This guide provides a practical, real-world introduction to generative AI in healthcare, moving beyond hype to examine how these systems function within clinical environments. It explores where generative AI adds value today across documentation, decision support, patient communication, and medical education, while clearly outlining its limitations, risks, and boundaries. The guide introduces key evaluation parameters for assessing clinical usefulness, including task clarity, source transparency, editability, consistency, and workflow integration. It further details the importance of clinical validation, risk stratification, and human-in-the-loop oversight to ensure that AI systems remain safe, accountable, and aligned with real-world care delivery. Through stakeholder perspectives spanning clinicians, hospitals, patients, developers, researchers, and policymakers, the guide presents a systems-level view of how generative AI can be responsibly implemented and scaled. It highlights critical challenges such as hallucinations, bias, overdependence on automation, and the gap between experimental performance and clinical reality. Designed for clinicians, healthcare professionals, innovators, and decision-makers, this resource bridges the gap between technological capability and clinical applicability. Part of the HealthInnovation Toolbox Innovation Series, it supports the development and adoption of generative AI systems that enhance care while preserving safety, trust, and human judgment.
Sonu et al. (Wed,) studied this question.