Thoughts on the Design and Development of Artificial Intelligence for Traditional Chinese Medicine Clinical Decision Making in Community Hospitals

Xiaoqing Zhang

doi:doi:10.11648/j.sd.20261402.11

Research Article |

| Peer-Reviewed

Thoughts on the Design and Development of Artificial Intelligence for Traditional Chinese Medicine Clinical Decision Making in Community Hospitals

Xiaoqing Zhang^*

Published in Science Discovery (Volume 14, Issue 2)

Received: 26 February 2026 Accepted: 26 March 2026 Published: 13 April 2026

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Abstract

The rapid development of artificial intelligence (AI) technology has opened up a new path for the development of community traditional Chinese medicine (TCM) diagnosis and treatment. By introducing intelligent auxiliary decision-making systems, the clinical diagnosis and treatment capabilities of grassroots TCM practitioners can be effectively enhanced, making up for their relatively insufficient experience. However, at present, the application of this technology in the TCM field still faces many challenges. Its algorithm models and knowledge systems need to be deeply integrated with the theoretical characteristics of TCM, such as the holistic view and syndrome differentiation and treatment, as well as the flexible and adaptable clinical practice requirements. It is necessary to actively explore feasible integration solutions in the real clinical decision-making process. This article systematically analyzes the current development status of community TCM clinics and the specific application of AI technology in areas such as auxiliary diagnosis and prescription recommendation. It focuses on sorting out the practical problems existing in core links such as the standardization and unification of TCM terms, modeling of syndrome differentiation and treatment processes, screening and compatibility of prescriptions, and dosage and contraindications of drugs. On this basis, it deeply explores how to design and develop a relatively complete and human-machine collaborative intelligent auxiliary decision-making system, and proposes optimization paths from multiple dimensions such as strengthening humanistic care, adhering to medical ethics, and ensuring data security. Finally, it provides systematic solutions for promotion from three aspects: strengthening cross-disciplinary scientific research, promoting the popularization of technology and knowledge education, and improving industry standards and policy guidance, with the aim of providing practical theoretical basis and practical reference for the in-depth empowerment of grassroots TCM services by AI.

Published in	Science Discovery (Volume 14, Issue 2)
DOI	10.11648/j.sd.20261402.11
Page(s)	18-23
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

Artificial Intelligence, Community Diagnosis and Treatment, TCM Outpatient Clinics, Decision Support

1．引言

中医药是我国传统医学的瑰宝

[1]

，有着深厚的理论积淀与丰富的实战经验，在医疗服务中占据着不可替代的地位。社区医院是基层医疗卫生服务的核心载体，直接面对广大居民群众的多元化健康需求，却长期面临中医优质医疗资源短缺、医师诊疗水平参差不齐、名老中医经验传承困难等问题

[2]

。随着人工智能技术的快速迭代更新，其应用在医疗领域的场景不断得以拓展，从最初的辅助影像诊断、新型药物研发，逐步渗透至临床决策等多种核心诊疗环节

[3]

。人工智能技术的融入，为基层中医诊疗提供了新的思路

[4]

。通过搭建智能辅助决策系统，可协助社区中医师完成证候分析、处方选择与方案优化等工作，不仅能提升基层中医师的诊疗水平，还能实现名老中医经验的数字化传承，推动中医药服务向标准化、均质化方向发展。

然而，人工智能与中医诊疗体系的融合，并不是简单的技术拼接。中医诊断过程有着较强的主观性与经验性，强调“整体观念”与“辨证论治”，这就要求人工智能技术开发，需要充分契合中医特有思维特点，同时还需符合临床实际需求。当前，在人机协同诊疗过程中，坚守“以人为本”的核心原则，实现技术与实用的深度融合，仍是亟待解决的问题。本文结合社区中医门诊的发展实际，从数据模型、核心算法、审核机制三个维度，系统探讨人工智能参与中医诊疗决策的技术框架构建路径，关注人文关怀、医学伦理、信息安全等多个数字系统薄弱层面，为该技术在基层中医领域的落地应用，提供综合性的理论参考与实践指导。

