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Article(id=1284897536183878441, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1284897477333586425, articleNumber=null, orderNo=null, doi=10.3981/j.issn.1000-7857.2025.06.00037, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1749398400000, receivedDateStr=2025-06-09, revisedDate=1756137600000, revisedDateStr=2025-08-26, acceptedDate=null, acceptedDateStr=null, onlineDate=1784273044867, onlineDateStr=2026-07-17, pubDate=1782576000000, pubDateStr=2026-06-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1784273044867, onlineIssueDateStr=2026-07-17, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1784273044867, creator=13701087609, updateTime=1784273044867, updator=13701087609, issue=Issue{id=1284897477333586425, tenantId=1146029695717560320, journalId=1146031591421210625, year='2026', volume='44', issue='12', pageStart='1', pageEnd='164', issueExtLink='null', onlineDate='null', pubDate='1782576000000', pubDateStr='2026-06-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1784273030837, creator='13701087609', updateTime=1784273069123, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1284897638025773152, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1284897477333586425, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1284897638025773153, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1284897477333586425, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=46, endPage=53, ext={EN=ArticleExt(id=1284897536347456298, articleId=1284897536183878441, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Current situation, issues and suggestions for AI enabled biomedicine in China, columnId=null, journalTitle=Science & Technology Review, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Artificial intelligence (AI) is pressing the accelerator button to break through the bottleneck of biomedicine research and development, profoundly changing the medical model. This paper provides an overview of the competitive landscape of AI−enabled biomedicine globally. European and American countries are accelerating the application of AI in the biomedicine and have established a significant first−mover advantage. Then, this paper analyzes the current status of AI−enabled biomedicine in China, which is in the early stages of rapid development, while facing severe challenges such as the heavy burden of major diseases and decoupling from international chains. Moreover, the main issues identified include the lack of high−quality biomedicine data, insufficient underlying original innovation in AI, imperfect policy regulation and industrial chain ecosystem construction, and a shortage of composite talents. Finally, we suggest promoting the construction of a national research−oriented biomedicine data platform, increasing the original innovation of AI for biomedicine, promoting the landing and application of AI in the fields of traditional Chinese medicine, biomedicine, and brain computer interfaces, and increasing the cultivation of versatile talents.

, authors=Lixue ZOU1, Yanping YANG1, Jizong ZHAO2, *, Tianhuan GUO3, *, authorsList=Lixue ZOU, Yanping YANG, Jizong ZHAO, Tianhuan GUO, authorCompany=null, correspAuthors=Jizong ZHAO, Tianhuan GUO, authorNote=null, correspAuthorsNote=null, copyrightStatement=All rights reserved. Unauthorized reproduction is prohibited., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1284897537182122795, articleId=1284897536183878441, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=中国人工智能赋能生物医药的现状、问题及建议, columnId=1150494642375586098, journalTitle=科技导报, columnName=特色专题, runingTitle=null, highlight=null, articleAbstract=

人工智能（artificial intelligence，AI）技术正在为突破生物医药研发瓶颈按下“加速键”，深刻改变医药产业和医疗模式。综述了全球AI赋能生物医药的竞争环境：欧美国家正在密集推进生物数据建设，加速促进AI在生物医药领域的应用，已利用AI快速攻克了生物医药研发的诸多难题，建立起显著的先发优势。分析了中国AI赋能生物医药发展现状，中国AI生物医药产业迎来高速发展初期阶段的同时，面临着严峻挑战，重大疾病、罕见病等防治面临较重负担，同时，存在与国际脱钩断链风险，试剂耗材、制药装备精密控制核心部件、医疗器械关键零部件等供应链薄弱；剖析了存在的主要问题，包括高质量生物医药数据不足、AI底层原始创新不足、政策监管及产业链生态建设尚不完善、复合型人才缺失等。建议推进国家级研究型生物医药数据平台建设，加大面向生物医药的AI原始创新，推动AI在中医药、生物药和脑机接口等领域落地应用，加大培育复合型人才。

, authors=邹丽雪1, 杨艳萍1, 赵继宗2, *, 郭天欢3, *, authorsList=邹丽雪, 杨艳萍, 赵继宗, 郭天欢, authorCompany=null, correspAuthors=赵继宗, 郭天欢, authorNote=

