Trustworthiness of artificial intelligence from a patient perspective

Abstract

Discussions about the trustworthiness of artificial intelligence (AI) in healthcare are increasingly grounded in ethical frameworks rather than technical performance metrics. A key reference is Ethics and governance of artificial intelligence for health [World Health Organization (WHO)], 1 which formulates six ethical consensus principles for: protecting human autonomy; promoting well-being, safety, and the public interest; ensuring transparency, explainability, and intelligibility; fostering responsibility and accountability; ensuring inclusive-ness and equity; and promoting responsive and sustainable AI. These principles do not primarily describe how healthcare systems should be organized, but rather define normative design, implementation, and governance principles. From the patient's perspective, these principles resonate fundamentally with the inherent nature of medical practice: becoming a patient already means coming into a system structured around uncertainty, probability, and imperfect knowledge. Artificial intelligence can introduce and remove uncertainty in healthcare 2 paradoxically by entering an intrinsically uncertain domain while simultaneously supporting risk-benefit calculations with predictable modelling. This management of uncertainty is crucial because medical decisions are rarely absolute; diagnoses, prognoses, and treatment outcomes are inherently probabilistic. For patients, uncertainty is not a theoretical construct but a daily reality. Making this uncertainty explicit, for instance, through AI systems displaying confidence intervals, reflects a form of epistemic honesty. Such transparency can strengthen trust by aligning technological outputs with the patient's lived experience of medical ambiguity, thereby enhancing shared decision-making. The clinical necessity of addressing this ambiguity is underscored by the high stakes of medical error: model-based extra-polations from WHO data suggest medication errors may be associated with roughly 163 000 deaths annually in the EU, while digitally integrated medication-traceability systems show error reduction up to 58%. 3,4 Such figures illustrate that error and system failure are characteristics of complex healthcare environments. From the patient's viewpoint, trust tends to be placed in imperfect systems. Therefore, the ethical challenge is not about AI's flawlessness, but about a design that makes uncertainties visible, accountable, and manageable.