Objective:To describe the hotspots and application trends of artificial intelligence (AI) in epidemiology in the past decade and analyze its advantages and challenges.Methods:The literatures with AI and epidemiology related keywords were systematically retrieved from Web of Science and China National Knowledge Infrastructure from 2014 to 2024. CiteSpace was used for bibliometric analysis of publication volume, keyword co-occurrence, clustering, emergence and cited literature co-occurrence analysis.Results:A total of 5 389 English papers and 1 659 Chinese papers were included, showing an increasing publication trend. High-frequency Chinese keywords included prediction, influencing factor, and machine learning, while English keywords frequently used were machine learning, prediction, and artificial intelligence. The Chinese keywords formed 14 clusters such as epidemiological characteristic, dietary pattern, and elderly individual, and the English keywords formed 21 clusters including prediction model, risk factor, and adult. In international studies, health policy, COVID-19, and digital health were the emerging frontier keywords. Eleven core papers were selected, covering key areas like traffic accident risk assessment, public health big data application, and deep learning in medical diagnosis.Conclusions:This study systematically summarized the research hotspots and development trends of AI applications in epidemiology over the past decade by using bibliometric methods, which indicated that current AI-based epidemiological studies are still in the exploratory phase, with the coexisting of both advantages and challenges. Continued attention should be paid to the future development of this field.

The emergence of artificial intelligence(AI)has had a significant impact on medical research and practice,both in terms of the number of studies and research paradigms,and has become an important tool for the development of pharmacoepidemiology.However,traditional AI has faced many challenges,while facilitating pharmacoepidemiology research,such as complex data processing,difficulty in identifying drug exposures and potential outcomes,and time-consuming and laborious study design and implementation.The rapid development of generative AI,represented by large language models(LLMs),has demonstrated a unique potential to enhance research efficiency,shift research paradigms,and facilitate knowledge discovery.LLMs are equipped with natural language understanding and generation capabilities.Through deep mining of multi-dimensional data resources,LLMs can quickly and accurately extract,analyze,summarize,and present the required information,which can not only help drug discovery,drug repurposing,pharmacovigilance and other pharmacoepidemiological tasks,but also provide powerful support for the whole process of research protocol design,data analysis,result interpretation and paper publication.Driven by LLMs,pharmacoepidemiology research is gradually moving into a new stage based on big data and automated analysis.Of course,LLMs also have problems of data bias,"illusion"of results,and ethical and legal regulation.By strengthening interdisciplinary cooperation,establishing a standardized evaluation system,improving ethical and regulatory guidance,enhancing data quality,strengthening practitioner training and capacity building,and promoting human-machine collaborative research modes,it is expected that the potential of LLMs in pharmacoepidemiology will be fully released,and it will provide a more scientific,rapid,and efficient technological support for drug regulation and public health decision-making.

The emergence of artificial intelligence(AI)has had a significant impact on medical research and practice,both in terms of the number of studies and research paradigms,and has become an important tool for the development of pharmacoepidemiology.However,traditional AI has faced many challenges,while facilitating pharmacoepidemiology research,such as complex data processing,difficulty in identifying drug exposures and potential outcomes,and time-consuming and laborious study design and implementation.The rapid development of generative AI,represented by large language models(LLMs),has demonstrated a unique potential to enhance research efficiency,shift research paradigms,and facilitate knowledge discovery.LLMs are equipped with natural language understanding and generation capabilities.Through deep mining of multi-dimensional data resources,LLMs can quickly and accurately extract,analyze,summarize,and present the required information,which can not only help drug discovery,drug repurposing,pharmacovigilance and other pharmacoepidemiological tasks,but also provide powerful support for the whole process of research protocol design,data analysis,result interpretation and paper publication.Driven by LLMs,pharmacoepidemiology research is gradually moving into a new stage based on big data and automated analysis.Of course,LLMs also have problems of data bias,"illusion"of results,and ethical and legal regulation.By strengthening interdisciplinary cooperation,establishing a standardized evaluation system,improving ethical and regulatory guidance,enhancing data quality,strengthening practitioner training and capacity building,and promoting human-machine collaborative research modes,it is expected that the potential of LLMs in pharmacoepidemiology will be fully released,and it will provide a more scientific,rapid,and efficient technological support for drug regulation and public health decision-making.

