Care cannot become an algorithm.

AI may help clinicians summarize records, support decisions, and accelerate scientific research. The same systems may make confident mistakes, inherit unequal patterns from training data, or encourage excessive reliance on automated suggestions. In healthcare, an error can be especially costly.

Used with rigorous evaluation, AI could reduce paperwork and extend services in underserved areas. It may help translate medical information and aid discovery. The question is whether tools improve outcomes for the people most likely to be excluded.

Independent clinical evaluations across patient groups, data security, consent and explainability, adverse events, and whether people can ask for human review. Marketing demonstrations are not a substitute for clinical evidence.

Treat high-impact clinical use as high-stakes technology: independent pre-deployment assessment, continuing post-market monitoring, responsible data governance and accessible routes to appeal. Keep clinicians accountable for patient care.