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Article(id=1322168379520176613, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322167989802217791, articleNumber=null, orderNo=null, doi=10.12102/j.issn.1009-6493.2026.04.001, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1746547200000, receivedDateStr=2025-05-07, revisedDate=1769875200000, revisedDateStr=2026-02-01, acceptedDate=null, acceptedDateStr=null, onlineDate=1793159106378, onlineDateStr=2026-10-28, pubDate=1771948800000, pubDateStr=2026-02-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793159106378, onlineIssueDateStr=2026-10-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793159106378, creator=13701087609, updateTime=1793159106378, updator=13701087609, issue=Issue{id=1322167989802217791, tenantId=1146029695717560320, journalId=1220038315760373764, year='2026', volume='40', issue='4', pageStart='529', pageEnd='704', issueExtLink='null', onlineDate='null', pubDate='1771948800000', pubDateStr='2026-02-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793159013461, creator='13701087609', updateTime=1793159013461, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=529, endPage=535, ext={EN=ArticleExt(id=1322168381223064040, articleId=1322168379520176613, tenantId=1146029695717560320, journalId=1220038315760373764, language=EN, title=Application effectiveness of large language models in health consultation for stroke patients, columnId=1220689484526572183, journalTitle=Chinese Nursing Research, columnName=Original Articles, runingTitle=null, highlight=null, articleAbstract=
Objective

To evaluate the efficacy and application potential of different large language models in health education consultation for stroke patients.

Methods

Using 21 common clinical questions of stroke and 2 sets of typical clinical scenario simulation cases,three rounds of tests were conducted on DeepSeek R1,ERNIE Bot X1,ChatGPT-4o,and Claude 3.7 Sonnet large language models.Three neurology department chief physicians were selected to evaluate the models from five dimensions:accuracy,comprehensiveness,understandability,humanistic care,and case analysis ability.

Results

The overall performance of all models was good.DeepSeek R1 had the highest total score, followed by ChatGPT-4o,Claude 3.7 Sonnet, and ERNIE Bot X1.DeepSeek R1 and ChatGPT-4o performed outstandingly in complex clinical reasoning,evidence-based medical knowledge transmission,humanistic care,and case analysis.ERNIE Bot X1 had an advantage in the adaptability to the Chinese context.

Conclusions

Large language models showed positive auxiliary application value in the field of stroke health education.However,the clinical review mechanism needs to be combined to enhance the reliability of the information.The clinical application of these models could be promoted by optimizing the transparency of the algorithms and the adaptability to the local context.

, authors=Hong CHEN1, Tong LIU2, Guosong LI2, Yingzhuo MA2, Mingzhao XIAO2, Qinghua ZHAO2, authorsList=Hong CHEN, Tong LIU, Guosong LI, Yingzhuo MA, Mingzhao XIAO, Qinghua ZHAO, authorCompany=null, correspAuthors=Tong LIU, authorNote=null, correspAuthorsNote=
LIU Tong， E-mail：
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目的

评估不同大语言模型在脑卒中病人健康教育咨询中的效能和应用潜力。

方法

采用21个脑卒中常见临床问题与2组典型临床场景模拟案例，对DeepSeek R1、文心一言X1、ChatGPT-4o及Claude 3.7 Sonnet大语言模型进行3轮测试。选取3名神经内科主任医师从准确性、综合性、可理解性、人文关怀度和案例分析能力5个维度进行评估。

结果

各模型总体表现良好，DeepSeek R1总分最高，其次为ChatGPT-4o、Claude 3.7 Sonnet和文心一言X1。DeepSeek R1和ChatGPT-4o在复杂临床推理、循证医学知识传递、人文关怀度和案例分析能力方面表现突出，文心一言X1在中文语境适应性上具有优势。

结论

大语言模型在脑卒中健康教育领域展现出积极的辅助应用价值，但需结合临床审核机制以提升信息可靠性，并通过优化算法透明性及本土化适配性推动其临床应用。

, authors=陈红1, 刘彤2, *, *, 李国松2, 马应卓2, 肖明朝2, 赵庆华2, authorsList=陈红, 刘彤, 李国松, 马应卓, 肖明朝, 赵庆华, authorCompany=null, correspAuthors=刘彤, authorNote=