Objective:To describe the hotspots and application trends of artificial intelligence (AI) in epidemiology in the past decade and analyze its advantages and challenges.Methods:The literatures with AI and epidemiology related keywords were systematically retrieved from Web of Science and China National Knowledge Infrastructure from 2014 to 2024. CiteSpace was used for bibliometric analysis of publication volume, keyword co-occurrence, clustering, emergence and cited literature co-occurrence analysis.Results:A total of 5 389 English papers and 1 659 Chinese papers were included, showing an increasing publication trend. High-frequency Chinese keywords included prediction, influencing factor, and machine learning, while English keywords frequently used were machine learning, prediction, and artificial intelligence. The Chinese keywords formed 14 clusters such as epidemiological characteristic, dietary pattern, and elderly individual, and the English keywords formed 21 clusters including prediction model, risk factor, and adult. In international studies, health policy, COVID-19, and digital health were the emerging frontier keywords. Eleven core papers were selected, covering key areas like traffic accident risk assessment, public health big data application, and deep learning in medical diagnosis.Conclusions:This study systematically summarized the research hotspots and development trends of AI applications in epidemiology over the past decade by using bibliometric methods, which indicated that current AI-based epidemiological studies are still in the exploratory phase, with the coexisting of both advantages and challenges. Continued attention should be paid to the future development of this field.

Objective:To investigate the effect of Mediterranean diet on blood glucose control and cardiovascular risk factors in patients with type 2 diabetes.Methods:As to December 2021, the PubMed, Cochrance Central Register of Controlled Trials and Cochrance Database, Cochranc Library, Embase, China National Knowledge Infrastructure and Wanfang Medical Network system were searched for clinical randomized controlled trials(RCTs) of Mediterranean diet in patients with type 2 diabetes to conduct Meta-analysis The main observation index were cardiovascular risk factors, and the mean difference and its 95% confidence interval were used to estimate the effect size.Results:There were six RCTs, and 1181 patients met the inclusion criteria and entered the Meta-analysis. Compared with the control group, the intervention group can significantly reduce the level of systolic blood pressure ( MD=-1.20, 95% CI-2.21 to -0.19) and diastolic blood pressure ( MD=-4.17, 95% CI-7.12 to -1.22) in patients with type 2 diabetes mellitus, but there were no significant difference in the level of TC ( MD=2.92, 95% CI-0.84 to-6.67), HDL ( MD=2.33, 95% CI-0.27 to -4.92) and LDL ( MD=-2.34, 95% CI-5.67 to -0.99) between the two groups (all P>0.05). Conclusions:The meta-analysis provided evidence the Mediterranean diet showed the beneficial improvements in blood pressure glycemic control, but the effect of Mediterranean diet on lipid profile was not significant, which needed further verification.

Autophagy is one of the most common protective mechanisms during plant stress response. We studied the effect of exogenous Cd on autophagy in celery, by using transcriptome sequencing technique to analyze the differentially expressed genes under different Cd concentrations (0, 2, 4 and 8 mg/L). Eight differentially expressed autophagy-related genes were screened and identified by qRT-PCR. Cd had obvious toxic effect on celery, in a dose-dependent manner. Eight differentially expressed autophagy-related genes were screened, among which ATG8a, ATG8f, ATG13, AMPK-1 and AMPK-2 were up-regulated, whereas ATG12, VPS30 and VPS34 were first up-regulated and then decreased. The up-regulated expression of differential genes may resist Cd toxicity by increasing autophagosome structures; however, 8 mg/L Cd exceeded the autophagosome tolerance limit of celery, resulting in decreased expression of multiple autophagy-related genes. The above results can provide help for subsequent functional study of autophagy-related genes, and provide a reference for further exploring the tolerance mechanism of celery to Cd toxicity.