邹丽雪，副研究员，研究方向为生物医药战略情报，电子信箱：

, correspAuthorsNote=
赵继宗（通信作者），教授，中国科学院院士，研究方向为神经外科，电子信箱：;
郭天欢（共同通信作者），高级工程师，研究方向为生物技术、生物工程，电子信箱：
, copyrightStatement=版权所有，未经授权，不得转载。, copyrightOwner=《科技导报》编辑部, extLink=null, articleAbsUrl=null, sourceXml=xSLw1kZkEWhKO1vRmqu29A==, magXml=7ZqbRNMfy57zdfh4+/mXnA==, pdfUrl=null, pdf=U26jCY+2Nb00ROG3yUDhUw==, pdfFileSize=754725, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=AmtMkOzLxCU32uV1XuwrWQ==, mapNumber=null, fund=null)}, authors=[Author(id=1284897538457191222, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1284897536183878441, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=zoulx@mail.las.ac.cn, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1284897538532688696, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1284897536183878441, authorId=1284897538457191222, language=EN, stringName=Lixue ZOU, firstName=Lixue, middleName=null, lastName=ZOU, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1, address=1National Science Library, Chinese Academy of Sciences, Beijing 100190, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1284897538591408953, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1284897536183878441, authorId=1284897538457191222, language=CN, stringName=邹丽雪, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1, address=1中国科学院文献情报中心，北京 100190, bio={"content":"

邹丽雪，副研究员，研究方向为生物医药战略情报，电子信箱：

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邹丽雪，副研究员，研究方向为生物医药战略情报，电子信箱：

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中国人工智能赋能生物医药的现状、问题及建议
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邹丽雪 1 , 杨艳萍 1 , 赵继宗 2, * , 郭天欢 3, *
科技导报 | 特色专题 2026,44(12): 46-53
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科技导报 |特色专题 2026 , 44 (12) : 46 -53
中国人工智能赋能生物医药的现状、问题及建议
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邹丽雪1 , 杨艳萍1, 赵继宗2, * , 郭天欢3, *
作者信息
  • 1中国科学院文献情报中心，北京 100190
  • 2首都医科大学附属北京天坛医院，北京 100070
  • 3中国科协生命科学学会联合体，北京 100101
通讯作者:
赵继宗（通信作者），教授，中国科学院院士，研究方向为神经外科，电子信箱：;
郭天欢（共同通信作者），高级工程师，研究方向为生物技术、生物工程，电子信箱：
作者简介:

邹丽雪，副研究员，研究方向为生物医药战略情报，电子信箱：

Current situation, issues and suggestions for AI enabled biomedicine in China
Lixue ZOU1 , Yanping YANG1, Jizong ZHAO2, * , Tianhuan GUO3, *
Affiliations
  • 1National Science Library, Chinese Academy of Sciences, Beijing 100190, China
  • 2Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
  • 3China Union of Life Science Societies, Beijing 100101, China
出版时间: 2026-06-28 doi: 10.3981/j.issn.1000-7857.2025.06.00037
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摘要
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人工智能（artificial intelligence，AI）技术正在为突破生物医药研发瓶颈按下“加速键”，深刻改变医药产业和医疗模式。综述了全球AI赋能生物医药的竞争环境：欧美国家正在密集推进生物数据建设，加速促进AI在生物医药领域的应用，已利用AI快速攻克了生物医药研发的诸多难题，建立起显著的先发优势。分析了中国AI赋能生物医药发展现状，中国AI生物医药产业迎来高速发展初期阶段的同时，面临着严峻挑战，重大疾病、罕见病等防治面临较重负担，同时，存在与国际脱钩断链风险，试剂耗材、制药装备精密控制核心部件、医疗器械关键零部件等供应链薄弱；剖析了存在的主要问题，包括高质量生物医药数据不足、AI底层原始创新不足、政策监管及产业链生态建设尚不完善、复合型人才缺失等。建议推进国家级研究型生物医药数据平台建设，加大面向生物医药的AI原始创新，推动AI在中医药、生物药和脑机接口等领域落地应用，加大培育复合型人才。