陈红，副主任护师，本科

, correspAuthorsNote=
刘彤，E-mail：
LIU Tong， E-mail：
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江瑞, 张学工, journalName=医学信息杂志, refType=null, unstructuredReference=闾海荣,江瑞,张学工,等.DeepSeek与医学大语言模型:技术创新与医疗服务模式重构[J].医学信息杂志,2025(2):1-7., articleTitle=DeepSeek与医学大语言模型:技术创新与医疗服务模式重构, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1322530294884843736, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168379520176613, xref=1, ext=[AuthorCompanyExt(id=1322530294893232345, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168379520176613, companyId=1322530294884843736, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1Fengdu County People's Hospital, Chongqing 408200 China), AuthorCompanyExt(id=1322530294897426650, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168379520176613, companyId=1322530294884843736, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1重庆市丰都县人民医院，重庆 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different dimensions, figureFileSmall=Fl/TgNSZYyPeLt+27dAvYg==, figureFileBig=vd2nMkPH1QdwGjC6vISVTQ==, tableContent=null), ArticleFig(id=1322530297409814790, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168379520176613, language=CN, label=图1, caption=4种模型不同维度得分情况

（A为准确性比较；B为综合性比较；C为可理性比较；D为人文关怀度比较）

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（NS为差异无统计学意义）

, figureFileSmall=QY9m90+JQKCGTbe/AbR1dQ==, figureFileBig=e29a0ryjZ57C581KCehZuQ==, tableContent=null), ArticleFig(id=1322530299624407307, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168379520176613, language=EN, label=Figure 4, caption=Comparison of total scores of different models, figureFileSmall=JmsOsV76mQm7ibVwpQARJw==, figureFileBig=YVzKVA2Wy5XB18qTM2MwCQ==, tableContent=null), ArticleFig(id=1322530299888648460, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168379520176613, language=CN, label=图4, caption=不同模型的总分比较

（*P<0.05；***P<0.001；NS为差异无统计学意义）

, figureFileSmall=JmsOsV76mQm7ibVwpQARJw==, figureFileBig=YVzKVA2Wy5XB18qTM2MwCQ==, tableContent=null), ArticleFig(id=1322530299951563021, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168379520176613, language=EN, label=Table 1, caption=

Problems and clinical cases

, figureFileSmall=null, figureFileBig=null, tableContent=
分类编号问题
概述1什么是脑卒中？主要分为哪些类型？
2脑卒中的危险因素有哪些？哪些是可干预的？
3脑卒中是否有遗传倾向？
4脑卒中可能引发哪些严重并发症？
5缺血性脑卒中与出血性脑卒中在病理机制上有何区别？
诊断6如何通过症状快速识别脑卒中（如FAST原则）？
7怀疑脑卒中时，应进行哪些影像学检查？各自的优缺点是什么？
8为什么脑卒中病人需要紧急进行CT或MRI检查？
9如何区分脑卒中与其他类似症状的疾病（如偏头痛、低血糖）？
10脑电图（EEG）在脑卒中诊断中的作用是什么？
治疗11急性缺血性脑卒中病人接受静脉溶栓治疗的黄金时间窗是多久？
12脑卒中后康复训练的最佳开始时间及核心内容是什么？
13长期服用抗血小板药物（如阿司匹林）需要注意哪些副作用？
14出血性脑卒中病人的手术治疗指征有哪些？
15脑卒中病人合并高血压时，血压控制目标应如何设定？
16脑卒中后吞咽困难应如何评估和管理？
17脑卒中病人出现情绪障碍（如抑郁）时，有哪些干预措施？
随访与预防18如何通过饮食调整降低脑卒中复发风险？
19脑卒中病人术后随访需要监测哪些指标？
20有氧运动对脑卒中康复的具体益处是什么？
21脑卒中病人能否驾驶汽车？需满足哪些条件？
案例122急性场景：男，70岁，突发右侧肢体无力、言语不清2 h，血压190/110 mmHg，美国国立卫生研究院卒中量表（NIHSS）评分为12分。头颅CT未见出血。应优先考虑哪种治疗？需排除哪些禁忌证？后续需完善哪些检查？
案例223慢性复杂场景：女，65岁，既往脑梗死病史，近期出现认知功能下降、步态不稳，MRI显示多发腔隙性梗死灶。合并糖尿病和心房颤动，国际标准化比值为1.8。应如何调整抗凝治疗？需关注哪些潜在风险？
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问题和临床案例

