لخّصلي

Large language models (LLMs), a generative AI that can accept image and text inputs and produce text outputs, have shown promise in various aspects of medical care.In the realm of diabetes care, LLMs not only could potentially offer valuable support to patients by enhancing communication, providing personalized information, and assisting continuous management, but also help healthcare professionals streamline clinical tasks and provide clinical training.Therefore, the rapid emergence of clinical knowledge encoded and fine-tuned LLMs, including ClinicalBERT, Med-PaLM2, GatorTron, MedGPT, and Huatuo-GPT, have exhibited significant potential in medical NLP and clinical applications .They are designed to recognize and understand the structure and meaning of human language, classify texts according to their content or purpose, and generate responses that are appropriate and coherent General-purpose LLMs like GPT-4 have shown significant potential in various applications across industries.LLMs are built using natural language processing (NLP) techniques and trained on large quantities of unlabeled text using self-supervised learning or semi-supervised learning.

Large language models (LLMs), a generative AI
that can accept image and text inputs and produce text outputs,
have shown promise in various aspects of medical care.
LLMs are built using natural language processing (NLP) techniques and trained on large quantities of unlabeled text using
self-supervised learning or semi-supervised learning. They are
designed to recognize and understand the structure and meaning
of human language, classify texts according to their content or purpose, and generate responses that are appropriate and coherent
General-purpose LLMs like GPT-4 have shown significant potential
in various applications across industries. However, their performance in medical fields is debatable, especially for diabetes and
cardiovascular disease, which may be due to their insufficient
training dataset of medical specialties . Therefore, the rapid
emergence of clinical knowledge encoded and fine-tuned LLMs,
including ClinicalBERT, Med-PaLM2, GatorTron, MedGPT, and Huatuo-GPT, have exhibited significant potential in medical NLP and
clinical applications .
In the realm of diabetes care, LLMs not only could potentially
offer valuable support to patients by enhancing communication,
providing personalized information, and assisting continuous management, but also help healthcare professionals streamline clinical
tasks and provide clinical training.