2．社区中医门诊现状分析

2.1．社区医院中医门诊现况

当前我国社区医院中医门诊普遍面临突出的结构性矛盾，一方面居民对“简便验廉”的中医药服务需求持续攀升，另一方面社区中医师队伍力量薄弱的问题日益凸显

[2]

。高水平中医师多集中于大型医疗机构，社区中医师临床经验普遍不足，年龄结构相对较低。此外，传统师带徒的经验传承模式覆盖范围有限，且转化效率偏低，名老中医的宝贵临床经验难以系统化下沉至基层

[5]

。在诊疗过程中，由于缺乏统一的量化标准，不同医师对舌象、脉象等关键诊断信息的判断，存在较大主观性差异，直接影响了诊疗效果的稳定性与可重复性。在慢性病管理方面，社区医院尚未搭建起完善的健康数据监测与分析体系

[6]

，不同系统间的数据相互孤立、无法顺畅共享，难以为居民提供个性化健康管理服务，也无法真正落实中医“治未病”理念。这些因素共同导致居民对社区中医药服务的信任度不足，进一步加剧了优质医疗资源向大医院集中的“马太效应”

[7]

。

2.2．人工智能赋能中医现况

近年来，人工智能技术在中医领域的应用取得了显著进展。技术层面，自然语言处理技术（NLP）可实现对中医古籍与临床医案的深度解析，计算机视觉技术推动了舌象、面象诊断的客观化分析，知识图谱技术则为中医辨证体系搭建起结构化表达框架。目前已有多个研究团队研发出中医智能辅助诊疗系统

[8]

，集成了智能问诊、图像采集、证候判断、处方筛选等核心功能。实践应用层面，国内已有部分社区医院试点应用新型智能辅助系统，在一定程度上缓解了社区医师经验不足的压力。通过智能设备采集患者舌脉信息，结合问诊数据为医师提供诊疗参考，提升了日常诊疗效率。但现有人工智能系统还不够完善

[9]

，比如，对复杂证候的辨识精度有限，且生成的结果普遍缺乏大样本、多中心的临床验证，难以实现对名老中医隐性知识的深度挖掘与数能转化

[10]

。要想让人工智能与中医诊疗深度融合，不仅需要突破现阶段技术层面的瓶颈，更要契合中医诊疗规范与基层临床的实际需求。

3．现阶段AI实践问题分析

3.1．术语规范问题

中医历经千年发展，不同学术流派层出不穷，在长期的实践中总结出各具特色的诊疗经验，不乏对同一症状、证候及药物的理解存在显著差异。这也决定了人工智能在中医领域的应用过程中，必定面临术语标准化与数据一致性的难题

[11]

。比如，临床中常出现“腹痛”与“胃痛”、“眩晕”与“头晕”，这类近义、同义术语混用的情况，直接导致智能系统在数据处理过程中，出现不可避免的语义分歧。此外，对于目前的社区医院电子病历系统，尚未建立统一的中医数据录入标准，医师描述舌象、脉象时，多使用主观经验性词汇，缺乏可量化的客观指标，使得系统难以构建稳定、准确的数据关联逻辑。术语不规范的问题，还进一步加大了中医知识图谱的构建难度，导致人工智能系统无法精准梳理“证-法-方-药”

[12]

之间的内在逻辑链条。甚至，还有可能会生成看似合理，实则违背中医理论的结论，直接影响辨证推理的准确性。所以，有必要建立统一的中医诊疗术语数据库，规范中医证候信息编码，为智能系统的搭建，提供结构化、标准化的数据基础。

3.2．辨证论治问题

传统的“望闻问切”四诊信息采集完整性不足，会给人工智能在中医辨证环节带来挑战。现有智能系统大多依赖患者主诉与有限的体征数据录入，难以全面采集中医辨证所需的各种信息，尤其是舌象、脉象等，需要专业设备采集的关键诊断信息

[13]