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分类编号问题
概述1什么是脑卒中？主要分为哪些类型？
2脑卒中的危险因素有哪些？哪些是可干预的？
3脑卒中是否有遗传倾向？
4脑卒中可能引发哪些严重并发症？
5缺血性脑卒中与出血性脑卒中在病理机制上有何区别？
诊断6如何通过症状快速识别脑卒中（如FAST原则）？
7怀疑脑卒中时，应进行哪些影像学检查？各自的优缺点是什么？
8为什么脑卒中病人需要紧急进行CT或MRI检查？
9如何区分脑卒中与其他类似症状的疾病（如偏头痛、低血糖）？
10脑电图（EEG）在脑卒中诊断中的作用是什么？
治疗11急性缺血性脑卒中病人接受静脉溶栓治疗的黄金时间窗是多久？
12脑卒中后康复训练的最佳开始时间及核心内容是什么？
13长期服用抗血小板药物（如阿司匹林）需要注意哪些副作用？
14出血性脑卒中病人的手术治疗指征有哪些？
15脑卒中病人合并高血压时，血压控制目标应如何设定？
16脑卒中后吞咽困难应如何评估和管理？
17脑卒中病人出现情绪障碍（如抑郁）时，有哪些干预措施？
随访与预防18如何通过饮食调整降低脑卒中复发风险？
19脑卒中病人术后随访需要监测哪些指标？
20有氧运动对脑卒中康复的具体益处是什么？
21脑卒中病人能否驾驶汽车？需满足哪些条件？
案例122急性场景：男，70岁，突发右侧肢体无力、言语不清2 h，血压190/110 mmHg，美国国立卫生研究院卒中量表（NIHSS）评分为12分。头颅CT未见出血。应优先考虑哪种治疗？需排除哪些禁忌证？后续需完善哪些检查？
案例223慢性复杂场景：女，65岁，既往脑梗死病史，近期出现认知功能下降、步态不稳，MRI显示多发腔隙性梗死灶。合并糖尿病和心房颤动，国际标准化比值为1.8。应如何调整抗凝治疗？需关注哪些潜在风险？
), ArticleFig(id=1322530300077392143, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168379520176613, language=EN, label=Table 2, caption=

Expert scores of different models

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模型准确性综合性可理解性人文关怀度案例分析能力总分
DeepSeek R1110106928010398
文心一言X1949184736348
ChatGPT-4o1091041027210397
Claude 3.7 Sonnet10295926910368
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不同模型的专家得分

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模型准确性综合性可理解性人文关怀度案例分析能力总分
DeepSeek R1110106928010398
文心一言X1949184736348
ChatGPT-4o1091041027210397
Claude 3.7 Sonnet10295926910368
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大语言模型在脑卒中病人健康咨询中的应用效能
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陈红 1 , 刘彤 2, *, * , 李国松 2 , 马应卓 2 , 肖明朝 2 , 赵庆华 2
护理研究 | 科研论著 2026,40(4): 529-535
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护理研究 |科研论著 2026 , 40 (4) : 529 -535
大语言模型在脑卒中病人健康咨询中的应用效能
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陈红1, 刘彤2, *, * , 李国松2, 马应卓2, 肖明朝2, 赵庆华2
作者信息
  • 1重庆市丰都县人民医院，重庆 408200
  • 2重庆医科大学附属第一医院
通讯作者:
刘彤，E-mail：
LIU Tong， E-mail：
作者简介:

陈红，副主任护师，本科

Application effectiveness of large language models in health consultation for stroke patients
Hong CHEN1, Tong LIU2 , Guosong LI2, Yingzhuo MA2, Mingzhao XIAO2, Qinghua ZHAO2
Affiliations
  • 1Fengdu County People's Hospital, Chongqing 408200 China
  • 2The First Affiliated Hospital of Chongqing Medical University
    • Corresponding Author:
    LIU Tong， E-mail：
出版时间: 2026-02-25 doi: 10.12102/j.issn.1009-6493.2026.04.001
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摘要
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目的

评估不同大语言模型在脑卒中病人健康教育咨询中的效能和应用潜力。

方法

采用21个脑卒中常见临床问题与2组典型临床场景模拟案例，对DeepSeek R1、文心一言X1、ChatGPT-4o及Claude 3.7 Sonnet大语言模型进行3轮测试。选取3名神经内科主任医师从准确性、综合性、可理解性、人文关怀度和案例分析能力5个维度进行评估。