人工智能  /  生物医药  /  医疗器械  /  生物数据

Artificial intelligence (AI) is pressing the accelerator button to break through the bottleneck of biomedicine research and development, profoundly changing the medical model. This paper provides an overview of the competitive landscape of AI−enabled biomedicine globally. European and American countries are accelerating the application of AI in the biomedicine and have established a significant first−mover advantage. Then, this paper analyzes the current status of AI−enabled biomedicine in China, which is in the early stages of rapid development, while facing severe challenges such as the heavy burden of major diseases and decoupling from international chains. Moreover, the main issues identified include the lack of high−quality biomedicine data, insufficient underlying original innovation in AI, imperfect policy regulation and industrial chain ecosystem construction, and a shortage of composite talents. Finally, we suggest promoting the construction of a national research−oriented biomedicine data platform, increasing the original innovation of AI for biomedicine, promoting the landing and application of AI in the fields of traditional Chinese medicine, biomedicine, and brain computer interfaces, and increasing the cultivation of versatile talents.

artificial intelligence  /  biomedicine  /  medical devices  /  biological data
邹丽雪, 杨艳萍, 赵继宗, 郭天欢. 中国人工智能赋能生物医药的现状、问题及建议. 科技导报, 2026 , 44 (12) : 46 -53 . DOI: 10.3981/j.issn.1000-7857.2025.06.00037
Lixue ZOU, Yanping YANG, Jizong ZHAO, Tianhuan GUO. Current situation, issues and suggestions for AI enabled biomedicine in China[J]. Science & Technology Review, 2026 , 44 (12) : 46 -53 . DOI: 10.3981/j.issn.1000-7857.2025.06.00037
正文
