The enlightenment of artificial intelligence large-scale model on the research of intelligent eye diagnosis in traditional Chinese medicine

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.

Systems Theory-Driven Framework for AI Integration into the Holistic Material Basis Research of Traditional Chinese Medicine

This paper introduces a systems theory-driven framework to integration artificial intelligence(AI)into traditional Chinese medicine(TCM)research,enhancing the understanding of TCM’s holistic material basis while adhering to evidence-based principles.Utilizing the System Function Decoding Model(SFDM),the research progresses through define,quantify,infer,and validate phases to systematically explore TCM’s material basis.It employs a dual analytical approach that combines top-down,systems theory-guided perspectives with bottom-up,elements-structure-function methodologies,provides comprehensive insights into TCM’s holistic material basis.Moreover,the research examines AI’s role in quantitative assessment and predictive analysis of TCM’s material components,proposing two specific AIdriven technical applications.This interdisciplinary effort underscores AI’s potential to enhance our understanding of TCM’s holistic material basis and establishes a foundation for future research at the intersection of traditional wisdom and modern technology.

Feasibility and Challenges of Interactive AI for Traditional Chinese Medicine: An Example of ChatGPT

As a complementary and alternative medicine(CAM),traditional Chinese medicine(TCM)represents an established medical system with a rich history and abundant clinical experience.TCM is an empirical medicine,the process of which is analogous to ChatGPT’s learning and development model.In TCM,inquiry is a relatively objective way of traditional syndrome differentiation.Although various artificial intelligence systems related to TCM consultation exist,their interactive abilities remain limited.The study standardized the primary complaint and instructed ChatGPT to simulate a TCM practitioner,conducting three comprehensive inquiry tests.The results yielded unexpected conclusions,revealing that ChatGPT could simulate a TCM practitioner’s inquiry with patients,confirming its potential in the field of TCM inquiry.However,current applications still pose certain limitations and risks.Hence,to integrate ChatGPT-like language models with traditional TCM AI to establish an associative mode that can facilitate TCM diagnosis and treatment with more convenience and standardization is crucial,yet at the same time,it should be treated very carefully.

Research on Empowering the Development of Traditional Chinese Medicine with New Quality Productivity

The Traditional Chinese Medicine(TCM)industry,deeply rooted in cultural heritage and complex production processes,has been advancing through technological innovations such as intelligent production systems,digital management,blockchain traceability,and personalized health services.Despite its historical and cultural significance,the integration of these modern technologies offers significant potential to enhance the industry’s modernization and internationalization.Empirical research and case studies indicate that adopting intelligent production systems,blockchain-enabled traceability,and data-driven resource management strategies can address industry challenges while promoting sustainable development.This paper examines the current state of new quality productivity in TCM,evaluates the challenges and opportunities of its application,and proposes strategic recommendations for the industry’s sustainable growth.The findings suggest that new quality productivity can improve production efficiency,ensure consistent product quality,and boost consumer confidence,particularly in global markets.These insights offer a theoretical foundation and practical strategies for applying advanced productivity models in TCM,with potential implications for other traditional healthcare industries.

Application and Development of Intelligent Medicine in Traditional Chinese Medicine

As modern science and technology constantly progresses,the fields of artificial intelligence,mixed reality technology,remote technology,etc.have rapidly developed.Meanwhile,these technologies have been gradually applied to the medical field,leading to the development of intelligent medicine.What’s more,intelligent medicine has greatly promoted the development of traditional Chinese medicine(TCM),causing huge changes in the diagnosis of TCM ailments,remote treatment,teaching,etc.Therefore,there are both opportunities and challenges for inheriting and developing TCM.Herein,the related research progress of intelligent medicine in the TCM in China and abroad over the years is analyzed,with the purpose of introducing the present application status of intelligent medicine in TCM and providing reference for the inheritance and development of TCM in a new era.