。临床中如果出现信息缺失，人工智能很有可能单从症状分析，容易将气血两虚体质误判为气郁或阳虚体质。其次，人工智能系统对中医证候的理解多停留在表面，无法实现对病机演变过程的动态把握。中医辨证强调因人、因时、因地的“三因制宜”，而基于大数据训练的系统，很容易机械套用常见诊疗模式而忽略精准化适配。更值得关注的是，当患者症状表现与训练数据中的典型案例存在差异时，现有人工智能系统则表现出不足，往往会给出高置信度但实际错误的判断，这种局限性在面对复杂病例时，极易对临床诊疗形成错误引导

[14]

。

3.3．方剂选择问题

在实际诊疗中遇到方剂选择环节，现有智能系统常常会表现出明显的机械性特征。人工智能系统生成的方剂多为高频经典方剂，缺乏临床诊疗所需的精炼性与针对性。比如，针对脾胃不适的患者，人工智能系统可能会给出融合多个经典方剂元素的复杂方案，形成“博杂而不精”的问题。其核心原因是，人工智能系统更倾向于覆盖所有零散症状，却无法精准识别患者的核心病机。另一方面，面对不同人群，人工智能系统也时常难以实现真正的个体化用药。中医临床强调随证加减、灵活化裁，但人工智能系统在药物剂量调整与配伍变化上表现僵硬，比如，针对子宫脱垂患者，人工智能系统推荐补中益气汤作为基础方，却无法根据患者的具体舌苔脉象，灵活加减药味或调整核心药物用量。此外，人工智能系统还会对古今方剂的应用场景区分不足，常将适配古代人群体质的经方，机械套用于现代生活方式下的患者群体。

3.4．药物使用问题

药物使用环节的问题，主要集中在剂量调整机械化，以及炮制规格缺失，两个方面。针对特殊人群、特殊体质患者，现有人工智能系统通常遵循药典常用剂量，缺乏灵活化、个体化调整方案。比如，调理脾胃的方剂中，白术的剂量常被设定为10～15g，但在临床中医师需要根据患者的体质差异，动态调整为30g甚至更大剂量

[15]

。更关键的是，人工智能系统普遍忽视了药材炮制规格的差异化，而这一点直接影响中药药效的发挥，生白术与炒白术的功效侧重差异显著

[16]

，但现有人工智能系统大多无法基于辨证结果做出准确区分与推荐。此外，人工智能系统对药物相互作用与配伍禁忌的分析理解不够全面，虽然内置了十八反、十九畏等用药规则，但对慢病多药联用场景下的复杂配伍禁忌，缺乏精准的判断能力。针对孕妇、儿童、肝肾功能不全等特殊群体，人工智能系统的知识库也难以覆盖全面的安全用药规则。这些短板，都直接制约着人工智能系统在临床实践中的可靠性与安全性。

4．AI决策系统框架构建

4.1．数据模型构建

高质量、标准化的数据模型，是智能辅助决策系统的基础支撑

[17]

。在智能系统还不够完善的现阶段，需要建立一个统一的中医诊疗数据库，全面规范术语体系、数据结构与接口协议，通过制定标准化的社区中医电子病历规范，打通不同机构间的数据壁垒，确保不同渠道采集的数据具备可比性与共享性。数据采集环节，需整合多维度诊疗信息，包括医师记录的症状体征文本数据，智能设备采集的舌象、脉象等量化数据，以及居民健康档案中的历史诊疗信息。针对中医知识的体系化特征，需搭建结构化的中医知识图谱，将证候、治法、方药等核心要素，及其内在的“证-法-方-药”

[12]

逻辑关系进行数字化串联。尤其要重视名老中医经验的传承，通过深度解析经典医案资料，提取其中的隐性诊疗知识，并进一步融入知识图谱。考虑到医疗数据的高敏感性，需采用隐私计算技术，在保障数据安全与患者隐私的前提下，实现多机构协同建模，突破社区医院单机构数据量不足的难题，避免形成数据孤岛。

