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Generative AI and Large Language Models in Health Care: Pathways to Implementation
Generative AI and large language models (LLMs) offer promising avenues for healthcare, with applications including interpreting electronic medical records (EMRs) and enhancing predictive performance. However, concerns exist regarding implementation barriers and model hallucination. To overcome these challenges, a comprehensive pathway to implementation is necessary. This includes establishing leadership for model development and validation, continued regulation to ensure safety and efficacy, and incentivizing adoption through payer incentives. Moreover, collaboration between stakeholders and investment in research and development are crucial. By addressing these factors, generative AI can move beyond hype and become a valuable asset in healthcare, driving improved outcomes and efficiency in clinical practice.
Medigy Insights
Generative AI and large language models (LLMs) present promising applications in healthcare, particularly in interpreting electronic medical records (EMRs) and enhancing predictive performance. However, challenges such as model hallucination and implementation barriers persist. Overcoming these hurdles requires a comprehensive pathway to implementation, including leadership for model development, continued regulation, and incentivizing adoption through payer incentives. Collaboration among stakeholders and investment in research and development are vital. By addressing these factors, generative AI can evolve beyond hype to become a valuable asset in healthcare, driving improved outcomes and efficiency in clinical practice.
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