Less than four years into the large language model era, healthcare professionals are aware of AI’s growing role, though the pace of advances since late 2022 has been overwhelming. A brief overview from advanced IT supplier Databricks highlighted healthcare-specific AI applications and best practices. Electronic health records are the primary data source for most healthcare AI systems, containing structured fields like lab results and medication lists alongside unstructured clinical notes, which AI can analyze to predict disease risks, identify care gaps, and flag patients for earlier intervention. AI systems require high-quality data, making data collection standards foundational, specifying acceptable sources, minimum sample sizes, and documentation requirements. The FDA plans to monitor AI-equipped medical devices for continuous learning after initial clearance, departing from the traditional fixed-product approval model; a compliance checklist should confirm HIPAA safeguards, applicable AI Act risk classification, FDA clearance status, and documented human oversight. Healthcare AI adoption faces challenges like data privacy and algorithmic bias, with bias mitigation requiring auditing training data for representativeness, testing performance within subpopulations, and establishing retraining or retirement processes for models with performance gaps. AI is reshaping needed skills, creating interdisciplinary roles at the intersection of clinical medicine and data science, including clinical informaticists, health data governance specialists, and AI implementation leads.