4.2．核心算法构建

算法设计需要深度契合中医辨证论治的核心诊疗逻辑，避免简单而机械地套用通用模型。辨证算法需突破简单的症状与证候对应模式，引入病机演变动态模型，模拟中医整体观念下的动态辩证思维。对于处方生成算法，则需将经典方剂理论与现代临床数据深度融合，建立多层级推荐机制，先基于辨证结果推荐核心基础方剂，再根据患者的个体特征进行加减化裁，最终结合药物知识库完成用药安全校验。为提升算法的临床实用性，需强化多维度信息的融合分析能力，比如将舌象的图像特征与问诊的文本特征进行深度关联分析。模型训练方面，可借助计算机迁移学习技术，先在大规模经典医案数据集上完成基础训练，再结合社区诊疗的实际需求进行针对性优化，以此解决社区医院数据量不足的问题。同时，算法设计需注重可解释性，完整呈现清晰的推理路径与决策依据，帮助医师准确理解人工智能系统的判断逻辑，规避算法偏见带来的临床决策风险。

4.3．审核机制构建

完善的审核机制，是保障智能系统安全可靠运行的核心防线。人工智能决策系统需建立多层级审核流程，涵盖自动审核与人工审核两个环节。自动审核基于中医药理论知识库与用药安全规则库，对系统生成的处方进行自动化校验，识别潜在的配伍禁忌与用药风险。针对置信度较低、方证不对应的输出结果，人工智能系统需明确标注不确定性，并进行风险提示，避免出现“AI幻觉”

[18]

。人工审核环节需设计便捷的交互界面，方便医师查看人工智能系统完整的推理过程，同时对比不同诊疗方案，并可直接修改与记录。实行算法持续优化的反馈闭环机制，将医师的审核修改意见、临床诊疗结局作为优化样本，推动模型的持续迭代升级。此外，有必要明确人机协同的分工边界，系统仅负责提供循证参考与数据支持，执业医师始终保留最终诊疗决策权，通过规范的决策流程设计，确保医师在使用智能辅助工具时，始终保持中医临床思维的主导地位。

5．AI技术优化与提升

5.1．人文关怀

人工智能参与辅助诊疗，需要坚守并传承中医的人文关怀特色

[19]

。人工智能系统设计中，要全面体现中医“整体观念”，不仅要关注患者的当前症状，更要整合其体质特征、生活环境、社会心理因素等全维度信息，构建完整的患者画像，帮助医师全面把握患者的整体健康状况。交互设计上，要摒弃冰冷的技术化输出，建立友好的界面设计，以及通俗易懂的内容解读，增强医患沟通效果，避免机械堆砌专业术语。比如，人工智能系统可自动生成患者易于理解的健康指导建议，辅助医师开展健康教育工作。针对社区常见的的慢性病、多发病，人工智能系统可深度融入中医“治未病”理念，为患者提供个性化的生活方式指导，包含饮食养护、情志调节等全周期健康管理，充分体现中医“防重于治”的核心思想。这些设计的融入，能够让人工智能成为优化医患关系、提升诊疗温度的重要载体，而非医患沟通的壁垒。

5.2．医学伦理

人工智能在中医临床的应用，需要严格遵循医学伦理核心准则

[4]

。明确诊疗责任主体至关重要，无论人工智能系统提供何种参考建议，最终的诊疗决策责任始终由执业医师全部承担，这就要求系统具备完整的操作日志记录功能，可清晰还原系统的计算内容与医师的决策过程，实现诊疗全流程可追溯、可核查。算法设计上需注重公平性，避免因训练数据偏差导致对不同性别、年龄、地域、体质人群的诊疗判断失衡，保障不同群体都能获得均等化的智能诊疗服务。同时，患者的知情同意权必须得到充分保障，在使用智能辅助诊疗前，需向患者充分说明技术应用方式、数据使用范围与隐私保护措施，获得患者的知情同意。伦理建设的重点内容是对患者医疗数据的隐私保护，有必要建立分级分类、权责清晰的数据访问权限管理制度，严防患者隐私数据泄露。此外，为减少技术依赖风险，可以通过系统化培训与规范化管理，来确保医师始终保持独立的中医临床判断能力，避免因过度依赖智能系统导致临床诊疗能力退化。

