Eye diagnosis is a method for inspecting systemic diseases and syndromes by observing the eyes.With the development of intelligent diagnosis in traditional Chinese medicine(TCM);artificial intelligence(AI)can improve ...Eye diagnosis is a method for inspecting systemic diseases and syndromes by observing the eyes.With the development of intelligent diagnosis in traditional Chinese medicine(TCM);artificial intelligence(AI)can improve the accuracy and efficiency of eye diagnosis.However;the research on intelligent eye diagnosis still faces many challenges;including the lack of standardized and precisely labeled data;multi-modal information analysis;and artificial in-telligence models for syndrome differentiation.The widespread application of AI models in medicine provides new insights and opportunities for the research of eye diagnosis intelli-gence.This study elaborates on the three key technologies of AI models in the intelligent ap-plication of TCM eye diagnosis;and explores the implications for the research of eye diagno-sis intelligence.First;a database concerning eye diagnosis was established based on self-su-pervised learning so as to solve the issues related to the lack of standardized and precisely la-beled data.Next;the cross-modal understanding and generation of deep neural network models to address the problem of lacking multi-modal information analysis.Last;the build-ing of data-driven models for eye diagnosis to tackle the issue of the absence of syndrome dif-ferentiation models.In summary;research on intelligent eye diagnosis has great potential to be applied the surge of AI model applications.展开更多
Objective To improve the accuracy and professionalism of question-answering(QA)model in traditional Chinese medicine(TCM)lung cancer by integrating large language models with structured knowledge graphs using the know...Objective To improve the accuracy and professionalism of question-answering(QA)model in traditional Chinese medicine(TCM)lung cancer by integrating large language models with structured knowledge graphs using the knowledge graph(KG)to text-enhanced retrievalaugmented generation(KG2TRAG)method.Methods The TCM lung cancer model(TCMLCM)was constructed by fine-tuning Chat-GLM2-6B on the specialized datasets Tianchi TCM,HuangDi,and ShenNong-TCM-Dataset,as well as a TCM lung cancer KG.The KG2TRAG method was applied to enhance the knowledge retrieval,which can convert KG triples into natural language text via ChatGPT-aided linearization,leveraging large language models(LLMs)for context-aware reasoning.For a comprehensive comparison,MedicalGPT,HuatuoGPT,and BenTsao were selected as the baseline models.Performance was evaluated using bilingual evaluation understudy(BLEU),recall-oriented understudy for gisting evaluation(ROUGE),accuracy,and the domain-specific TCM-LCEval metrics,with validation from TCM oncology experts assessing answer accuracy,professionalism,and usability.Results The TCMLCM model achieved the optimal performance across all metrics,including a BLEU score of 32.15%,ROUGE-L of 59.08%,and an accuracy rate of 79.68%.Notably,in the TCM-LCEval assessment specific to the field of TCM,its performance was 3%−12%higher than that of the baseline model.Expert evaluations highlighted superior performance in accuracy and professionalism.Conclusion TCMLCM can provide an innovative solution for TCM lung cancer QA,demonstrating the feasibility of integrating structured KGs with LLMs.This work advances intelligent TCM healthcare tools and lays a foundation for future AI-driven applications in traditional medicine.展开更多
OBJECTIVE： This study investigated how polypeptide 2B1 is involved in regulating and governing dampness in rat models with dampness pattern defined in terms of Traditional Chinese Medicine. METHODS： We randomly divi...OBJECTIVE： This study investigated how polypeptide 2B1 is involved in regulating and governing dampness in rat models with dampness pattern defined in terms of Traditional Chinese Medicine. METHODS： We randomly divided 48 SPF 10-week-old male Sprague-Dawley （SD） rats into a normal group, normal ＋ Aristolochic acid I （AA-I） for 5 min group, normal ＋ AA-I for 60 min group, dampness pattern group （DS-Group）, dampness pattern ＋ AA-I for 5 rain tern ＋ AA-I for 60 min group, and dampness pat- group. Groups were then treated accordingly. We took out the lung, stom- ach, liver, spleen, kidney, large intestine, and small intestine tissues to detect gene and protein expres- sion of organic anion transporter polypeptide 2B1 （OATP2B1）. RESULTS= Gene expression of OATP2B1 in spleen, kidney, and small intestine of rats with dampness pattern was lower than that in normal rats （P〈0.05）. The gene expressions of OATP2B1 in liver, stomach, large intestine, and small intestine were lower than that in control rats at different time points after being stimulated by AA-I （P〈0.05）. CONCLUSION There is coordination among multiple viscera in handling the condition of dampness, and the mechanism underlying the action may rely on regulating the expression of OATP2B1.展开更多
基金National Natural Science Foundation of China(82274265 and 82274588)Hunan University of Traditional Chinese Medicine Research Unveiled Marshal Programs(2022XJJB003).