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生物医药产业是关系国计民生和国家安全的战略性新兴产业，其范畴包括利用生物技术开发和生产用于预防、诊断和治疗人类疾病的药物和医疗器械。近年来，人工智能（AI）在蛋白质结构预测[1−2]、医学影像诊断[3−6]、脑机接口[7−8]等技术领域均实现了飞速发展，为生物医药产业的发展按下“加速键”[9−11]。药物研发领域，AI正在打破药物研发界的“双十定律”。现代医药学的研究思维和范式已逐渐显现瓶颈，不仅许多复杂疾病的发病机制尚未被完全阐明，药物研发也仍面临周期长、成本高、成功率低等问题[11]。据统计，一款创新药从启动研发到上市，平均成本超过10亿美元，研发时间超过10年[12−13]。波士顿咨询集团数据[14]显示，AI可以将临床前发现时间缩短30%~50%，成本降低25%~50%。英矽智能公司[15]利用生成式AI，从靶点发现到候选新药进入临床仅用18个月。医疗器械领域，AI医疗器械正在深刻改变医疗模式[16]。大模型[17]与第5代移动通信（5G）技术[18]、AI传感[19]、可穿戴设备等数字技术深度融合创新，实现医疗信息的高效处理与精准分析，推动医疗行业从传统医疗服务向“以人为中心”、全生命周期健康管理的个性化、精准化、智慧化医疗转变[20]。例如，当前全球超过1/3的人口受到中风、婴儿脑损伤等神经疾病的困扰[21]，脑机接口利用AI“读懂大脑”，在医疗康养、工业安全、教育体育、智慧生活等领域展现出广阔发展前景[22]，成为引领新一轮科技发展的重要力量，备受全球瞩目。
现有研究中，技术应用层面，一些研究围绕药物研发具体环节，介绍了AI在蛋白质结构预测和蛋白质设计[23−26]、靶点发现[23, 27−31]、靶标确证[27−28, 31−33]、临床研究[25, 27, 28, 30−31]、药物再利用[23, 27]中发挥的作用和研究进展，并指出面临的主要挑战，包括数据隐私和伦理、透明度和可解释性、知识产权、计算资源、数据质量与多样性、监管挑战；一些研究概述了AI在医疗器械[34−36]中的典型应用模式，包括赋能医疗装备、优化诊疗流程、辅助医学影像诊断、医疗机器人等，指出当前主要难题是算法泛化能力、模型可解释性、大规模临床验证以及数据隐私等。政策层面，一些研究分析了美国、欧盟和英国等国家制定的AI政策，提出要促进AI在医疗领域的应用，涉及药物研发和医疗器械[37]。在此基础上，本文综合分析全球AI赋能生物医药的竞争环境，聚焦中国当前发展现状，剖析中国发展AI赋能生物医药的深层次问题，并提出对策建议，为中国加速发展AI赋能生物医药产业提供支撑。
1 全球AI赋能生物医药的竞争环境
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1.1 欧美国家正在密集推进生物数据建设，促进AI在生物医药领域的应用
在加强生物数据供给方面，美国国立卫生研究院（National Institute of Health，NIH）启动“AI之桥”计划，4年内投资1.3亿美元[38]；欧盟委员会加速部署欧洲健康数据空间[39]；德国通过《健康数据使用法》（Health Data Utilization Act）建立分散式健康数据基础设施[40]；英国投入1.54亿英镑增加生物数据库容量[41]。在推进AI应用方面，美国国家科学基金会（National Science Foundation，NSF）启动国家AI研究资源试点项目[42−43]，6年内投入26亿美元，并建立皮德蒙特三叠纪再生医学引擎，10年内投资1.6亿美元；欧盟建设并开放医疗大规模AI测试和实验设施[44]，启动GenAI4EU计划[45]，建立了首批AI工厂[46]。
1.2 欧美国家已利用AI快速攻克生物医药研发的诸多难题
药物研发领域，在蛋白质结构预测和蛋白质设计方面，美国的谷歌公司、华盛顿大学、英伟达公司、麻省理工学院和Recursion公司已研发出AlphaFold[1]、ProteinMPNN[47]、Logos[48]、La−Proteina[49]、Boltz−2[50]等模型，成功锁定无序蛋白，突破不可成药靶点困境，生成全原子级别的蛋白质结构，并实现联合预测生物分子结构和结合亲和力。抗体设计方面，美国OpenAI公司投资的Chai Discovery公司推出抗体设计模型Chai−2[51]，实现零样本抗体设计，能够绕过分子发现中的传统高通量筛选需求，比现有计算方法提高了100倍，并将发现时间从数月或数年压缩到2周。大模型方面，OpenAI公司[52]、Arc研究所[53]、欧洲分子生物学实验室[54]于2025年相继推出生物AI大模型GPT−4b Micro、Evo−2、BioChatter，有望将干细胞生产效率提高50倍，加速基因疗法创新，助力理解药物机制。据统计，截至2024年1月，全球已有102项AI药物研发管线进入临床试验，发展较快的如美国BioXcel公司的BXCL−501已进入临床Ⅲ期，英矽智能公司的ISM001−055进入临床Ⅱ期，谷歌首款AI设计药物计划于2025年底进入临床试验阶段。医疗器械领域，截至2024年12月底，美国食品药品监督管理局（Food and Drug Administration，FDA）已批准了1016种AI医疗器械，获批数量每年上涨，目前尚未有生成式AI或由大语言模型驱动的医疗设备获批。2024年以来，全球脑机接口临床试验取得迅速突破，美国Neuralink公司已完成9例脑机接口临床植入试验，美国Paradromics公司完成Connexus系统的首例人体植入，德国CorTec公司完成首例Brain Interchange系统的人体植入。
