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Glossary Definition
Generative AI in Healthcare
Quick Answer
Generative AI in healthcare refers to artificial intelligence systems — primarily large language models — that can produce new content such as clinical text, diagnostic assessments, treatment summaries, and patient communications based on medical knowledge and patient data.
Source: The Clinical AI Report, February 2026
Definition
Generative AI in healthcare encompasses the application of large language models and other generative AI technologies to clinical workflows. Unlike analytical AI (which classifies or predicts from existing data), generative AI creates new content: drafted clinical notes, synthesized literature reviews, patient-facing explanations, diagnostic reasoning, and treatment recommendations. The technology has seen rapid adoption in healthcare, with 66% of US physicians reporting use of health AI tools in 2024 — a 78% increase from 2023.
Key Applications in Healthcare
Generative AI is being applied across healthcare in several ways: (1) Clinical documentation — ambient AI scribes generate structured notes from patient conversations, (2) Clinical decision support — AI synthesizes evidence and generates diagnostic and treatment recommendations, (3) Patient communication — drafting after-visit summaries, referral letters, and pre-authorization requests, (4) Literature synthesis — summarizing relevant research for specific clinical questions, (5) Medical education — generating case-based learning scenarios and assessment questions. Each application leverages the model's ability to produce coherent, contextually appropriate medical text.
Generative AI vs Traditional Healthcare AI
Traditional healthcare AI focused on classification and prediction: analyzing medical images for abnormalities, predicting patient deterioration, or flagging drug interactions using rule-based systems. Generative AI differs by producing novel text, enabling natural language interaction between physicians and clinical knowledge. This shift enables physicians to ask questions in plain language rather than navigating structured interfaces, making clinical knowledge more accessible at the point of care. However, generative AI also introduces new risks — particularly hallucination — that classification-based AI does not face.
Regulatory and Safety Landscape
The FDA's regulatory framework for AI medical devices was designed for static, rule-based systems and is adapting to address generative AI's unique characteristics — particularly its potential to produce different outputs for the same input and its capacity for hallucination. As of 2026, most generative AI clinical tools operate under the FDA's clinical decision support exemption, which excludes certain CDS software from device regulation if it enables physicians to independently review the basis for recommendations. This is why citation transparency and evidence grounding are both clinical and regulatory priorities for generative AI in healthcare.
Written by The Clinical AI Report editorial team. Last updated February 15, 2026.