Large Language Models in Healthcare: Innovations, Challenges, and Future Prospects
Code: G-1545
Authors: Amin Rezanejad * ℗, Ali Heydari, Ayda Hafsi Kurdestani, Amir Seyed Danesh
Schedule: Not Scheduled!
Tag: Intelligent Virtual Assistant
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Abstract:
Abstract
Background and aims: The rapid advancements in Natural Language Processing (NLP) and Large Language Models (LLMs) have significantly impacted various industries, with healthcare being one of the most promising domains. LLMs, such as BERT and GPT, have revolutionized medical informatics by enhancing clinical decision-making, biomedical research, and drug discovery. This study aims to explore the development, applications, challenges, and future prospects of LLMs in healthcare. Method: A comprehensive review of literature from 2015 to 2024 was conducted, analyzing studies related to the implementation of LLMs in healthcare. The review covers the evolution of language models, their architectures, and their applications in clinical diagnosis, medical decision support, drug discovery, and pandemic management. Additionally, the limitations and ethical concerns associated with LLMs in medical practice were examined. Results: Findings indicate that LLMs have significantly contributed to improving diagnostic accuracy, streamlining clinical workflows, and accelerating biomedical research. However, challenges such as data biases, hallucinations, and the need for domain-specific expertise remain significant barriers to widespread adoption. Ethical concerns, including patient data privacy and model transparency, also require careful consideration. Conclusion: Despite their transformative potential, the integration of LLMs into healthcare demands rigorous validation, ethical oversight, and continuous refinement. Future research should focus on developing specialized medical LLMs, mitigating biases, and establishing robust regulatory frameworks to ensure their safe and effective use in clinical settings.
Keywords
Large Language Models, Intelligent Medicine, Healthcare