5.3．信息安全

医疗数据安全是中医智能辅助决策系统建设的生命线。这个原则应该覆盖数据采集、传输、存储、应用、共享的全周期，全面落实数据传输加密、存储加密、分级访问限制等核心安全措施。尤其要重视患者隐私保护，对患者个人信息进行严格的脱敏处理，从源头降低隐私泄露风险。人工智能系统架构设计需严格遵循“最小权限”原则，为不同角色的用户分配仅满足工作必需的最小数据访问权限，杜绝越权访问行为。对于跨机构数据共享环节，在不交换原始医疗数据的前提下，实现数据顺捷共享的同时，采用隐私加密技术，兼顾数据应用与隐私安全。在此过程中，需要建立完善的安全审计机制，对所有数据访问、系统操作行为进行全量记录与实时监控，及时发现异常操作并及时弹窗警报。另外，需制定完善的应急预案，确保在系统故障、数据泄露等突发状况下，人工智能系统能够快速响应，并且有效处置。还需定期开展安全风险评估，及时发现并修复系统安全漏洞，保障系统长期稳定且安全地运行。

6．综合性应对及引导

6.1．科研攻关

持续加大科研投入力度，可阻力突破中医人工智能关键技术瓶颈。科研攻关项目需进一步聚焦于，中医多维度诊疗信息融合、隐性知识的量化表达、可解释性算法优化、小样本场景下的技术适配等核心难题。建议聚集中医药、计算机、医学伦理等多学科人才，设立国家级中医人工智能重点实验室，共同开展联合攻关，重点突破中医特色鲜明的研究方向，比如脉象信号的动态量化处理、舌象特征的动态分析、中医辨证思维的数字化模拟等。鼓励医疗机构与科技企业基于社区中医临床真实数据开展技术创新与突破，积极开展深度“生产-学习-研发”合作，推动科研成果快速向临床应用转化。模型研发层面，需探索适配中医理论体系与诊疗逻辑的专用架构。同时，通过严谨规范的多中心临床试验，以及增加中医“真实世界”验证研究，全面提升智能系统临床运用的有效性与安全性。建立开放共享的研发平台与标准化数据集，促进行业内的学术交流与协同发展。

6.2．教育普及

复合型人才队伍建设，是未来中医人工智能可持续发展的核心保障

[20]

。建议在中医药院校系统开设人工智能相关学科，构建“中医+科技”的复合型人才培养体系，培育既精通中医辨证论治理论与临床技能，又掌握智能技术应用能力的专业人才。同时，需加强对社区在职中医师的系统化培训，全面提升其“数字素养”与智能工具的使用能力。培训内容不仅要涵盖技术实操方法，更要重点树立正确的人机协同诊疗理念，始终保持中医临床思维的主导性，规避技术依赖风险。结合技术发展与临床需求，进一步建立常态化继续教育体系，定期更新医师的知识结构与技能体系。针对技术研发人员，需组织系统化的中医基础理论培训，帮助其深入理解中医诊疗的核心逻辑与特色需求，避免技术研发与临床实际脱节。在社区层面，可面向居民常态化开展“智慧中医”科普活动

[21]

，提高公众对智能辅助中医诊疗的认知度与接受度。建议编制统一的培训教材与标准化操作指南，建立师资队伍认证标准，全面保障教学质量与培训效果。

6.3．政策引导

中医人工智能行业健康有序发展，同样离不开完善的政策导向。建议由国家中医药主管部门牵头，联合医疗、药监、医保等相关部门，制定国家级中医人工智能发展专项规划与全流程标准体系，明确智能辅助诊疗系统的研发规范、准入要求与监管规则，建立适配中医诊疗特点的审评审批机制。医保支付层面积极探索，逐步将经过临床验证、安全有效的智能辅助诊疗服务纳入医保报销范畴