结果

各模型总体表现良好，DeepSeek R1总分最高，其次为ChatGPT-4o、Claude 3.7 Sonnet和文心一言X1。DeepSeek R1和ChatGPT-4o在复杂临床推理、循证医学知识传递、人文关怀度和案例分析能力方面表现突出，文心一言X1在中文语境适应性上具有优势。

结论

大语言模型在脑卒中健康教育领域展现出积极的辅助应用价值，但需结合临床审核机制以提升信息可靠性，并通过优化算法透明性及本土化适配性推动其临床应用。

大语言模型  /  脑卒中  /  人工智能  /  健康咨询
Objective

To evaluate the efficacy and application potential of different large language models in health education consultation for stroke patients.

Methods

Using 21 common clinical questions of stroke and 2 sets of typical clinical scenario simulation cases,three rounds of tests were conducted on DeepSeek R1,ERNIE Bot X1,ChatGPT-4o,and Claude 3.7 Sonnet large language models.Three neurology department chief physicians were selected to evaluate the models from five dimensions:accuracy,comprehensiveness,understandability,humanistic care,and case analysis ability.

Results

The overall performance of all models was good.DeepSeek R1 had the highest total score, followed by ChatGPT-4o,Claude 3.7 Sonnet, and ERNIE Bot X1.DeepSeek R1 and ChatGPT-4o performed outstandingly in complex clinical reasoning,evidence-based medical knowledge transmission,humanistic care,and case analysis.ERNIE Bot X1 had an advantage in the adaptability to the Chinese context.

Conclusions

Large language models showed positive auxiliary application value in the field of stroke health education.However,the clinical review mechanism needs to be combined to enhance the reliability of the information.The clinical application of these models could be promoted by optimizing the transparency of the algorithms and the adaptability to the local context.

