Generative AI in healthcare: an implementation science informed translational path on application, integration and governance
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View abstract on PubMed
Summary
This summary is machine-generated.Generative artificial intelligence (AI) offers transformative potential in healthcare for improved decision-making and patient care. Responsible integration, guided by implementation science and addressing ethical concerns, is crucial for realizing its benefits in clinical medicine.
Area Of Science
- Healthcare Technology
- Artificial Intelligence in Medicine
- Clinical Decision Support
Background
- Generative AI presents a significant opportunity to revolutionize healthcare by enhancing patient care, diagnosis, and treatment options.
- However, the practical application and impact of generative AI in healthcare are not yet fully understood, with notable concerns regarding ethical, medico-legal, and integration challenges.
- A clear roadmap for implementing generative AI within healthcare delivery systems is currently lacking.
Approach
- This article provides a comprehensive overview of generative AI's utility and translational applications in healthcare.
- It emphasizes the need for careful planning, expectation management, and consideration of data privacy, security, and clinician expertise.
- Frameworks such as the Technology Acceptance Model (TAM) and NASSS model are discussed to guide responsible integration and address adoption barriers.
Key Points
- Generative AI can transform healthcare through automation, enhanced clinical decision-making, and democratized expertise, leading to more efficient and equitable care delivery.
- Applications span billing, diagnosis, treatment, and research, offering personalized suggestions and improving overall healthcare efficiency.
- Successful integration requires meticulous change management, risk mitigation, and structured adoption programs grounded in implementation science.
Conclusions
- Responsible introduction of generative AI can drive significant healthcare progress, provided strategic adoption, incremental deployment, and balanced communication are prioritized.
- Real-world piloting, clinical alignment, and conscientious governance focused on human well-being are essential for safe and ethical integration.
- Continuous reassessment and transparent communication regarding AI capabilities and limitations are vital for building trust and improving patient outcomes.
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