Advancing Personalized and Precision Medicine with Large Language Models

Dear Colleagues,

Advances in large language models (LLMs) are rapidly transforming the landscape of personalized and precision medicine. Beyond traditional natural language processing tasks, LLMs are increasingly being applied to clinical documentation, decision support, risk stratification and the integration of multimodal biomedical data. Their ability to extract, synthesize and contextualize information from unstructured and structured sources offers new opportunities to improve clinical workflows and enhance patient-centered care.

This Special Issue invites contributions that explore the development, validation and clinical application of LLMs and related generative AI approaches across diverse domains of medicine. Topics of interest include, but are not limited to, automated phenotyping from electronic health records, clinical summarization, prognostic modeling, multimodal data integration and real-world implementation of AI systems in clinical environments.

A particularly important emerging direction is the use of LLMs to interpret complex biomedical data, including outputs from machine-learning models, high-dimensional phenotype representations and biological ontologies, translating these into clinically meaningful insights.

We welcome original research and reviews that advance the field and address the opportunities and challenges of deploying LLMs in precision medicine.

Dr. Daniel B. Hier
Guest Editor

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