Building Trustworthy AI in Geriatric Medicine

The Trust Deficit in Healthcare AI

Artificial intelligence holds enormous promise for geriatric medicine. From early detection of cognitive decline to personalised medication management, AI systems can process complexity that exceeds human cognitive capacity and identify patterns invisible to even experienced clinicians. Yet despite this potential, adoption remains cautious, and for good reason.

Trust is the currency of healthcare. Patients trust their physicians with their lives. Physicians trust their training, their colleagues, and their clinical judgement. Introducing an AI system into this deeply human relationship requires a level of trustworthiness that goes far beyond technical accuracy. It demands transparency, reliability, fairness, and accountability.

In geriatric medicine specifically, the stakes are amplified. Elderly patients often present with multiple comorbidities, polypharmacy challenges, atypical symptom presentations, and varying levels of cognitive capacity. An AI system that works well for a general adult population may fail dangerously when applied to this complex patient group. Building AI that geriatricians and their patients can genuinely trust requires deliberate, specialised effort.

The Pillars of Trustworthy AI in Geriatrics

Explainability: Showing the Work

The most technically sophisticated AI system is useless in clinical practice if it cannot explain its reasoning. When an AI tool flags a potential drug interaction or suggests adjusting a treatment plan, the geriatrician needs to understand why. A black-box recommendation, no matter how statistically accurate, undermines clinical autonomy and patient safety.

Explainable AI (XAI) in geriatric medicine means providing clear, clinically meaningful justifications for every recommendation. Rather than simply outputting a risk score, a trustworthy system explains which patient factors contributed to the assessment, how those factors were weighted, what evidence base supports the reasoning, and what the confidence level and limitations of the assessment are.

This explainability serves multiple purposes. It allows clinicians to validate AI recommendations against their own expertise. It enables patients and families to understand and participate in care decisions. And it creates an audit trail that supports accountability when outcomes are reviewed.