1.3 美国不断加码制裁，切断中国AI生物医药产业创新和供应“双链”
美国自2025年1月2日起限制对中国AI投资，禁止使用生物序列数据且以算力超过1024进行AI系统训练的交易，这对中国百图生科等快速发展企业的融资和出海构成阻碍。美国商务部于2025年1月17日以“能够生成高质量生物数据，可用于AI训练”为由，对2类生物技术设备（高参数流式细胞仪、用于蛋白质组学的液相色谱质谱仪）实施出口管制。美国司法部于2025年1月8日限制中国获取美国批量敏感个人数据包括人类组学数据、个人健康数据等。美国众议院2024年通过《生物安全法案》（Biosecure Act）草案[55]，围堵中国“链主”药企，禁止美国联邦政府机构向华大基因、药明康德等企业采购或为其提供资金。美国2021年以研发“脑控”武器为由，将中国军事医学科学院及其下属研究所等列入实体清单，打压中国生物医药的发展。
2 中国AI赋能生物医药的发展现状
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2.1 中国AI赋能生物医药产业迎来高速发展的初期阶段，正处于战略机遇期
药物研发领域，中国在蛋白质结构预测、蛋白质设计、抗体和RNA药物设计等方向不断突破。例如，百奥几何公司推出蛋白质设计大模型GeoFlow，首次以原子级精度统一蛋白质结构预测与从头设计。华大智造公司推出EvoPlay模型，利用强化学习算法高效设计功能蛋白。中国科学院上海药物研究所提出了PertKGE模型，从微扰转录组数据中解耦出化合物与蛋白质相互作用。中南大学提出了蛋白质功能预测方法DPFunc，深度整合蛋白质序列与结构域信息，在提升预测精度的同时揭示结构与功能调控机制。上海交通大学提出AbNovo模型，能够实现多目标抗体设计。百图生科公司推出RNAGenesis大模型，成功设计出靶向亲和力达4.02 nM的高效适配体分子，为RNA药物研发提供全新范式。清华大学研发中西医药智能和定量分析技术与系统UNIQ系统，已应用于30余个中药、标志物等的创新研发与品种升级。
医疗器械领域，以脑机接口为例，中国脑机接口技术与国外水平相当。在侵入式脑机接口技术上，中国科学院脑科学与智能技术卓越创新中心于2025年成功开展侵入式脑机接口临床试验，使中国成为继美国之后，全球第二个进入临床试验阶段的国家。在半侵入式脑机接口技术上，“北脑一号”完成5例半侵入式脑机接口临床试验，上海脑虎科技公司实现了脑机接口汉语“意念对话”。
2.2 新发展阶段，中国生物医药产业面临更严峻挑战
一是重大疾病、罕见病等给人民生命健康带来的威胁是国家亟待解决的问题。《中国心血管健康与疾病报告2024》显示，1990—2019年，中国全人群心血管疾病的发病人数从530.07万例增至1234.11万例，增幅132.82%。《2024年全国癌症报告》显示，2022年中国癌症新发病例482.47万例。二是中国生物医药产业与国际存在“脱钩断链”风险。长期以来，中国生物医药供应链薄弱环节较多，生物医药研发所需的试剂耗材、制药装备精密控制核心部件、医疗器械关键零部件高度依赖进口，高端医疗器械供给不足。
3 中国发展AI生物医药存在的主要问题
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3.1 生物医药数据资源短缺与高质量数据不足
生物医药数据积累不足，数据可用性低。一是缺少类似国外PubMed的研究型生物医药大数据平台，中国生物相关数据中心建设起步晚，在数据整合利用、影响力、资金投入、基础设施方面与国际相比还有差距，目前主要处于服务型数据中心的发展阶段，对比国外尚未转型为研究型生物医药数据中心，对生物医药研究支撑作用有限，亟需提档升级。二是国内创新药械研发起步晚，临床数据、基因组数据、药物实验数据、大脑数据等原始数据积累仍微不足道。三是生物医药企业自有核心数据量少，数据可用性低，难以支撑大模型的训练和AI创新。
生物医药数据大量外流，海外依赖严重。一是“国内研究数据国外找”问题突出。长期以来，中国产生的生物数据伴随在国际顶级期刊发表论文，大部分提交给美国国家生物技术信息中心（National Center for Biotechnology Information‌，NCBI）、欧洲生物信息学研究所（European Bioinformatics Institute，EBI）等国外数据库，多达数千TB的数据目前存储在国外数据中心，占国际生物数据的20%以上。二是中国生物医药科研工作严重依赖国外的PubMed数据库和科研设施，用户占国际生物数据用户的20%以上。
