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This article examines the integration of large language models into medical research and clinical practice, weighing their expanded data flexibility against critical concerns regarding reliability and safety. It synthesizes recent discourse on the appropriate role of LLMs in healthcare, highlighting both emerging opportunities and implementation challenges specific to this domain.
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Applying Language Models in medical Medicine: Recent Trends and Perspectives

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2609.34780v1 Announce Type: new Abstract: The use and applicability of artificial intelligence (AI) in medical research and clinical practice has received increasing attention in the literature over recent years. The emergence of large language models (LLMs) has expanded discussions in regards to applications of AI within healthcare. While traditional deep learning based AI applications in medicine have often focused on specific and defined tasks, LLMs offer broader capabilities and flexibility in working with available data,. At the same time of writing, the integration of LLMs into medical settings raises important questions regarding their reliability, accuracy, transparency, safety, and appropriate role in a medical setting. This text presents and discusses recent talks and articles concerning the application of LLMs in medicine, with particular emphasis on their potential utility in research and clinical practice. It considers both the opportunities offered by these technologies and the challenges associated with their implementation, aiming to provide a perspective on the current and emerging role of LLMs within the medical field.

Abstract:The use and applicability of artificial intelligence (AI) in medical research and clinical practice has received increasing attention in the literature over recent years. The emergence of large language models (LLMs) has expanded discussions in regards to applications of AI within healthcare. While traditional deep learning based AI applications in medicine have often focused on specific and defined tasks, LLMs offer broader capabilities and flexibility in working with available data,. At the same time of writing, the integration of LLMs into medical settings raises important questions regarding their reliability, accuracy, transparency, safety, and appropriate role in a medical setting. This text presents and discusses recent talks and articles concerning the application of LLMs in medicine, with particular emphasis on their potential utility in research and clinical practice. It considers both the opportunities offered by these technologies and the challenges associated with their implementation, aiming to provide a perspective on the current and emerging role of LLMs within the medical field.

Artificial Intelligence (cs.AI)

arXiv:2609.34780 [cs.AI]

(or arXiv:2609.34780v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.34780 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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