Usage of Generative AI in Healthcare: An Implementation Science Informed Translational Path on Application, Integration and Governance

BackgroundArtificial intelligence (AI), particularly generative AI, has emerged as a transformative tool in healthcare, with the potential to revolutionize clinical decision-making and improve health outcomes.Generative AI, capable of generating new data such as text and images, holds promise in enhancing patient care, revolutionizing disease diagnosis and expanding treatment options.However, the utility and impact of generative AI in healthcare remain poorly understood, with concerns around ethical and medico-legal implications, integration into healthcare service delivery and workforce utilisation.Also, there is not a clear pathway to implement and integrate generative AI in healthcare delivery. -MethodsThis article aims to provide a comprehensive overview of the use of generative AI in healthcare, focusing on the utility of the technology in healthcare and its translational application highlighting the need for careful planning, execution and management of expectations in adopting generative AI in clinical medicine.Key considerations include factors such as data privacy, security and the irreplaceable role of clinicians' expertise.Frameworks like the technology acceptance model (TAM) and the Non-Adoption, Abandonment, Scale-up, Spread and Sustainability (NASSS) model are considered to promote responsible integration.These frameworks allow anticipating and proactively addressing barriers to adoption, facilitating stakeholder participation and responsibly transitioning care systems to harness generative AI's potential. -ResultsGenerative AI has the potential to transform healthcare through automated systems, enhanced clinical decision-making and democratization of -expertise with diagnostic support tools providing timely, personalized suggestions.Generative AI applications across billing, diagnosis, treatment and research can also make healthcare delivery more efficient, equitable and effective.However, integration of generative AI necessitates meticulous change management and risk mitigation