生物医药数据“孤岛”问题突出，数据无法充分共享。一是数据共享意愿低，生物医药研发数据是科研机构、医院和企业的核心资产，出于知识产权、商业保护和数据安全顾虑，多数机构不愿共享。二是数据共享机制尚不健全，缺乏数据生产方以及数据利用方均能有效获益的分配模式，造成数据共享及利用缺乏源动力，不同机构之间甚至同一机构内部的数据无法进行充分共享。三是数据共享存在技术壁垒，中国生物医药数据分散在科研机构、医院和企业，多维异构生物数据和临床数据标准化程度低，难以高质量整合。
3.2 面向生物医药的AI底层原始创新力不足
针对生物医药的AI底层基础研究薄弱，原创性算法少。一是在AI底层基本规律、数学理论基础、算法可解释性等方面研究薄弱，与国际前沿存在较大差距。二是针对生物医药领域的核心算法创新较少，医疗器械和药物研制所使用的前端开发环境、算法框架均以国外开源产品为主，亟待从底层进行颠覆性创新，提出新的大模型体系，建立新模型框架和算法来理解生物复杂体系。三是算力供给仍有不足，AI模型训练数据需要大量存储资源和计算基础设施，算力供给碎片化导致资源利用率低。四是高端硬件自主可控性不足，研发机构仍需储备进口高端芯片，国产高端芯片尚未实现规模替代。
3.3 AI生物医药的政策监管及产业链生态建设尚不完善
AI生物医药全过程监管体系仍不完善。数据隐私保护和伦理问题要求监管机构制定更加严格的法律和监管框架，确保个人数据的安全合法使用。AI在影像诊断、疾病预测等方面的准确性和可靠性均有待提升，仍缺乏统一的算法透明度标准，微小误差都可能对疾病诊断等产生重大影响。
AI生物医药产业化路径相对模糊。多数研发机构依赖已有的研发模式，引入AI需要考虑更换系统、成本及风险等多方面问题。AI生物医药的转化路径存在“实验室−临床−市场”断层现象，高校和科研机构研发的AI技术缺乏成熟的成果转化平台和产业孵化机制，在实际临床场景中落地较难。例如，一些侵入式脑机接口技术已达到国际先进水平，但缺少应用场景，尚未开展临床试验。
3.4 生物医药和AI复合型人才缺失
生物医药和AI领域的领军人才匮乏。一是面向生物医药领域，研究AI底层基础理论的顶尖人才不足，尚未形成“以才带才，以才引才”的模式。二是缺乏在生物医药领域具有原创能力的顶尖科学家，中国很多生物医药企业创始人是外国国籍。
生物医药和AI领域的跨学科人才缺口大。一是缺少既精通AI、信息科学、计算机等学科，又精通生物医药、脑科学、临床医学等学科的人才。二是药物研发和医疗器械专家与AI专家跨学科合作难度大，领域之间知识体系和语言体系的差异较大，增加了跨学科融合的难度，难以在短时间内培养出具备跨学科能力的人才。
4 中国补齐AI生物医药短板的建议
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4.1 推进国家级研究型生物医药数据平台建设
整合分散的生物医药数据，建设国家级研究型生物医药数据平台。一是整合分散在科研机构、医院、企业的药物研发和医疗器械数据，体系化建设生物医药数据池。二是制定药物研发和医疗器械数据标准，完善数据采集、处理、存储和交换的标准规范，提高数据质量，降低数据集成复杂性，实现高质量整合。三是推进国家生物信息中心和人类遗传资源库、生物医学大数据等重大战略资源平台建设，加强基础设施和服务体系建设，为生物医药大数据汇聚提供平台和服务支撑，并推进其向平台型生物医药研究数据中心转变，减少对国外数据的依赖。四是建设生物医药国际一流期刊，制定生物数据汇交政策，要求论文支撑数据汇交至国家生物医药数据池，促进科研数据回流。
建立生物医药数据共享机制，推动生物医药数据高效流通。一是制定生物医药数据管理办法，建立生物医药数据收集、标识、分类、训练、验证、转化、应用、评价、共享和互操作体系，打破数据“孤岛”。二是建立生物医药数据共享激励机制，健全数据使用的立项、授权、审批管理制度、行业规范和共识，通过数据产权归属认定、市场交易、权益分配、利益保护等方式，提高科研机构、医院、企业的数据共享意愿，促进各方数据可信交互和融合利用。
4.2 加强面向生物医药的AI原始创新研究
加强面向生物医药的AI底层机理研究。一是设立AI+生物医药基础研究专项，支持针对生物医药领域的AI基础理论、底层规律、模型架构等方面的研究，创新技术路径。二是设立AI+生物医药应用基础研究专项。针对中国生物医药数据特性，支持可解释性AI、多模态AI等研究，构建符合中国生物医药研发需求的AI模型。基于国家生物信息中心、国家微生物科学数据中心、国家药物化合物数据库等现有数据库，加大跨层级多尺度研究，研发基于跨层级计算架构和多类别计算单元的生物医学大数据高效、并行、异构计算新技术和方法。
