care.