Large language models(LLMs)offer new opportunities in diabetes research by integrating contextual behavioral data with physiological biosignals,supporting more personalized care.This review surveys recent studies on t...Large language models(LLMs)offer new opportunities in diabetes research by integrating contextual behavioral data with physiological biosignals,supporting more personalized care.This review surveys recent studies on the application of LLMs to interpret interactions among stress,physical activity,and glucose dynamics across type 1,type 2,gestational,and monogenic diabetes(MODY).We examine methods for capturing contextual data beyond traditional patient diaries,including wearable sensors,lifestyle logs,and digital health tools,and discuss how these data are combined with continuous glucose monitoring.After retrieving 39 relevant studies and finally retaining 25 after screening,we summarize the current capabilities,limitations,and clinical implications of LLM-assisted multimodal approaches in diabetes management.The findings highlight both the promise and challenges of applying LLMs to synthesize heterogeneous data,providing insights for future research on enhancing individualized and evidence-based diabetes care.展开更多
基金supported by the Starting Research Grant ofÓbuda University,the National Research,Development and Innovation Agency(No.2024-1.1.1-KKV_FÓKUSZ-2024-00074)the National Research,Development and Innovation Fund of Hungary(No.TKP2021-NKTA-36)+4 种基金the National Natural Science Foundation of China(No.82372051)the Beijing Natural Science Foundation(No.L222099)the Science and Technology Development Fund,Macao SAR(Nos.0093/2023/RIA2,0145/2023/RIA3,and 0157/2024/RIA2)the Capital’s Funds for Health Improvement and Research(No.2024-2-4015)the Sichuan Key-Area Research and Development Program(No.2024YFHZ0011).
文摘Large language models(LLMs)offer new opportunities in diabetes research by integrating contextual behavioral data with physiological biosignals,supporting more personalized care.This review surveys recent studies on the application of LLMs to interpret interactions among stress,physical activity,and glucose dynamics across type 1,type 2,gestational,and monogenic diabetes(MODY).We examine methods for capturing contextual data beyond traditional patient diaries,including wearable sensors,lifestyle logs,and digital health tools,and discuss how these data are combined with continuous glucose monitoring.After retrieving 39 relevant studies and finally retaining 25 after screening,we summarize the current capabilities,limitations,and clinical implications of LLM-assisted multimodal approaches in diabetes management.The findings highlight both the promise and challenges of applying LLMs to synthesize heterogeneous data,providing insights for future research on enhancing individualized and evidence-based diabetes care.
