Research on interdisciplinary issues of artificial intelligence medical devices_Journal of Biomedical Engineering

Authors：

YAN Shu , LU Yan , XU Dongzi ,  OUYANG Zhaolian

Department of Strategic Intelligence Research on Medical Device and Drug, Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100020, P. R. China;

Corresponding author：

OUYANG Zhaolian, Email: ouyang.zhaolian@imicams.ac.cn

Keywords：

Artificial intelligence; Medical devices; Interdisciplinary; Citation analysis

DOI：

10.7507/1001-5515.202407052

Video：

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Abstract Full text Figures/Tables Video References Cited by

In recent years, the research on artificial intelligence medical devices has risen markedly along with the expanding application scenarios, exhibiting prominent interdisciplinary characteristics. From 2000 to 2024, the variety of research in artificial intelligence medical devices has significantly increased, while the balance of disciplines has slightly declined, and Simpson's diversity index has continuously increased. Medicine and biology are the main research themes and supportive disciplines in this field. Knowledge from computer science, engineering technology, and mathematics is widely involved and shows an upward trend, while content from the humanities and social sciences is less involved in the research. Compared to the United States and the United Kingdom, China has relatively less biological and chemical knowledge content in the research of this field, but more content related to computer science, engineering technology and material science is involved. This study analyzes the current state and trends of interdisciplinary on artificial intelligence medical devices from the perspective of macro-categories of disciplines, aiming to provide references for research planning, talent training and interdisciplinary cooperation in the field.

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1. 路甬祥. 学科交叉与交叉科学的意义. 中国科学院院刊, 2005, 20(1): 58-60.
2. Porter A L, Rafols I. Is science becoming more interdisciplinary? Measuring and mapping six research fields over time. Scientometrics, 2009, 81(3): 719.
3. Rafols I, Meyer M. Diversity and network coherence as indicators of interdisciplinarity: case studies in bionanoscience. Scientometrics, 2010, 82(2): 263-287.
4. Berkes E, Marion M, Milojević S, et al. Slow convergence: Career impediments to interdisciplinary biomedical research. Proc Natl Acad Sci U S A, 2024, 121(32): e2402646121.
5. White J A, Gaver D P, Butera R J, et al. Core competencies for undergraduates in bioengineering and biomedical engineering: findings, consequences, and recommendations. Ann Biomed Eng, 2020, 48(3): 905-912.
6. Shafi S, Parwani A V. Artificial intelligence in diagnostic pathology. Diagn Pathol, 2023, 18(1): 109.
7. Cheng K, Guo Q, He Y, et al. Exploring the potential of GPT-4 in biomedical engineering: the dawn of a new era. Ann Biomed Eng, 2023, 51(8): 1645-1653.
8. 窦贤康. 推动基础研究高质量发展为建设世界科技强国夯实根基. 中国科学基金, 2023, 37(5): 709-712.
9. Malea E O, López-Cozar E D. Dimensions: re-discovering the ecosystem of scientific information. El Profes Inf, 2018, 27: 420.
10. Thelwall M. Dimensions: a competitor to scopus and the web of science?. J Informetr, 2018, 12(2): 430-435.
11. 李秋实, 刘红玉. 基于文献计量的期刊分区与论文学术评价量化实证研究. 图书馆工作与研究, 2015, 4: 60-66.
12. 张琳, 黄颖. 交叉科学测度、评价与应用. 科学出版社, 2019: 96-104.
13. Simpson E H. Measurement of diversity. Nature, 1949, 163(4148): 688.
14. Tran B X, Latkin C A, Sharafeldin N, et al. Characterizing artificial intelligence applications in cancer research: a latent dirichlet allocation analysis. JMIR Med Inform, 2019, 7(4): e14401.
15. Colling R, Pitman H, Oien K, et al. Artificial intelligence in digital pathology: a roadmap to routine use in clinical practice. J Pathol, 2019, 249(2): 143-150.
16. Wu J H, Liu T Y A, Hsu W T, et al. Performance and limitation of machine learning algorithms for diabetic retinopathy screening: meta-analysis. J Med Internet Res, 2021, 23(7): e23863.
17. Grauslund J. Diabetic retinopathy screening in the emerging era of artificial intelligence. Diabetologia, 2022, 65(9): 1415-1423.
18. Kataria S, Ravindran V. Digital health: a new dimension in rheumatology patient care. Rheumatol Int, 2018, 38(11): 1949-1957.
19. Yetisen A K, Martinez-Hurtado J L, Uenal B, et al. Wearables in medicine. Adv Mater, 2018, 30(33): 1706910.
20. Boulos L J, Mendes A, Delmas A, et al. An iterative and collaborative end-to-end methodology applied to digital mental health. Front Psychiatry, 2021, 12: 574440.
21. Bera K, Schalper K A, Rimm D L, et al. Artificial intelligence in digital pathology-new tools for diagnosis and precision oncology. Nat Rev Clin Oncol, 2019, 16(11): 703-715.
22. 严舒, 徐东紫, 欧阳昭连. 美国人工智能医疗器械监管与应用分析. 中国医疗设备, 2021, 36(2): 117-122.
23. Hedderich D M, Weisstanner C, Van Cauter S, et al. Artificial intelligence tools in clinical neuroradiology: essential medico-legal aspects. Neuroradiology, 2023, 65(7): 1091-1099.
24. Subramaniam S, Akay M, Anastasio M A, et al. Grand challenges at the interface of engineering and medicine. IEEE Open J Eng Med Biol, 2024, 5: 1-13.
25. Huang X, Qiao C. Enhancing computational thinking skills through artificial intelligence education at a STEAM high school. Sci Educ, 2024, 33(2): 383-403.
26. Friedrich S, Antes G, Behr S, et al. Is there a role for statistics in artificial intelligence?. Adv Data Anal Classif, 2022, 16(4): 823-846.
27. Wang J K, Roy S K, Barry M, et al. Institutionalizing healthcare hackathons to promote diversity in collaboration in medicine. BMC Med Educ, 2018, 18: 269.
28. Maloney L M, Hakimi M, Hays T, et al. Learning the language of medical device innovation: a longitudinal interdisciplinary elective for medical students. Acad Med, 2022, 97(9): 1341-1345.
29. Bini F, Franzò M, Maccaro A, et al. Is medical device regulatory compliance growing as fast as extended reality to avoid misunderstandings in the future?. Health Technol, 2023, 13(5): 831-842.
30. Grunhut J, Marques O, Wyatt A T M. Needs, Challenges, and applications of artificial intelligence in medical education curriculum. JMIR Med Educ, 2022, 8(2): e35587.
31. Maccaro A, Stokes K, Statham L, et al. Clearing the fog: a scoping literature review on the ethical issues surrounding artificial intelligence-based medical devices. J Pers Med, 2024, 14(5): 443.

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