[22]

。建立科学合理的激励机制，降低基层医疗机构的应用门槛。鼓励各地方结合区域医疗特点开展政策试点，打造区域性的社区中医智能辅助诊疗示范应用场景。明确各相关部门的职责分工，加强跨部门协调联动，形成监管合力的长效机制

[23]

。结合技术发展与临床应用实际情况，建立动态化的行业监管，及时调整优化监管政策与行业标准。同时需完善相关法律法规，明确智能系统生成内容的法律地位，清晰界定研发机构、医疗机构、执业医师等各方的权责边界，制定完善的行业规范，引导相关企事业单位规范研发、合规应用，推动行业健康可持续地发展。

7．总结与展望

人工智能技术的发展，为社区医院中医门诊的高质量发展带来了全新机遇。通过构建完善的技术框架与审核体制，标准化数据集成平台、智能化辅助诊疗优化，全流程规范化质控流程，在未来几年内，人工智能技术能够迅速而有效地提升基层中医药服务的效率与质量，让优质中医诊疗服务更精准、更可及。随着技术的持续迭代，以及应用场景的不断深化，人工智能将从更深层面融入中医诊疗全流程，渗透“证-法-方-药”各个阶段，成为基层中医师的得力助手，全面提升中医诊疗的同质化水平与服务能力。当前行业发展的核心重点，是突破自然语言处理、知识图谱构建、多维度数据融合等核心技术瓶颈，同时完善算法审核、临床验证与全流程审查机制。行业发展的最终目标，是构建智能化、人性化、可信化的社区中医药服务新模式，全面覆盖预防、保健、医疗、康复、健康教育及计划生育技术指导“六位一体”的基层医疗，更好地满足人民群众多层次、多样化的健康需求。坚持中西医并重、融合发展的基本原则，结合科研创新、教育普及、政策支持的三方联动，构建协同发展的长效机制，加强中医与人工智能复合型人才的培养，让现代科技为传统中医药注入全新的发展活力，推动中医药事业的传承发展与技术革新。这需要中医界、科技界、产业界等社会各界的广泛参与与长期协同努力。

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Zhang, X. (2026). Thoughts on the Design and Development of Artificial Intelligence for Traditional Chinese Medicine Clinical Decision Making in Community Hospitals. Science Discovery, 14(2), 18-23. https://doi.org/10.11648/j.sd.20261402.11

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Zhang, X. Thoughts on the Design and Development of Artificial Intelligence for Traditional Chinese Medicine Clinical Decision Making in Community Hospitals. Sci. Discov. 2026, 14(2), 18-23. doi: 10.11648/j.sd.20261402.11

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Zhang X. Thoughts on the Design and Development of Artificial Intelligence for Traditional Chinese Medicine Clinical Decision Making in Community Hospitals. Sci Discov. 2026;14(2):18-23. doi: 10.11648/j.sd.20261402.11

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@article{10.11648/j.sd.20261402.11,
  author = {Xiaoqing Zhang},
  title = {Thoughts on the Design and Development of Artificial Intelligence for Traditional Chinese Medicine Clinical Decision Making in Community Hospitals},
  journal = {Science Discovery},
  volume = {14},
  number = {2},
  pages = {18-23},
  doi = {10.11648/j.sd.20261402.11},
  url = {https://doi.org/10.11648/j.sd.20261402.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20261402.11},
  abstract = {The rapid development of artificial intelligence (AI) technology has opened up a new path for the development of community traditional Chinese medicine (TCM) diagnosis and treatment. By introducing intelligent auxiliary decision-making systems, the clinical diagnosis and treatment capabilities of grassroots TCM practitioners can be effectively enhanced, making up for their relatively insufficient experience. However, at present, the application of this technology in the TCM field still faces many challenges. Its algorithm models and knowledge systems need to be deeply integrated with the theoretical characteristics of TCM, such as the holistic view and syndrome differentiation and treatment, as well as the flexible and adaptable clinical practice requirements. It is necessary to actively explore feasible integration solutions in the real clinical decision-making process. This article systematically analyzes the current development status of community TCM clinics and the specific application of AI technology in areas such as auxiliary diagnosis and prescription recommendation. It focuses on sorting out the practical problems existing in core links such as the standardization and unification of TCM terms, modeling of syndrome differentiation and treatment processes, screening and compatibility of prescriptions, and dosage and contraindications of drugs. On this basis, it deeply explores how to design and develop a relatively complete and human-machine collaborative intelligent auxiliary decision-making system, and proposes optimization paths from multiple dimensions such as strengthening humanistic care, adhering to medical ethics, and ensuring data security. Finally, it provides systematic solutions for promotion from three aspects: strengthening cross-disciplinary scientific research, promoting the popularization of technology and knowledge education, and improving industry standards and policy guidance, with the aim of providing practical theoretical basis and practical reference for the in-depth empowerment of grassroots TCM services by AI.},
 year = {2026}
}

