Healthcare AI often optimizes measurable objectives such as risk reduction, treatment response, readmission, or length of stay. These objectives are useful, but they do not capture every dimension of patient value.
Consider two patients with similar clinical profiles who have different preferences regarding treatment burden, independence, or quality of life. An algorithm that optimizes a single outcome may generate the same recommendation for both, even when their informed priorities differ.
Decision-support systems should distinguish between predicted outcomes and preference-sensitive decisions. They can present expected benefits, potential harms, uncertainty, and reasonable alternatives to support shared decision-making.
Agentic systems require additional safeguards. They should recognize actions that need patient consent, clinician authorization, or further discussion rather than proceeding solely because an action aligns with a predefined objective.
Patient preferences should not be inferred carelessly from incomplete records. Where preferences are unknown or may have changed, systems should identify the gap and facilitate appropriate human engagement.
The aim is not to encode every human value into an algorithm. It is to design AI that helps healthcare professionals make evidence-informed decisions without treating patients as interchangeable prediction targets.
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