Table 1.

Categories of generative artificial intelligence (AI) applications in health care.

Category	Example	Setting	User	Input data	Output data	Personalization level	Workflow integration	Validation needed	Impact	Risks	Human involvement
Medical diagnostics	AI-Rad Companion	Radiology	Radiologists	Medical images	Text findings	Individual	Postimaging	High	Improved diagnosis	Reliability and bias	High
Drug discovery	Insilico Medicine	Biotechnology	Research scientists	Target proteins and disease data	Novel molecular structures	Semipersonalized	Early-stage research	High	Faster discoveries	Safety and testing requirements	Moderate
Virtual health assistants	Sensely	Web clinics	Patients	Conversation	Conversation	Semipersonalized	Patient engagement	Moderate	Increased access	Privacy and misinformation	Moderate
Medical research	Anthropic	Laboratories and academia	Researchers	Research concepts and data sets	Hypotheses and questions	Semipersonalized	Idea generation	Low	Research insights	Misdirection	Moderate
Clinical decision support	Glass AI	Point of care	Physicians	Patient data	Treatment suggestions	Individual	Diagnosis and treatment	High	Improved outcomes	Overreliance and bias	High