加强AI生物医药的自主创新研究。一是加大对生物医药领域高性能计算芯片的研发投入，减少对海外AI芯片的依赖。二是加大对高端医疗器械零部件、制药试剂耗材的研发投入，集中资源攻克生物医药“卡脖子”技术难题，提高AI药物研发和AI医疗器械的自主创新。
4.3 推动AI在生物医药领域的落地应用
面向中医药领域，形成中国特色的AI中医药应用场景。一是打造AI+中医药管理服务标杆型应用项目，支持中医临床智能辅助诊疗、中医经络智能检测、中医临床病案智能质控等AI应用工程。二是打造AI+中医药产业标杆型应用项目，支持中药智能生产设备、中药材智能生态种植，中药材智能仿生鉴定识别等AI应用工程。
面向药物研发领域，瞄准国家战略需求推动AI落地应用。一是设立AI+药物研发标杆型应用工程项目，聚焦AI在加速药物设计及优化、临床试验研究等环节的应用，支持药物研发模型赋能新药研发。二是建立中国特色的AI政、产、学、研、医联合体，建立合作平台和合作机制，推动AI更快地在生物医药领域落地，并以应用牵引算法技术、智能芯片和硬件的突破。
面向脑机接口领域，推动AI在未来健康产业的应用。一是支持医疗机构、高校院所和企业建设脑机接口临床试验基地，支持开展脑机接口产品临床试验。二是打造脑机接口的标杆应用场景，在医疗、康养、工业等领域形成典型应用。三是建设特色脑机接口体验和示范中心，推广脑机接口应用示范，促进脑机接口技术推广普及。
4.4 培养生物医药和AI跨学科人才
加强生物医药和AI顶尖人才培育和引进。一是加大对生物医药和AI跨学科领军人才的政策扶持力度，在项目资助上对跨学科领军人才予以倾斜。二是引培AI赋能生物医药领域的领军人才，依托项目培养跨学科领域的骨干人才，把骨干人才培养作为项目考核指标，落实到项目开发任务中。三是加大海外高层次人才引进力度，吸引海外生物医药和AI领域顶尖科学家、企业家来华发展，强化顶尖人才集聚效应。四是鼓励企业联合高校院所和医院共建联合培养基地，聚合人才资源，在科研、临床及成果转化方面深度融合，培养跨学科领域高端人才。
加强培养生物医药和AI跨学科复合型人才。一是拓展“AI+生物医药”专业方向的专业化教育体系，依托高校、医疗机构和科研院所，优化跨学科人才培养模式。二是设置“AI+生物医药”学科，推动高校开设AI生物医药课程，体系化培养生物医药和AI跨学科青年人才。三是设立资金和奖励机制，鼓励企业与高校、科研机构开展产教融合，联合培养跨学科人才。
5 结论
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本文重点分析了中国AI赋能生物医药发展的外部环境和内生需求，从外部来看，欧美国家正在密集推进生物数据建设、加强技术管制，从源头抢占制高点。从中国生物医药产业发展来看，重大疾病治疗依然亟待解决，且中国生物医药产业与国际存在“脱钩断链”风险。中国在AI赋能生物医药领域存在的问题主要是高质量生物医药数据不足、AI底层原始创新不足、政策监管及产业链生态建设尚不完善、复合型人才缺失。因此，建议推进国家级研究型生物医药数据平台建设，加大面向生物医药的AI原始创新，推动AI在中医药、药物研发和脑机接口领域落地应用，加大培育复合型人才。

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2026年第44卷第12期
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doi: 10.3981/j.issn.1000-7857.2025.06.00037
  • 接收时间：2025-06-09
  • 首发时间：2026-07-17
  • 出版时间：2026-06-28
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  • 收稿日期：2025-06-09
  • 修回日期：2025-08-26
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    1中国科学院文献情报中心，北京 100190
    2首都医科大学附属北京天坛医院，北京 100070
    3中国科协生命科学学会联合体，北京 100101

通讯作者:

赵继宗（通信作者），教授，中国科学院院士，研究方向为神经外科，电子信箱：;
郭天欢（共同通信作者），高级工程师，研究方向为生物技术、生物工程，电子信箱：
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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