Artificial intelligence meets traditional Chinese medicine: a bridge to opening the magic box of sphygmopalpation for pulse pattern recognition

Artificial intelligence(AI) aims to mimic human cognitive functions and execute intellectual activities like that performed by humans dealing with an uncertain environment. The rapid development of AI technology provides powerful tools to analyze massive amounts of data, facilitating physicians to make better clinical decisions or even replace human judgment in healthcare.Advanced AI technology also creates novel opportunities for exploring the scientific basis of traditional Chinese medicine(TCM) and developing the standardization and digitization of TCM pulse diagnostic methodology. In the present study, we review and discuss the potential application of AI technology in TCM pulse diagnosis. The major contents include the following aspects:(1) a brief introduction of the general concepts and knowledge of TCM pulse diagnosis or palpation,(2) landmark developments in AI technology and the applications of common AI deep learning algorithms in medical practice,(3) the current progress of AI technology in TCM pulse diagnosis,(4) challenges and perspectives of AI technology in TCM pulse diagnosis. In conclusion, the pairing of TCM with modern AI technology will bring novel insights into understanding the scientific principles underlying TCM pulse diagnosis and creating opportunities for the development of AI deep learning technology for the standardization and digitalization of TCM pulse diagnosis.

Application of artificial intelligence in tongue diagnosis of traditional Chinese medicine:A review

Tongue diagnosis is an important process to non-invasively assess the condition of a patient’s internal organs in traditional Chinese medicine(TCM)and each part of the tongue is related to corresponding internal organs.Due to continuing computer technological advances,especially the artificial intelligence(AI)methods have achieved significant success in tackling tongue image acquisition,processing,and classification,novel AI methods are being introduced in traditional Chinese medicine tongue diagnosis medical practices.Traditional tongue diagnose depends on observations of tongue characteristics,such as color,shape,texture,moisture,etc.by traditional Chinese medicine physicians.The appearance of the tongue color,texture and coating reflects the improvement or deterioration of patient’s conditions.Moreover,AI can now distinguish patient’s condition through tongue images,texture or coating,which is all possible increasingly with help from traditional Chinese medicine physicians under the traditional Chinese medicine tongue theory.AI has enabled humans to do what was previously unimagined:traditional Chinese medicine tongue diagnosis with feeding a large amount of tongue image and tongue texture/coating data to train the AI modes.This review focuses on the research advances of AI in TCM tongue diagnosis thus far to identify the major scientific methods and prospects.In this article,we tried to review the AI application in resolving the tongue diagnosis of traditional Chinese medicine on color correction,tongue image extraction,tongue texture/coating segmentation.

A brief overview of traditional Chinese medicine prescription powered by artificial intelligence

Traditional Chinese medicine prescription is one of the treasures of traditional Chinese medicine(TCM).There are tens of thousands TCM prescriptions accumulated in the past thousands of years,corresponding to different diseases,symptoms and therapeutic goals.The correspondences are so complicated that there is an urgent need to leverage new technologies such as artificial intelligence(AI)to analyze,understand and utilize them effectively.In this paper,we present a brief overview of this direction,where current research progress on TCM prescription powered by AI is summarized.Our summarization focuses on three aspects,TCM prescription mining that aims at understanding the TCM prescription,TCM prescription or herb knowledge base construction that aims at extracting knowledge to support the TCM prescription-related study,and TCM prescription discovery that aims at utilizing AI technologies to further energize TCM.It is encouraging to see that steady progress in this direction has been made recently.Besides,a toy experiment on image-based TCM herb recognition by using convolutional neural networks is also conducted.It basically verifies that it is promising to use AI technologies to address challenging tasks in TCM.We also point out several research topics that could be cooperatively performed by researchers from the two disciplines.

Artificial intelligence for the development and implementation guidelines for traditional Chinese medicine and integrated traditional Chinese and western medicine

The translation and implementation of clinical practice guidelines(CPGs)for Traditional Chinese Medicine(TCM)and Integrated Traditional Chinese and Western medicine is crucial to the adoption of medical science and technology,but the low operability and slow update of integrated traditional Chinese and Western Medicine guidelines,and the lack of integration between guidelines and clinical practice,result in the guidelines not having the desired clinical effects in practice.The application of Artificial Intelligence(AI)to the field of CPGs development aims to shorten the development time,optimize and accelerate the whole process of CPG’s development.This article summarized the current research and application status of AI in development and implementation CPGs for TCM and Integrated Traditional Chinese and Western medicine and proposed the method of Combining real world data and AI technology to enrich for TCM and Integrated Traditional Chinese and Western medicine.

