Leveraging Generative AI and Large Language Models: A Comprehensive Roadmap for Healthcare Integration

Generative artificial intelligence (AI) and large language models (LLMs), exemplified by ChatGPT, are promising for revolutionizing data and information management in healthcare and medicine. However, there is scant literature guiding their integration for non-AI professionals. This study conducts a...

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Bibliographic Details
Published in:Healthcare (Basel) 2023-10, Vol.11 (20), p.2776
Main Authors: Yu, Ping, Xu, Hua, Hu, Xia, Deng, Chao
Format: Article
Language:English
Subjects:
Artificial intelligence
Chatbots
Decision making
Deep learning
Electronic health records
Ethics
Evaluation
generative AI
Generative artificial intelligence
Health care industry
Health care reform
healthcare
Keywords
Language
Large language models
LLM
Machine learning
Medicine
Natural language
R&D
Research & development
Review
Technology application
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Summary:Generative artificial intelligence (AI) and large language models (LLMs), exemplified by ChatGPT, are promising for revolutionizing data and information management in healthcare and medicine. However, there is scant literature guiding their integration for non-AI professionals. This study conducts a scoping literature review to address the critical need for guidance on integrating generative AI and LLMs into healthcare and medical practices. It elucidates the distinct mechanisms underpinning these technologies, such as Reinforcement Learning from Human Feedback (RLFH), including few-shot learning and chain-of-thought reasoning, which differentiates them from traditional, rule-based AI systems. It requires an inclusive, collaborative co-design process that engages all pertinent stakeholders, including clinicians and consumers, to achieve these benefits. Although global research is examining both opportunities and challenges, including ethical and legal dimensions, LLMs offer promising advancements in healthcare by enhancing data management, information retrieval, and decision-making processes. Continued innovation in data acquisition, model fine-tuning, prompt strategy development, evaluation, and system implementation is imperative for realizing the full potential of these technologies. Organizations should proactively engage with these technologies to improve healthcare quality, safety, and efficiency, adhering to ethical and legal guidelines for responsible application.
ISSN:2227-9032
2227-9032
DOI:10.3390/healthcare11202776