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TY  - JOUR
T1  - Thoughts on the Design and Development of Artificial Intelligence for Traditional Chinese Medicine Clinical Decision Making in Community Hospitals
AU  - Xiaoqing Zhang
Y1  - 2026/04/13
PY  - 2026
N1  - https://doi.org/10.11648/j.sd.20261402.11
DO  - 10.11648/j.sd.20261402.11
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 18
EP  - 23
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20261402.11
AB  - The rapid development of artificial intelligence (AI) technology has opened up a new path for the development of community traditional Chinese medicine (TCM) diagnosis and treatment. By introducing intelligent auxiliary decision-making systems, the clinical diagnosis and treatment capabilities of grassroots TCM practitioners can be effectively enhanced, making up for their relatively insufficient experience. However, at present, the application of this technology in the TCM field still faces many challenges. Its algorithm models and knowledge systems need to be deeply integrated with the theoretical characteristics of TCM, such as the holistic view and syndrome differentiation and treatment, as well as the flexible and adaptable clinical practice requirements. It is necessary to actively explore feasible integration solutions in the real clinical decision-making process. This article systematically analyzes the current development status of community TCM clinics and the specific application of AI technology in areas such as auxiliary diagnosis and prescription recommendation. It focuses on sorting out the practical problems existing in core links such as the standardization and unification of TCM terms, modeling of syndrome differentiation and treatment processes, screening and compatibility of prescriptions, and dosage and contraindications of drugs. On this basis, it deeply explores how to design and develop a relatively complete and human-machine collaborative intelligent auxiliary decision-making system, and proposes optimization paths from multiple dimensions such as strengthening humanistic care, adhering to medical ethics, and ensuring data security. Finally, it provides systematic solutions for promotion from three aspects: strengthening cross-disciplinary scientific research, promoting the popularization of technology and knowledge education, and improving industry standards and policy guidance, with the aim of providing practical theoretical basis and practical reference for the in-depth empowerment of grassroots TCM services by AI.
VL  - 14
IS  - 2
ER  -

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Xiaoqing Zhang

Department of General Practice, Tie Xinqiao Community Health Service Center, Nanjing City, China

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http://orcid.org/0009-0008-4655-1349

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Zhang, X. (2026). Thoughts on the Design and Development of Artificial Intelligence for Traditional Chinese Medicine Clinical Decision Making in Community Hospitals. Science Discovery, 14(2), 18-23. https://doi.org/10.11648/j.sd.20261402.11

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ACS Style

Zhang, X. Thoughts on the Design and Development of Artificial Intelligence for Traditional Chinese Medicine Clinical Decision Making in Community Hospitals. Sci. Discov. 2026, 14(2), 18-23. doi: 10.11648/j.sd.20261402.11

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Zhang X. Thoughts on the Design and Development of Artificial Intelligence for Traditional Chinese Medicine Clinical Decision Making in Community Hospitals. Sci Discov. 2026;14(2):18-23. doi: 10.11648/j.sd.20261402.11