Artificial intelligence and its application for cardiovascular diseases in Chinese medicine

Cardiovascular diseases(CVDs)are major disease burdens with high mortality worldwide.Early prediction of cardiovascular events can reduce the incidence of acute myocardial infarction and decrease the mortality rates of patients with CVDs.The pathological mechanisms and multiple factors involved in CVDs are complex;thus,traditional data analysis is insufficient and inefficient to manage multidimensional data for the risk prediction of CVDs and heart attacks,medical image interpretations,therapeutic decision-making,and disease prognosis prediction.Meanwhile,traditional Chinese medicine(TCM)has been widely used for treating CVDs.TCM offers unique theoretical and practical applications in the diagnosis and treatment of CVDs.Big data have been generated to investigate the scientific basis of TCM diagnostic methods.TCM formulae contain multiple herbal items.Elucidating the complicated interactions between the active compounds and network modulations requires advanced data-analysis capability.Recent progress in artificial intelligence(AI)technology has allowed these challenges to be resolved,which significantly facilitates the development of integrative diagnostic and therapeutic strategies for CVDs and the understanding of the therapeutic principles of TCM formulae.Herein,we briefly introduce the basic concept and current progress of AI and machine learning(ML)technology,and summarize the applications of advanced AI and ML for the diagnosis and treatment of CVDs.Furthermore,we review the progress of AI and ML technology for investigating the scientific basis of TCM diagnosis and treatment for CVDs.We expect the application of AI and ML technology to promote synergy between western medicine and TCM,which can then boost the development of integrative medicine for the diagnosis and treatment of CVDs.

Network pharmacology for traditional Chinese medicine in era of artificial intelligence

Traditional Chinese Medicine Network Pharmacology(TCM-NP) is an interdisciplinary discipline that integrates information science, systems biology, network science and pharmacology, providing a systematic research methodology for TCM studies. With the development of artificial intelligence(AI)and multi-omics technologies, TCM-NP has entered a new era and can incorporate multimodal and high-dimensional data in the context of big data to enhance both theoretical foundations and technical capabilities. Despite its advancement, TCM-NP still faces challenges, particularly in ensuring the quality of data and research, as well as achieving more profound scientific discoveries. The field needs further innovation to obtain more precise and biomedically meaningful results. Overall research progress in TCM-NP depends on developing more accurate algorithms together with utilizing higher-quality and larger-scale data. This paper gives a perspective on the trends and characteristics of TCM-NP development and application in the era of AI.

Deep learning-based recognition of stained tongue coating images

Objective To build a dataset encompassing a large number of stained tongue coating images and process it using deep learning to automatically recognize stained tongue coating images.Methods A total of 1001 images of stained tongue coating from healthy students at Hunan University of Chinese Medicine and 1007 images of pathological(non-stained)tongue coat-ing from hospitalized patients at The First Hospital of Hunan University of Chinese Medicine withlungcancer;diabetes;andhypertensionwerecollected.Thetongueimageswererandomi-zed into the training;validation;and testing datasets in a 7:2:1 ratio.A deep learning model was constructed using the ResNet50 for recognizing stained tongue coating in the training and validation datasets.The training period was 90 epochs.The model’s performance was evaluated by its accuracy;loss curve;recall;F1 score;confusion matrix;receiver operating characteristic(ROC)curve;and precision-recall(PR)curve in the tasks of predicting stained tongue coating images in the testing dataset.The accuracy of the deep learning model was compared with that of attending physicians of traditional Chinese medicine(TCM).Results The training results showed that after 90 epochs;the model presented an excellent classification performance.The loss curve and accuracy were stable;showing no signs of overfitting.The model achieved an accuracy;recall;and F1 score of 92%;91%;and 92%;re-spectively.The confusion matrix revealed an accuracy of 92%for the model and 69%for TCM practitioners.The areas under the ROC and PR curves were 0.97 and 0.95;respectively.Conclusion The deep learning model constructed using ResNet50 can effectively recognize stained coating images with greater accuracy than visual inspection of TCM practitioners.This model has the potential to assist doctors in identifying false tongue coating and prevent-ing misdiagnosis.

