Healing Without Certainty: The Ethical Significance of Uncertainty in Israeli Medicine in the Age of Artificial...

The growing integration of artificial intelligence into healthcare is frequently associated with efforts to reduce uncertainty through prediction, data analysis, and computational decision support. Recent work on generative and foundation models has intensified this aspiration while also documenting new uncertainties involving model error, variable performance, opacity, bias, and the conditions of meaningful human oversight. This conceptual and normative article argues that uncertainty in medicine is a multidimensional feature of practice rather than a single informational deficit that technology can progressively eliminate. Drawing on philosophy of medicine, medical ethics, sociology of medicine, and recent AI ethics literature, the article distinguishes epistemic, ontological, moral, communicative, and systemic uncertainty. Predictive technologies may reduce selected epistemic uncertainties, but they can also redistribute uncertainty to questions of model validity, workflow integration, and responsibility. Other uncertainties arise from value conflict, human variability, communication, and the application of population-level evidence to particular patients. The analysis examines predictive analytics, clinical decision support, and generative AI, and argues that predictive performance does not by itself settle clinical or ethical judgment. Israel is used as a bounded illustrative context, not as an empirical case study or as representative of all Israeli practice. Its digital infrastructure, current AI governance initiatives, and experience of prolonged emergency conditions make visible both the utility and the limits of prediction. The article proposes five principles for responsible integration: complementarity, proportionality, accountability, fairness, and human-centred care. Its claim is not that uncertainty is intrinsically valuable or should be preserved when it can safely be reduced. Rather, ethically responsible practice requires epistemic humility, honest communication, contestability, and deliberation where uncertainty persists. The framework is offered as a philosophical proposal requiring empirical testing with clinicians, patients, and institutions.