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@article{10.11648/j.sd.20261402.11,
  author = {Xiaoqing Zhang},
  title = {Thoughts on the Design and Development of Artificial Intelligence for Traditional Chinese Medicine Clinical Decision Making in Community Hospitals},
  journal = {Science Discovery},
  volume = {14},
  number = {2},
  pages = {18-23},
  doi = {10.11648/j.sd.20261402.11},
  url = {https://doi.org/10.11648/j.sd.20261402.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20261402.11},
  abstract = {The rapid development of artificial intelligence (AI) technology has opened up a new path for the development of community traditional Chinese medicine (TCM) diagnosis and treatment. By introducing intelligent auxiliary decision-making systems, the clinical diagnosis and treatment capabilities of grassroots TCM practitioners can be effectively enhanced, making up for their relatively insufficient experience. However, at present, the application of this technology in the TCM field still faces many challenges. Its algorithm models and knowledge systems need to be deeply integrated with the theoretical characteristics of TCM, such as the holistic view and syndrome differentiation and treatment, as well as the flexible and adaptable clinical practice requirements. It is necessary to actively explore feasible integration solutions in the real clinical decision-making process. This article systematically analyzes the current development status of community TCM clinics and the specific application of AI technology in areas such as auxiliary diagnosis and prescription recommendation. It focuses on sorting out the practical problems existing in core links such as the standardization and unification of TCM terms, modeling of syndrome differentiation and treatment processes, screening and compatibility of prescriptions, and dosage and contraindications of drugs. On this basis, it deeply explores how to design and develop a relatively complete and human-machine collaborative intelligent auxiliary decision-making system, and proposes optimization paths from multiple dimensions such as strengthening humanistic care, adhering to medical ethics, and ensuring data security. Finally, it provides systematic solutions for promotion from three aspects: strengthening cross-disciplinary scientific research, promoting the popularization of technology and knowledge education, and improving industry standards and policy guidance, with the aim of providing practical theoretical basis and practical reference for the in-depth empowerment of grassroots TCM services by AI.},
 year = {2026}
}

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TY  - JOUR
T1  - Thoughts on the Design and Development of Artificial Intelligence for Traditional Chinese Medicine Clinical Decision Making in Community Hospitals
AU  - Xiaoqing Zhang
Y1  - 2026/04/13
PY  - 2026
N1  - https://doi.org/10.11648/j.sd.20261402.11
DO  - 10.11648/j.sd.20261402.11
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 18
EP  - 23
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20261402.11
AB  - The rapid development of artificial intelligence (AI) technology has opened up a new path for the development of community traditional Chinese medicine (TCM) diagnosis and treatment. By introducing intelligent auxiliary decision-making systems, the clinical diagnosis and treatment capabilities of grassroots TCM practitioners can be effectively enhanced, making up for their relatively insufficient experience. However, at present, the application of this technology in the TCM field still faces many challenges. Its algorithm models and knowledge systems need to be deeply integrated with the theoretical characteristics of TCM, such as the holistic view and syndrome differentiation and treatment, as well as the flexible and adaptable clinical practice requirements. It is necessary to actively explore feasible integration solutions in the real clinical decision-making process. This article systematically analyzes the current development status of community TCM clinics and the specific application of AI technology in areas such as auxiliary diagnosis and prescription recommendation. It focuses on sorting out the practical problems existing in core links such as the standardization and unification of TCM terms, modeling of syndrome differentiation and treatment processes, screening and compatibility of prescriptions, and dosage and contraindications of drugs. On this basis, it deeply explores how to design and develop a relatively complete and human-machine collaborative intelligent auxiliary decision-making system, and proposes optimization paths from multiple dimensions such as strengthening humanistic care, adhering to medical ethics, and ensuring data security. Finally, it provides systematic solutions for promotion from three aspects: strengthening cross-disciplinary scientific research, promoting the popularization of technology and knowledge education, and improving industry standards and policy guidance, with the aim of providing practical theoretical basis and practical reference for the in-depth empowerment of grassroots TCM services by AI.
VL  - 14
IS  - 2
ER  -

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