Translational analysis of data science and causal learning in real-world clinical evaluation of traditional Chinese medicine

Real-world clinical evaluation of traditional Chinese medicine(RWCE-TCM)is a method for comprehensively evaluating the clinical effects of TCM,with the aim of delving into the causality between TCM intervention and clinical outcomes.The study explored data science and causal learning methods to transform RWD into reliable real-world evidence,aiming to provide an innovative approach for RWCE-TCM.This study proposes a 10-step data science methodology to address the challenges posed by diverse and complex data in RWCE-TCM.The methodology involves several key steps,including data integration and warehouse building,high-dimensional feature selection,the use of interpretable statistical machine learning algorithms,complex networks,and graph network analysis,knowledge mining techniques such as natural language processing and machine learning,observational study design,and the application of artificial intelligence tools to build an intelligent engine for translational analysis.The goal is to establish a method for clinical positioning,applicable population screening,and mining the structural association of TCM characteristic therapies.In addition,the study adopts the principle of real-world research and a causal learning method for TCM clinical data.We constructed a multidimensional clinical knowledge map of“disease-syndrome-symptom-prescription-medicine”to enhance our understanding of the diagnosis and treatment laws of TCM,clarify the unique therapies,and explore information conducive to individualized treatment.The causal inference process of observational data can address confounding bias and reduce individual heterogeneity,promoting the transformation of TCM RWD into reliable clinical evidence.Intelligent data science improves efficiency and accuracy for implementing RWCE-TCM.The proposed data science methodology for TCM can handle complex data,ensure high-quality RWD acquisition and analysis,and provide in-depth insights into clinical benefits of TCM.This method supports the intelligent translation and demonstration of RWD in TCM,leads the data-driven translational analysis of causal learning,and innovates the path of RWCE-TCM.

人工智能信息技术在中医四诊现代化研究中的应用现状与展望

随着人工智能信息技术爆发式发展,其在中医领域的应用也逐渐普及。随着现代信息技术的发展,中医诊断学中的望、闻、问、切四诊技术逐渐客观化、标准化、信息化与智能化。借助人工智能、大数据、智能医疗器械等关键要素,提升中医四诊融合及诊断水平,并为中医学的发展注入新的活力,是中医诊断学尤其是四诊技术发展的必然趋势。梳理了近年来中医望诊、闻诊、问诊和切诊的智能化发展及四诊融合概况,以期为中医诊断学及中医现代化的发展提供必要的信息化与智能化发展思路。

基于生命周期评价的中药提取物碳足迹核算研究:以某企业的桑叶提取物为例

在“双碳”目标背景下,生态环境部会同国家15部委制定了《关于建立碳足迹管理体系的实施方案》,中药制药企业开展相关产品的碳足迹核算方法研究迫在眉睫。该研究基于生命周期评价理论,以某企业生产的桑叶提取物为例,分析中药提取物从“从摇篮到大门”的碳足迹。研究结果表明,每生产1 kg产品,原材料获取阶段、运输阶段及提取物生产阶段的碳排放分别为-20.569、1.205、173.577 kgCO_(2)eq(二氧化碳当量),产品最终碳足迹为154.213 kgCO_(2)eq·kg^(-1)。生产阶段碳排放量最高,其中,乙醇溶剂的消耗对碳足迹贡献最大,占比25.71%,超四分之一;其次是中药渣处理过程,占比19.67%;其后是废水处理、热蒸汽及饮用水的消耗,占比分别为17.71%、17.43%及16.90%;电力消耗和包装材料的使用碳排放量相对较小,仅为2.58%,特别是包装材料的消耗,仅0.04%,可忽略不计。研究结果有望为中药企业开展产品碳足迹研究提供方法参考,为中药全产业链减污降碳协同创新提供思路,发展基于绿色低碳制造的现代中药产业新质生产力。

