About this Research Topic
Large Language Models (LLMs) have revolutionized various domains with their capabilities to understand, generate, and process human language at scale. In the realm of healthcare, LLMs hold immense potential to transform how medical information is analyzed, communicated, and utilized. This Research Topic delves into the applications, challenges, and future prospects of employing LLMs in medical settings.
The adoption of LLMs in medical settings holds the promise of enhancing clinical workflows, improving patient outcomes, and facilitating more informed decision-making processes. These models, built upon vast corpora of medical literature, patient records, and clinical guidelines, possess the capacity to sift through and distil complex information, providing health professionals with timely insights and recommendations tailored to individual patient needs.
The widespread adoption of LLMs in healthcare also raises important ethical, regulatory, performance and technical considerations. Concerns related to patient privacy, data security, algorithmic bias, and the responsible deployment of AI-driven solutions underscore the need for robust governance frameworks and transparent practices. Additionally, challenges such as model interpretability and disparities in access to AI technologies must be addressed to ensure equitable and ethical deployment of LLMs across diverse medical settings.
Despite these challenges, the promise of LLMs to transform healthcare delivery, improve patient outcomes, and advance medical knowledge remains undeniably compelling. This Research Topic aims to explore the myriad applications, challenges, and future directions of LLMs in medical settings, bringing together researchers, clinicians, policymakers, and industry experts to contribute to the ongoing dialogue surrounding this transformative technology.
Themes of interest for this Research Topic include, but are not limited to:
• Leveraging LLMs for clinical decision support;
• Multimodal LLMs for health applications;
• Patient engagement and education through LLMs;
• Integration of LLMs into telemedicine and remote patient monitoring;
• Training of healthcare professionals for LLM applications;
• Uses of LLMs in the training of healthcare professionals;
• Bias and fairness in LLMs for healthcare;
• Cross-lingual capabilities of LLMs in medicine;
• LLM-based tools for diagnostic and prognostic prediction;
• LLM-driven precision medicine and personalized treatment approaches;
• Ethical and regulatory considerations in LLM deployment;
• Strategies for ensuring transparency, explainability, and accountability in LLM-powered medical solutions.
The adoption of LLMs in medical settings holds the promise of enhancing clinical workflows, improving patient outcomes, and facilitating more informed decision-making processes. These models, built upon vast corpora of medical literature, patient records, and clinical guidelines, possess the capacity to sift through and distil complex information, providing health professionals with timely insights and recommendations tailored to individual patient needs.
The widespread adoption of LLMs in healthcare also raises important ethical, regulatory, performance and technical considerations. Concerns related to patient privacy, data security, algorithmic bias, and the responsible deployment of AI-driven solutions underscore the need for robust governance frameworks and transparent practices. Additionally, challenges such as model interpretability and disparities in access to AI technologies must be addressed to ensure equitable and ethical deployment of LLMs across diverse medical settings.
Despite these challenges, the promise of LLMs to transform healthcare delivery, improve patient outcomes, and advance medical knowledge remains undeniably compelling. This Research Topic aims to explore the myriad applications, challenges, and future directions of LLMs in medical settings, bringing together researchers, clinicians, policymakers, and industry experts to contribute to the ongoing dialogue surrounding this transformative technology.
Themes of interest for this Research Topic include, but are not limited to:
• Leveraging LLMs for clinical decision support;
• Multimodal LLMs for health applications;
• Patient engagement and education through LLMs;
• Integration of LLMs into telemedicine and remote patient monitoring;
• Training of healthcare professionals for LLM applications;
• Uses of LLMs in the training of healthcare professionals;
• Bias and fairness in LLMs for healthcare;
• Cross-lingual capabilities of LLMs in medicine;
• LLM-based tools for diagnostic and prognostic prediction;
• LLM-driven precision medicine and personalized treatment approaches;
• Ethical and regulatory considerations in LLM deployment;
• Strategies for ensuring transparency, explainability, and accountability in LLM-powered medical solutions.
Keywords: Large Language Models, LLMs, Natural Language Processing, Medical Education, Decision Support Systems
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