AI Enters the Primary Care Practice # Primary care represents perhaps the most consequential frontier for artificial intelligence in medicine. As the first point of contact for most patients, primary care physicians face the challenge of distinguishing serious conditions from benign presentations across every organ system, managing complex chronic diseases, and coordinating care across specialists, all while seeing 20-30+ patients per day. AI promises to enhance diagnostic accuracy, improve chronic disease management, and catch the “needle in a haystack” diagnoses that might otherwise be missed. But with this promise comes significant liability questions: When an AI clinical decision support system fails to suggest a diagnosis that a prudent physician should have considered, who is responsible?
AI Transforms Nursing Practice # Nurses stand at the intersection of patient care and technology, making them both primary users and critical evaluators of healthcare AI. From early warning systems that predict patient deterioration to AI-powered documentation tools and medication verification systems, artificial intelligence is reshaping nursing practice across all settings. But with 4.7 million registered nurses in the United States making countless clinical decisions daily, the stakes of AI in nursing are enormous.
AI in the Emergency Department: Time-Critical Decisions # Emergency medicine is where AI meets life-or-death decisions in real time. From sepsis prediction algorithms to triage decision support, AI promises to help emergency physicians identify critically ill patients faster and allocate resources more effectively. In April 2024, the FDA authorized the first AI diagnostic tool for sepsis, a condition that kills over 350,000 Americans annually.
The High Stakes of Diagnostic AI # When artificial intelligence gets a diagnosis wrong, the consequences can be catastrophic. Missed cancers, delayed stroke treatment, sepsis alerts that fail to fire, diagnostic AI failures are increasingly documented, yet lawsuits directly challenging these systems remain rare. This tracker compiles the evidence: validated failures, performance gaps, bias documentation, FDA recalls, and the emerging litigation that will shape AI medical liability for decades.