Protecting Human Judgment in AI-Enabled Healthcare: A Policy Framework for Responsible Artificial Intelligence Use

Abstract: Artificial intelligence (AI) is increasingly integrated into healthcare decision-making, offering significant opportunities to improve diagnostic support, operational efficiency, risk prediction, access, and quality of care. Existing AI governance frameworks appropriately emphasize safety, effectiveness, transparency, equity, privacy, accountability, and human oversight. However, comparatively less policy attention has been directed toward whether healthcare professionals retain the cognitive, professional, and institutional capacity to exercise meaningful independent judgment as AI assumes greater influence over consequential decisions. This white paper identifies this emerging governance gap and proposes the Human Judgment Protection Standard (HJPS), a risk-based framework for protecting meaningful human judgment in AI-enabled healthcare. The HJPS comprises six interrelated domains: Human Decision Authority, Cognitive Independence, AI Competency and Critical Evaluation, Meaningful Human Oversight, Accountability and Traceability, and Continuous Human-Judgment Monitoring. A four-level risk classification model is proposed to align the intensity of human-judgment safeguards with the potential consequences and degree of AI influence associated with specific healthcare use cases. The framework further introduces a Human-Judgment Impact Assessment (HJIA) and a continuous organizational implementation cycle incorporating AI inventory, risk classification, workflow design, competency development, controlled deployment, dual-domain monitoring, audit, corrective action, and reassessment. The paper argues that maintaining a human formally “in the loop” is insufficient when automation bias, cognitive offloading, algorithmic authority, professional deskilling, workload, or organizational conditions undermine the individual's practical capacity to evaluate or challenge AI-generated recommendations. The HJPS therefore advances a transition from human-in-the-loop to human-capacity-in-the-loop governance. Policy recommendations emphasize risk-proportionate oversight, professional AI competency, meaningful override authority, shared accountability, organizational monitoring, accreditation integration, and continued empirical evaluation. The framework offers a human-centered approach for ensuring that advances in healthcare AI strengthen rather than inadvertently diminish the human capabilities necessary for safe, ethical, accountable, and resilient healthcare. Keywords: artificial intelligence, healthcare AI, human judgment, human-centered AI, Human Judgment Protection Standard, meaningful human oversight, cognitive offloading, automation bias, AI governance, healthcare policy. Title: Protecting Human Judgment in AI-Enabled Healthcare: A Policy Framework for Responsible Artificial Intelligence Use Author: David Bull International Journal of Healthcare Sciences ISSN 2348-5728 (Online) Vol. 14, Issue 1, April 2026 - September 2026 Page No: 549-570 Research Publish Journals Website: www.researchpublish.com Published Date: 25-August-2026 DOI: https://doi.org/10.5281/zenodo.22092819 Paper Download Link (Source) https://www.researchpublish.com/papers/protecting-human-judgment-in-ai-enabled-healthcare-a-policy-framework-for-responsible-artificial-intelligence-use