文摘Eye diagnosis is a method for inspecting systemic diseases and syndromes by observing the eyes.With the development of intelligent diagnosis in traditional Chinese medicine(TCM);artificial intelligence(AI)can improve the accuracy and efficiency of eye diagnosis.However;the research on intelligent eye diagnosis still faces many challenges;including the lack of standardized and precisely labeled data;multi-modal information analysis;and artificial in-telligence models for syndrome differentiation.The widespread application of AI models in medicine provides new insights and opportunities for the research of eye diagnosis intelli-gence.This study elaborates on the three key technologies of AI models in the intelligent ap-plication of TCM eye diagnosis;and explores the implications for the research of eye diagno-sis intelligence.First;a database concerning eye diagnosis was established based on self-su-pervised learning so as to solve the issues related to the lack of standardized and precisely la-beled data.Next;the cross-modal understanding and generation of deep neural network models to address the problem of lacking multi-modal information analysis.Last;the build-ing of data-driven models for eye diagnosis to tackle the issue of the absence of syndrome dif-ferentiation models.In summary;research on intelligent eye diagnosis has great potential to be applied the surge of AI model applications.
基金Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX24_2145).
文摘Objective To improve the accuracy and professionalism of question-answering(QA)model in traditional Chinese medicine(TCM)lung cancer by integrating large language models with structured knowledge graphs using the knowledge graph(KG)to text-enhanced retrievalaugmented generation(KG2TRAG)method.Methods The TCM lung cancer model(TCMLCM)was constructed by fine-tuning Chat-GLM2-6B on the specialized datasets Tianchi TCM,HuangDi,and ShenNong-TCM-Dataset,as well as a TCM lung cancer KG.The KG2TRAG method was applied to enhance the knowledge retrieval,which can convert KG triples into natural language text via ChatGPT-aided linearization,leveraging large language models(LLMs)for context-aware reasoning.For a comprehensive comparison,MedicalGPT,HuatuoGPT,and BenTsao were selected as the baseline models.Performance was evaluated using bilingual evaluation understudy(BLEU),recall-oriented understudy for gisting evaluation(ROUGE),accuracy,and the domain-specific TCM-LCEval metrics,with validation from TCM oncology experts assessing answer accuracy,professionalism,and usability.Results The TCMLCM model achieved the optimal performance across all metrics,including a BLEU score of 32.15%,ROUGE-L of 59.08%,and an accuracy rate of 79.68%.Notably,in the TCM-LCEval assessment specific to the field of TCM,its performance was 3%−12%higher than that of the baseline model.Expert evaluations highlighted superior performance in accuracy and professionalism.Conclusion TCMLCM can provide an innovative solution for TCM lung cancer QA,demonstrating the feasibility of integrating structured KGs with LLMs.This work advances intelligent TCM healthcare tools and lays a foundation for future AI-driven applications in traditional medicine.
基金Supported by National Natural Science Foundation of China (Exprimental Study of Relation Between Transit of Dampness Turbidity in Lung,Spleen and Kidney and Adjust and Control of Organic Anion Transportingpolypeptide.No.81072806)
文摘OBJECTIVE： This study investigated how polypeptide 2B1 is involved in regulating and governing dampness in rat models with dampness pattern defined in terms of Traditional Chinese Medicine. METHODS： We randomly divided 48 SPF 10-week-old male Sprague-Dawley （SD） rats into a normal group, normal ＋ Aristolochic acid I （AA-I） for 5 min group, normal ＋ AA-I for 60 min group, dampness pattern group （DS-Group）, dampness pattern ＋ AA-I for 5 rain tern ＋ AA-I for 60 min group, and dampness pat- group. Groups were then treated accordingly. We took out the lung, stom- ach, liver, spleen, kidney, large intestine, and small intestine tissues to detect gene and protein expres- sion of organic anion transporter polypeptide 2B1 （OATP2B1）. RESULTS= Gene expression of OATP2B1 in spleen, kidney, and small intestine of rats with dampness pattern was lower than that in normal rats （P〈0.05）. The gene expressions of OATP2B1 in liver, stomach, large intestine, and small intestine were lower than that in control rats at different time points after being stimulated by AA-I （P〈0.05）. CONCLUSION There is coordination among multiple viscera in handling the condition of dampness, and the mechanism underlying the action may rely on regulating the expression of OATP2B1.
