With 4.5 billion people lacking essential healthcare access and an 11 million health worker shortage projected by 2030, artificial intelligence demonstrates measurable impact across diagnostic accuracy, workflow efficiency, and patient triage—yet healthcare remains below average in AI adoption compared to other industries.

A critical analysis of 180,640 patient records reveals that 40% of published AI prediction models use diagnostic codes that aren't finalized until after discharge—achieving artificially inflated accuracy of 97.6% while predending events like "brain death" to predict mortality.

While AI ambient scribes reduce physician documentation burden, new JAMA Health Forum analysis reveals concerning potential for automated upcoding and increased healthcare spending—with uncertain impacts on quality, equity, and patient outcomes that demand rigorous evaluation.

Healthcare AI spending reached $32.3 billion in 2024, with 80% of hospitals now deploying AI for patient care and operational efficiency. Yet 83% of consumers view AI's error potential as a barrier, creating an urgent imperative for physician leadership in implementation.

AI-enabled clinical platforms now account for 1.59 million monthly visits—over one-third of traffic compared to traditional resources like UpToDate—yet remain unvalidated for clinical outcomes, raising urgent questions about patient safety and decision-making quality.

Harvard Medical School experts reveal AI's transformative impact on healthcare, with language models reducing research time from hours to seconds while improving diagnostic accuracy by 16 percentage points compared to physicians alone in recent studies.

Delphi-2M, a generative transformer AI model, accurately predicts rates of over 1,000 diseases up to 20 years in advance by analyzing patient medical histories, demonstrating comparable accuracy to existing single-disease models while providing comprehensive health trajectory forecasting.