Version 1
: Received: 10 July 2024 / Approved: 11 July 2024 / Online: 11 July 2024 (10:20:18 CEST)
How to cite:
Jiang, X.; Yan, L.; Vavekanand, R.; Hu, M. Large Language Models in Healthcare Current Development and Future Directions. Preprints2024, 2024070923. https://doi.org/10.20944/preprints202407.0923.v1
Jiang, X.; Yan, L.; Vavekanand, R.; Hu, M. Large Language Models in Healthcare Current Development and Future Directions. Preprints 2024, 2024070923. https://doi.org/10.20944/preprints202407.0923.v1
Jiang, X.; Yan, L.; Vavekanand, R.; Hu, M. Large Language Models in Healthcare Current Development and Future Directions. Preprints2024, 2024070923. https://doi.org/10.20944/preprints202407.0923.v1
APA Style
Jiang, X., Yan, L., Vavekanand, R., & Hu, M. (2024). Large Language Models in Healthcare Current Development and Future Directions. Preprints. https://doi.org/10.20944/preprints202407.0923.v1
Chicago/Turabian Style
Jiang, X., Raja Vavekanand and Mengxuan Hu. 2024 "Large Language Models in Healthcare Current Development and Future Directions" Preprints. https://doi.org/10.20944/preprints202407.0923.v1
Abstract
The rapid advancement of large language models (LLMs), such as ChatGPT, has revolutionized natural language processing, enabling these models to generate coherent and contextually relevant text. These capabilities hold significant potential for various applications, particularly in the medical field. LLMs can assist healthcare professionals by providing accurate information, supporting diagnostic processes, and enhancing medical education. However, the implementation of LLMs in clinical settings faces several barriers, including technical limitations, ethical concerns, regulatory constraints, and practical challenges. Ensuring accuracy, reliability, and interpretability is crucial, along with addressing biases, ensuring data privacy, and establishing ethical guidelines. Future research should focus on improving training techniques, developing explainable AI methods, and creating efficient, resource-saving models. Expanding applications through domain-specific models and multi-modal integration can further unlock the potential of LLMs. By addressing these challenges and prioritizing responsible development, LLM technology can be safely and effectively integrated into various domains, driving innovation and improving outcomes. This paper explores the current state of LLM technology, its medical applications, barriers to implementation, and directions for future research and development
Keywords
LLM; Healthcare; AI
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
