Generative AI offers transformative potential in healthcare. This paper provides a rigorous framework for the responsible adoption of this technology. It highlights critical use cases, including advancements in medical imaging, personalized treatment protocols, and enhanced clinical decision-making, demonstrating its capacity to elevate patient outcomes and operational efficiency.

By applying principles of implementation science, the authors outline actionable strategies for embedding generative AI into healthcare systems, ensuring alignment with existing infrastructure and addressing the diverse needs of stakeholders, including clinicians and patients. It is necessary to address substantial governance challenges. The paper emphasizes the imperatives of transparency, bias mitigation, and robust data privacy within AI frameworks to build stakeholder trust and uphold equity. It advocates for a governance model that strikes a balance between fostering innovation and adhering to ethical and regulatory standards. 

This translational framework not only delineates a strategic pathway for deploying generative AI but also reinforces the importance of aligning technological progress with the foundational principles of safety, equity, and accountability that underpin the healthcare sector.

The main aspects highlighted by the authors are the following:

• Applications: generative AI can drive advancements in medical imaging, personalized medicine, drug discovery, and clinical decision support, enhancing diagnostic accuracy and operational efficiency.
• Integration: the paper outlines strategies for embedding AI tools within healthcare workflows. These include interoperability with existing systems, stakeholder training, and aligning outputs with clinical and patient needs.
• Governance: to mitigate risks such as bias, data privacy breaches, and ethical concerns, the authors propose comprehensive governance frameworks. These prioritize transparency, accountability, and regulatory compliance.
• Implementation science approach: leveraging this approach, the paper emphasizes context-sensitive strategies for scaling AI technologies, assessing outcomes, and fostering innovation while ensuring safety and equity.

The study serves as a roadmap for stakeholders - including policymakers, healthcare professionals, and tech developers - looking to responsibly integrate generative AI into healthcare systems.