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

With the popularization of large language models (LLMs), strategies for their effective and safe usage in health care and research have become increasingly pertinent. Despite the growing interest and eagerness among health care professionals and scientists to exploit the potential of LLMs, initial attempts may yield suboptimal results due to a lack of user experience, thus complicating the integration of artificial intelligence (AI) tools into workplace routine. Focusing on scientists and health care professionals with limited LLM experience, this viewpoint article highlights and discusses 6 easy-to-implement use cases of practical relevance. These encompass customizing translations, refining text and extracting information, generating comprehensive overviews and specialized insights, compiling ideas into cohesive narratives, crafting personalized educational materials, and facilitating intellectual sparring. Additionally, we discuss general prompting strategies and precautions for the implementation of AI tools in biomedicine. Despite various hurdles and challenges, the integration of LLMs into daily routines of physicians and researchers promises heightened workplace productivity and efficiency.

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Publication Details

Journal
JMIR Medical Informatics
Published
2024-09-05
DOI
https://doi.org/10.2196/58478
Citations
15
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
1.80
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article

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

Manfred Gossen, Christian Lenz, Hans‐Dieter Volk, Norman Michael Drzeniek et al.
15 citations
JMIR Medical Informatics
Artificial Intelligence in Healthcare and Education
1.80
article

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

Manfred Gossen, Christian Lenz, Hans‐Dieter Volk, Norman Michael Drzeniek, Florian Reis
article en
15 citations

Abstract

With the popularization of large language models (LLMs), strategies for their effective and safe usage in health care and research have become increasingly pertinent. Despite the growing interest and eagerness among health care professionals and scientists to exploit the potential of LLMs, initial attempts may yield suboptimal results due to a lack of user experience, thus complicating the integration of artificial intelligence (AI) tools into workplace routine. Focusing on scientists and health care professionals with limited LLM experience, this viewpoint article highlights and discusses 6 easy-to-implement use cases of practical relevance. These encompass customizing translations, refining text and extracting information, generating comprehensive overviews and specialized insights, compiling ideas into cohesive narratives, crafting personalized educational materials, and facilitating intellectual sparring. Additionally, we discuss general prompting strategies and precautions for the implementation of AI tools in biomedicine. Despite various hurdles and challenges, the integration of LLMs into daily routines of physicians and researchers promises heightened workplace productivity and efficiency.

JMIR Medical InformaticsVol. 12
Humboldt-Universität zu Berlin (DE), Pfizer (Germany) (DE), Berlin-Brandenburger Centrum für Regenerative Therapien (DE), Helmholtz-Zentrum Hereon (DE), Berlin Institute of Health at Charité - Universitätsmedizin Berlin (DE)
Decent work and economic growth
Openalex Percentile: Top 14%
Artificial Intelligence in Healthcare and Education
1.80
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Practical Applications of Large Language Models for Health Care Professionals and Scientists — Manfred Gossen, Christian Lenz, et al. · JMIR Medical Informatics (2024) | TGRS Research Map | TGRS