Chinese Oncologists' Perspectives on Integrating AI into Clinical Practice: Cross-Sectional Survey Study.

Ming Li, XiaoMin Xiong, Bo Xu, Conan Dickson
Author Information
  1. Ming Li: Department of Health Policy Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States. ORCID
  2. XiaoMin Xiong: Chongqing Key Laboratory of Intelligent Oncology for Breast Cancer, Chongqing University Cancer Hospital, Chongqing University School of Medicine, Chongqing, China. ORCID
  3. Bo Xu: Chongqing Key Laboratory of Intelligent Oncology for Breast Cancer, Chongqing University Cancer Hospital, Chongqing University School of Medicine, Chongqing, China. ORCID
  4. Conan Dickson: Department of Health Policy Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States. ORCID

Abstract

BACKGROUND: The rapid development of artificial intelligence (AI) has brought significant interest to its potential applications in oncology. Although AI-powered tools are already being implemented in some Chinese hospitals, their integration into clinical practice raises several concerns for Chinese oncologists.
OBJECTIVE: This study aims to explore the concerns of Chinese oncologists regarding the integration of AI into clinical practice and to identify the factors influencing these concerns.
METHODS: A total of 228 Chinese oncologists participated in a cross-sectional web-based survey from April to June in 2023 in mainland China. The survey gauged their worries about AI with multiple-choice questions. The survey evaluated their views on the statements of "The impact of AI on the doctor-patient relationship" and "AI will replace doctors." The data were analyzed using descriptive statistics, and variate analyses were used to find correlations between the oncologists' backgrounds and their concerns.
RESULTS: The study revealed that the most prominent concerns were the potential for AI to mislead diagnosis and treatment (163/228, 71.5%); an overreliance on AI (162/228, 71%); data and algorithm bias (123/228, 54%); issues with data security and patient privacy (123/228, 54%); and a lag in the adaptation of laws, regulations, and policies in keeping up with AI's development (115/228, 50.4%). Oncologists with a bachelor's degree expressed heightened concerns related to data and algorithm bias (34/49, 69%; P=.03) and the lagging nature of legal, regulatory, and policy issues (32/49, 65%; P=.046). Regarding AI's impact on doctor-patient relationships, 53.1% (121/228) saw a positive impact, whereas 35.5% (81/228) found it difficult to judge, 9.2% (21/228) feared increased disputes, and 2.2% (5/228) believed that there is no impact. Although sex differences were not significant (P=.08), perceptions varied-male oncologists tended to be more positive than female oncologists (74/135, 54.8% vs 47/93, 50%). Oncologists with a bachelor's degree (26/49, 53%; P=.03) and experienced clinicians (���21 years; 28/56, 50%; P=.054). found it the hardest to judge. Those with IT experience were significantly more positive (25/35, 71%) than those without (96/193, 49.7%; P=.02). Opinions regarding the possibility of AI replacing doctors were diverse, with 23.2% (53/228) strongly disagreeing, 14% (32/228) disagreeing, 29.8% (68/228) being neutral, 16.2% (37/228) agreeing, and 16.7% (38/228) strongly agreeing. There were no significant correlations with demographic and professional factors (all P>.05).
CONCLUSIONS: Addressing oncologists' concerns about AI requires collaborative efforts from policy makers, developers, health care professionals, and legal experts. Emphasizing transparency, human-centered design, bias mitigation, and education about AI's potential and limitations is crucial. Through close collaboration and a multidisciplinary strategy, AI can be effectively integrated into oncology, balancing benefits with ethical considerations and enhancing patient care.

Keywords

References

  1. N Engl J Med. 2019 Apr 4;380(14):1347-1358 [PMID: 30943338]
  2. Nat Mach Intell. 2019 May;1(5):206-215 [PMID: 35603010]
  3. Sci Rep. 2022 May 25;12(1):8888 [PMID: 35614106]
  4. Cancer Cell. 2021 Jul 12;39(7):916-927 [PMID: 33930310]
  5. J Clin Med. 2021 Dec 06;10(23): [PMID: 34884410]
  6. Health Technol (Berl). 2024;14(1):1-14 [PMID: 38229886]
  7. JAMA. 2018 Dec 4;320(21):2199-2200 [PMID: 30398550]
  8. NPJ Digit Med. 2019 Apr 26;2:28 [PMID: 31304375]
  9. Science. 2019 Oct 25;366(6464):447-453 [PMID: 31649194]
  10. Healthc Manage Forum. 2020 Jan;33(1):47-49 [PMID: 31340674]
  11. Artif Intell Med. 2020 Jan;102:101753 [PMID: 31980092]
  12. J Natl Cancer Cent. 2022 Dec 05;3(1):83-91 [PMID: 39036310]
  13. PeerJ. 2019 Oct 4;7:e7702 [PMID: 31592346]
  14. Nat Genet. 2019 Jan;51(1):12-18 [PMID: 30478442]
  15. NPJ Digit Med. 2023 Sep 14;6(1):172 [PMID: 37709945]
  16. JAMA. 2016 Dec 13;316(22):2402-2410 [PMID: 27898976]
  17. Sci Rep. 2021 Mar 4;11(1):5193 [PMID: 33664367]
  18. NPJ Digit Med. 2023 Jun 14;6(1):113 [PMID: 37311802]
  19. BMC Med Inform Decis Mak. 2023 Apr 20;23(1):73 [PMID: 37081503]
  20. Health Sci Rep. 2023 Sep 04;6(9):e1543 [PMID: 37674620]
  21. BMJ Qual Saf. 2019 Mar;28(3):231-237 [PMID: 30636200]
  22. Cancer Med. 2021 Jun;10(12):4138-4149 [PMID: 33960708]
  23. AMA J Ethics. 2019 Feb 1;21(2):E146-152 [PMID: 30794124]
  24. Future Healthc J. 2019 Jun;6(2):94-98 [PMID: 31363513]
  25. N Engl J Med. 2018 Mar 15;378(11):981-983 [PMID: 29539284]
  26. Nat Med. 2019 Jan;25(1):37-43 [PMID: 30617331]
  27. Br J Cancer. 2022 Jan;126(1):4-9 [PMID: 34837074]
  28. Pathol Res Pract. 2020 Sep;216(9):153040 [PMID: 32825928]
  29. JAMA. 2018 Jan 2;319(1):19-20 [PMID: 29261830]
  30. Nat Med. 2019 Jan;25(1):44-56 [PMID: 30617339]
  31. Zhonghua Yi Xue Za Zhi. 2020 Feb 18;100(6):411-415 [PMID: 32146762]
  32. Bioethics. 2023 Jun;37(5):424-429 [PMID: 36964989]
  33. J AHIMA. 2012 Oct;83(10):38-43; quiz 44 [PMID: 23061351]
  34. N Biotechnol. 2022 Sep 25;70:67-72 [PMID: 35526802]

Word Cloud

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