基于卷积神经网络建立中药材自动识别的人工智能模型及应用程序

背景传统中药材检测手段依赖主观经验,难以满足中药材在准确分类与鉴别方面的需求。目的基于卷积神经网络(CNN)开发一款能够自动识别163种中药材的人工智能模型及电脑端应用程序。方法2020年1月—2024年6月,采集了两个中药材数据集进行深度学习模型的训练、验证和测试,共包含163种中药材。通过准确率、灵敏度、特异度、精确率、受试者工作特征(ROC)曲线下面积(AUC)、F1分数等指标来衡量CNN模型的性能。在模型训练完成后,基于PyQt5技术开发了一款应用程序,供临床便携使用。结果本研究共纳入了276767张图像,开发了EfficientNetB0、ResNet50、MobileNetV3、VGG19和ResNet185种模型,通过性能比较,EfficientNet_B0模型在验证集上取得了最高的准确率(99.0%)和AUC(0.9942),被选为最佳模型。在测试集上,最佳模型对所有中药类别识别的准确率为99.0%、灵敏度为99.0%、特异度为100.0%、AUC为1.0,展现出良好的性能。结论基于卷积神经网络开发的深度学习模型能够快速准确地识别163种中药材,借助其高灵敏度的识别能力,为医师对中药材的鉴别提供有力辅助。

基于电发热技术的艾灸功能针织服装设计研究

以缓解白领人群因久坐导致的肩颈疼痛、腰部疲劳为出发点,基于中医穴位艾灸疗法,分析中医艾灸对缓解人体疲劳的作用及应用原理,设计开发集理疗、时尚为一体的智能电发热服装,包括一款缓解腰部疼痛的针织连衣裙和缓解肩颈疼痛的针织上衣。详细阐述设计思路及功能原理、艾灸功能模块设计、服装结构设计,并对成衣的加热功能性及穿着舒适性进行测试评价。结果表明,设计制作的服装理疗效果显著,可有效缓解人体肩颈部及腰部疲劳疼痛,且兼具功能性与时尚性。

人工智能辅助教学模式在中医院校影像教学脑出血评估中的应用

目的评估人工智能(artificial intelligence,AI)辅助教学模式在中医院校影像教学中脑出血中应用的效果。方法选取2021年1月—2022年12月在南京中医药大学附属医院放射科实习的60名本科生,按照随机数字表法分为试验组与对照组,每组30名。对照组采用传统教学,试验组采用“颅内出血CT影像辅助分诊软件”辅助教学。通过阅片考试和问卷调查,对比实习结束后2组学生的阅片技能、影像分析及教学满意度。结果试验组的诊断报告质量成绩[(91.00±0.73)分]高于对照组[(83.90±2.25)分],诊断报告书写时间[(6.41±1.08)min]短于对照组[(12.39±1.47)min],差异有统计学意义(P<0.001)。试验组影像分析成绩均高于对照组,差异有统计学意义(P<0.001)。试验组的教学满意度高于对照组,差异有统计学意义(P<0.05)。结论AI辅助教学模式,不仅有助于提升学生的综合能力,同时也在学生的影像思维能力的培养上发挥着重要作用,也有利于提高教学的质量和效率。

“智能+中医药”实验软件环境多级隔离部署方案研究

随着“智能+中医药”复合专业人才培养工作的推进,支撑课程体系的实验软件环境愈加复杂,部署冲突的异构软件成为实验室建设过程中亟待解决的问题。在梳理课程实验项目具体要求的基础上,提出了结合IDV云桌面技术、Docker容器技术和Python虚拟环境的多级隔离部署方案。实践证明,该方案可以很好地解决软件环境异构冲突问题。

数智赋能中医药新质生产力形成:内在机制与实践路径

加快发展新质生产力是我国在新发展阶段具有统领性的目标遵循,中医药产业作为大健康产业的重要组成部分,加快培育新质生产力是时代要求与内在发展动力。我国中医药健康服务在创新能力、体系建设、服务模式、治理水平等方面存在短板,数智技术可以在创新范式、资源整合、治理手段等方面赋能,助力中医药新质生产力的加快形成。基于此,亟需在创新数智化、系统功能协同、新质要素培育、产业智慧发展生态等方面发力,为数智赋能加快形成中医药新质生产力提供良好的发展环境。