large language models  /  stroke  /  artificial intelligence  /  health consultation
陈红, 刘彤, 李国松, 马应卓, 肖明朝, 赵庆华. 大语言模型在脑卒中病人健康咨询中的应用效能. 护理研究, 2026 , 40 (4) : 529 -535 . DOI: 10.12102/j.issn.1009-6493.2026.04.001
Hong CHEN, Tong LIU, Guosong LI, Yingzhuo MA, Mingzhao XIAO, Qinghua ZHAO. Application effectiveness of large language models in health consultation for stroke patients[J]. Chinese Nursing Research, 2026 , 40 (4) : 529 -535 . DOI: 10.12102/j.issn.1009-6493.2026.04.001
正文
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脑卒中已成为我国成人主要死亡和致残原因，作为一种慢性非传染性疾病，对国民健康构成了严重威胁[1]，其高致残率、高复发率及复杂的并发症管理需求，给病人家庭及社会医疗体系带来了沉重负担[2-4]。研究表明，早期诊断、及时干预和规范化随访是改善脑卒中预后、降低并发症发生率及预防复发的关键[5]。然而，实际诊疗过程中病人往往在疾病预防、诊断方法、治疗方案选择及预后评估等关键决策阶段缺乏可靠的信息支持，这种信息鸿沟可能直接影响治疗效果与病人生存质量。近年来，大语言模型（large language models，LLMs）作为人工智能领域的重要突破，展现出显著的医学应用潜力[6-9]。这类模型基于深度神经网络架构，通过海量文本数据训练，能够执行自然语言生成、问答交互、知识推理等复杂任务。以DeepSeek、ChatGPT为代表的大语言模型已在公共卫生咨询、辅助诊断、治疗建议生成及健康教育等医疗场景中表现出卓越效能[10-11]。然而，现有研究多集中于大语言模型的通用医疗能力评估，在脑卒中病人咨询和教育方面的表现仍未得到探索。不同模型在专业术语理解、临床指南依从性、复杂案例分析等方面的性能差异尚未明确。基于此，本研究拟选取用户基数大、技术成熟度高的4种大语言模型（DeepSeek R1、文心一言X1、ChatGPT-4o及Claude 3.7 Sonnet）进行测试，从回答准确性、综合性、可理解性、人文关怀度和案例分析能力5个维度评估其在脑卒中健康咨询与病人教育中的实际效能，为大语言模型的专科化医疗应用提供循证依据。
1　资料与方法
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1.1　问题和临床案例设计
研究团队由2名医学研究生、1名护理学专家及1名临床医学专家构成。研究前研究团队收集了21个脑卒中相关临床问题，并构建了2个典型临床情景模拟案例。问题的设置基于在线医疗咨询平台的高频查询分析、住院病人调查结果、研究成员及临床医务人员的临床经验。根据问题类型和临床管理阶段，将21个临床问题划分为概述类（涵盖基础概念及病理分型）、诊断类（包含症状识别与鉴别诊断）、治疗类（涉及急性期干预与并发症处理）、随访与预防类（针对预防及康复）4类。概述类问题同时依据临床实践特征，设计了两类差异化情景案例，一是急性脑卒中发作的紧急处置场景，二是既往脑梗死病人并发症管理的慢性期处理方案。完整问题和临床案例见表1。
1.2　模型选择和测试
本研究选取用户基数大、技术成熟度高的4种大语言模型，包括DeepSeek R1、文心一言X1、ChatGPT-4o和Claude 3.7 Sonnet。为减少误差，每个模型测试了3轮。
1.3　评分标准
从回答准确性、综合性、可理解性、人文关怀度和案例分析能力5个维度评估其在脑卒中健康咨询与病人教育中的实际效能。1）准确性：评估应答内容与现行科学证据及临床指南的符合程度。2）综合性：评估应答内容对临床问题或案例相关要素的覆盖广度。3）可理解性：评估应答内容的逻辑结构、语言表述及对目标受众的理解清晰度。4）人文关怀度：评估应答内容对病人的同理心以及解决病人关切、尊重其价值和偏好的能力。5）案例分析能力：评估应答内容在解释案例方案、识别关键问题以及提供解决方案方面的能力水平。采用Likert 5级评分法，1分为不认可，即大语言模型在该维度上的表现明显不足；2分为尚可，即大语言模型在该维度上的表现有一定不足；3分为一般，即大语言模型在该维度上的表现基本符合要求，但并无特别突出之处；4分为良好，即大语言模型在该维度上的表现与标准高度契合；5分为优秀，即大语言模型在该维度上的表现超出标准期望，表现卓越。所有题目总分为470分。
1.4　评分流程
选取3名具有脑卒中专科经验的专家组成评审组，均为主任医师，来自不同三级甲等医院的神经内科，且拥有10年以上的临床经验。为确保评估的客观性和公正性，采用了盲法评估，即评审人员对模型来源信息完全不知情，独立进行评分。在正式评估开始前，对评审组成员进行了1次培训，详细阐述了5个评估维度的说明和定义。为获得对每个模型的全面而准确的评估结果，取3名专家评分的众数或中位数，累加21项基础问题及2项案例分析的各维度得分，得到模型总分。
1.5　统计学方法
采用Hiplot可视化分析平台生成点图和雷达图，可视化比较不同模型的得分特征及性能对比。采用SPSS 26.0软件进行统计分析。符合正态分布的定量资料以均数±标准差（x±s）表示，采用t检验；不符合正态分布的定量资料以中位数（四分位数）[M(P25,P75)]表示，采用Wilcoxon秩和检验。定性资料用频数、百分比（%）表示，采用χ2检验。以P<0.05为差异有统计学意义。
2　结果
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2.1　不同模型的应答表现
