A new class of artificial intelligence models is prompting healthcare leaders to reconsider the long-standing assumptions behind post-surgical cancer surveillance. The recent deployment of the RADAR CARE system by Samsung Medical Center offers a case in point. Trained on more than 14,000 early-stage non-small cell lung cancer (NSCLC) cases, the transformer-based model provides individualized predictions of recurrence risk within the first year after surgery.

Early evidence from a study presented at the American Heart Association’s 2025 Hypertension Scientific Sessions suggests that AI voice agents may hold untapped potential in addressing persistent care gaps for older adults with chronic conditions. The trial, involving 2,000 Medicare-aged patients, found that AI-powered phone agents not only improved the accuracy of home blood pressure readings, but also elevated quality ratings, lowered care management costs, and improved clinical workflows.

Despite record levels of experimentation and investment, most health systems remain underprepared to operationalize artificial intelligence at scale. New data from a joint report by the Healthcare Financial Management Association (HFMA) and Eliciting Insights shows that while 88% of organizations are using AI in some form, only 18% have achieved maturity which is a status defined by the presence of both a late-stage governance framework and a defined enterprise AI strategy.

Cleveland Clinic has launched a system-wide rollout of Dyania Health’s Synapsis AI platform following a series of successful pilot programs in cardiology, oncology, and neurology. The deployment signals a growing commitment to integrating medically trained large language models (LLMs) into clinical research workflows to accelerate trial recruitment and improve patient access to experimental therapies.

The rapid adoption of artificial intelligence (“AI”) tools across the healthcare industry has vast potential to save both providers and payers time and money, simultaneously improving patient encounters and outcomes. Yet rapid adoption necessarily requires additional duties: namely, to use AI tools responsibly (AI governance), and to communicate to all parties, especially patients, how they’re being used (AI transparency).

As generative AI becomes more integrated into daily life, a growing number of patients are turning to it for healthcare decision-making, particularly in choosing providers. A new survey from rater8 reveals that nearly one-third of patients now use AI tools to research doctors, and more than one-quarter report that AI directly influenced their provider selection.

Ben Scharfe’s interview offers a grounded view of AI’s role in healthcare today. Instead of treating AI as a monolithic solution, he frames it as a set of targeted, specialty-aware tools designed to enhance, not replace, clinician performance.

Artificial intelligence is moving rapidly into healthcare workflows, but as explored in last week’s HIT Leaders & News editorial AI in Healthcare Is Moving Fast but Trust Is Moving Slowly, technology readiness does not guarantee successful adoption.