AI developers are shifting from specialized imaging to opportunistic screening, extracting systemic disease markers from routine, low-cost clinical data like basic ECGs, retinal photos, and wearable pulse signals.

As clinical AI transitions to active decision-making, a wave of validation failures and self-overestimation is forcing healthcare buyers to inherit the entire burden of real-world model safety.

Clinical AI models are increasingly being outperformed by, or failing to improve upon, traditional statistical methods and established clinical formulas in critical predictive tasks.