从政策推动到研究产出——浅析医院主导人工智能研究的技术性挑战
From Policy Promotion to Research Output: Brief Analysis of Technical Challenges of Hospital-led Artificial Intelligence Research
From Policy Promotion to Research Output: Brief Analysis of Technical Challenges of Hospital-led Artificial Intelligence Research
作者
庄昱1,2(北京大学 哲学系 北京 100871;北京大学第三医院 北京 100191)
周程1,3*(北京大学 哲学系 北京 100871;北京大学 医学人文学院 北京 100191)
周程1,3*(北京大学 哲学系 北京 100871;北京大学 医学人文学院 北京 100191)
中文关键词
人工智能;医院主导;多模态数据;协调员
英文关键词
artificial intelligence;hospital leading;multi-modal data;research coordinator
中文摘要
近年来，人工智能已成为医疗健康相关研究的重点方向和国际竞争热点。为了解我国医院主导人工智能研究的现况及挑战，该研究采用定量、定性相结合的研究方法，选择 14 家国家高质量发展试点医院作为样本，对样本医院 2018 年以来人工智能相关研究论文及专利进行分析，并尝试剖析了医院主导人工智能研究未能深入开展的技术性挑战。结果显示，医院主导的人工智能研究论文数在增加，但约 55% 的研究仅是浅层次研究，研究质量仍可提高。同时，医院获批的人工智能专利数量也较少。医院主导人工智能研究的技术性难题在于学习曲线陡峭、迭代计算的成本高、将临床多模态数据转化为高质量研究数据挑战较多和可解释性弱。医疗机构应主动响应政策推动，调动内部资源，提前布局多模态数据资源，培养人工智能协调员，促进研究及产出。
英文摘要
In recent years, artificial intelligence has become a key direction of medical and health-related research and a hot spot of international competition. In order to investigate the current situation and challenges in hospital-led artificial intelligence researched, this study selects 14 national pilot hospitals to promote the high-quality development of public hospitals as samples, adopts a combination of quantitative and qualitative methods, analyzes the research articles related to artificial intelligence published by the sample hospitals in recent years, and analyzes the technical challenges in the hospital-led artificial intelligence research. The results show that although the number of hospital-led artificial intelligence research papers is increasing, in which 55% of the research is of prospect and expectation, while the quality of research could be improved. Meanwhile, the number of authorized AI related patent is rather small. The technical difficulties of hospital-led artificial intelligence research lie in the steep learning curve of artificial intelligence technology, high costs from computation iteration, difficulties in transferring clinical multimodal data into research data, and weak explainability. Hospitals should actively respond to policy promotion, reallocating resources to cultivate artificial intelligence coordinators, organize multi-modal data resources, and promote research and outputs.
DOI10.16418/j.issn.1000-3045.20230111001
作者简介
庄昱 北京大学第三医院管理助理研究员，北京大学哲学系在读博士。主要研究领域：科学技术哲学。
E-mail: y829@bjmu.edu.cn
ZHUANG Yu Assitant Management Researcher at Peking University Third Hospital, and Ph.D. student at Department of Philosophy, Peking University. His researches focus on philosophy of science and technology.
E-mail: y829@bjmu.edu.cn
周程 北京大学哲学系教授、医学人文学院院长。国务院学位委员会科学技术史学科评议组成员。研究领域为科学社会史、科学技术与社会、创新管理与科技政策。
E-mail: zhoucheng@pku.edu.cn
ZHOU Cheng Ph.D., Professor at Department of Philosophy and Dean of School of Health Humanities, Peking University. He is Member of the Discipline Appraisal Group for History of Science and Technology at the Academic Degrees Committee of the State Council, China. His research focuses on social history of science; science, technology and society studies;innovation management; and science and technology policy.
E-mail: zhoucheng@pku.edu.cn
E-mail: y829@bjmu.edu.cn
ZHUANG Yu Assitant Management Researcher at Peking University Third Hospital, and Ph.D. student at Department of Philosophy, Peking University. His researches focus on philosophy of science and technology.
E-mail: y829@bjmu.edu.cn
周程 北京大学哲学系教授、医学人文学院院长。国务院学位委员会科学技术史学科评议组成员。研究领域为科学社会史、科学技术与社会、创新管理与科技政策。
E-mail: zhoucheng@pku.edu.cn
ZHOU Cheng Ph.D., Professor at Department of Philosophy and Dean of School of Health Humanities, Peking University. He is Member of the Discipline Appraisal Group for History of Science and Technology at the Academic Degrees Committee of the State Council, China. His research focuses on social history of science; science, technology and society studies;innovation management; and science and technology policy.
E-mail: zhoucheng@pku.edu.cn