DeepSeek R1和ChatGPT-4o模型表现最好，随后是Claude 3.7 Sonnet和文心一言X1模型，专家对不同模型的评价得分见表2，4种模型不同维度得分见图1。为便于模型性能的直观对比，对得分进行了归一化处理后生成雷达图，见图2。
2.2　不同模型的案例分析能力
DeepSeek R1、ChatGPT-4o和Claude 3.7 Sonnet在脑卒中的临床病例分析上展现出了卓越的能力，均分均>4分；文心一言X1模型在案例分析中的表现则相对较弱，均分为3分。4种模型得分比较差异无统计学意义（P=0.097），表明其性能具有可比性，见图3。
2.3　不同模型总分比较
DeepSeek R1模型整体回答表现最好，在准确性、综合性、人文关怀度和案例分析能力方面均展现出了显著的稳定性，所有评估维度得分均>80分。除了人文关怀度这一维度外，ChatGPT-4o模型在其他各方面的表现都相对均衡且出色。值得关注的是，文心一言X1模型的应答结果表现最稳定，但得分低于其他模型。DeepSeek R1和ChatGPT-4o总分比较差异无统计学意义（P=0.32），表示结果有可比性，且DeepSeek R1模型更出色。见图4。
3　讨论
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3.1　大语言模型在健康咨询方面的能力
本研究系统性评估了4种不同的大语言模型在脑卒中健康咨询和病人教育方面的性能，结果显示大部分模型的结果都是令人满意的，但文心一言X1未能对案例分析提供高效回答。DeepSeek R1和ChatGPT-4o模型在复杂临床推理和循证医学知识整合中都展现了卓越能力，体现了其在医疗场景中应用的优势，两者差异较小，这与相关研究结论[12]一致。但一项评估不同语言模型在尿结石病人的健康咨询的测试显示，Claude模型得分最高，ChatGPT排名第2位[13]。可能是因为本研究使用的是最新迭代的ChatGPT-4o模型，效能优于Claude模型。文心一言X1模型虽总分较低，但在中文语境适应性方面展现了独特优势，验证了Zhu 等[14]大语言模型需本地化的观点。
3.2　大语言模型在健康咨询方面的局限性
尽管大语言模型在健康咨询领域展现出显著潜力，但仍存在人文关怀度差、应答速度差异、算法黑箱和伦理安全等局限。首先，尽管已有研究将大型语言模型应用到心理及精神领域[15-16]，但本研究结果显示，所有模型的人文关怀度得分都较低，表明人工智能在情感识别、共情表达和情感反馈等方面存在短板，这与Kim等[17]研究结论是一致的。其次，模型的应答速度存在差异，本研究中ChatGPT-4o模型的响应速度远胜所有模型，而性能最好的开源模型DeepSeek R1的响应速度缓慢，且经常超时显示“服务器繁忙”，这可能是由于用户流量过大、高推理算力需求、大规模的恶意攻击等因素，会影响病人的体验感和使用效率等。值得关注的是，现有大语言模型的推理机制都存在显著的“算法黑箱”，ChatGPT-4o和Claude 3.7 Sonnet模型不显示推理过程，而DeepSeek R1和文心一言X1模型虽提供推理过程，但其生成的医疗建议难以追溯至原始循证依据。“算法黑箱”导致的信息不透明严重削弱了病人对大语言模型建议的信任度[18]。此外，大语言模型在健康咨询中的应用也面临着严峻的伦理与安全挑战，包括病人隐私保护、医疗责任划分及数据合规性等问题。
3.3　大语言模型在健康咨询方面的应用前景及建议
尽管大语言模型在健康咨询领域仍面临技术瓶颈与伦理争议，但其未来发展前景广阔。我国人口众多、医疗资源分布不均，健康信息获取碎片化、缺乏医生指导，导致居民倾向于在线收集医疗信息但可能并不准确，更加凸显了大语言模型在健康咨询中的战略价值[10]。以DeepSeek R1模型为例，其本土化部署优势克服了ChatGPT等国外模型的访问壁垒，同时依托开源特性降低了使用成本，为大规模普惠医疗提供了可行性路径，有利于提升医疗服务的可及性和公平性[19]。目前针对大语言模型健康咨询的监管体系尚未形成[20]，2024年5月欧盟通过了《人工智能法案》，这为大型语言模型使用的伦理准则与行业标准提供了制定框架[21-22]。大语言模型的使用应建立相应的标准与规则[23]，以提升使用效率和准确性，为数智医院建设和医疗服务模式重构提供强有力的技术支持[24]。
4　小结
收起
本研究对医疗健康领域4种主流大语言模型进行评估，并系统验证其在脑卒中健康咨询场景的综合应用能力。结果显示，DeepSeek R1模型在准确性、全面性、易理解性及案例分析能力等方面表现突出。当前，人工智能与自然语言处理技术飞速发展，为医疗咨询与病人教育领域的大语言模型带来广阔前景。但在追求技术创新时，需建立专业医疗团队审查机制与持续监督体系，并采用多模态数据融合、领域知识增强等优化策略，以提升大语言模型在临床实践中的应用价值。

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2026年第40卷第4期
26
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doi: 10.12102/j.issn.1009-6493.2026.04.001
  • 接收时间：2025-05-07
  • 首发时间：2026-10-28
  • 出版时间：2026-02-25
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  • 收稿日期：2025-05-07
  • 修回日期：2026-02-01
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    1重庆市丰都县人民医院，重庆 408200
    2重庆医科大学附属第一医院

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