Practical Applications of Large Language Models for Health Care Professionals and Scientists.

Area of Science:

• Biomedical research
• Healthcare informatics
• Artificial intelligence applications

Background:

• Growing interest in large language models (LLMs) among healthcare professionals and scientists.
• Challenges in integrating AI tools due to limited user experience, leading to suboptimal initial results.
• Need for practical strategies for effective and safe LLM utilization in health care and research.

Purpose of the Study:

• To provide scientists and healthcare professionals with limited LLM experience practical, easy-to-implement use cases.
• To discuss general prompting strategies and precautions for AI tool implementation in biomedicine.
• To highlight the potential of LLMs to improve workplace productivity and efficiency.

Main Methods:

• Focus on a viewpoint article format.
• Identification and discussion of six key LLM use cases.
• Inclusion of general prompting strategies and safety considerations.

Main Results:

• Six practical LLM use cases identified: customized translations, text refinement and information extraction, generating overviews and insights, idea compilation, personalized educational material creation, and intellectual sparring.
• Discussion of general prompting strategies and precautions for AI implementation.
• Emphasis on the potential for increased productivity and efficiency.

Conclusions:

• LLMs can significantly enhance productivity and efficiency for physicians and researchers.
• Addressing user experience gaps through practical use cases and strategies is crucial for successful AI integration.
• Careful implementation and adherence to precautions are necessary for safe and effective use of AI tools in biomedicine.