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Research on Ethical Issues and Countermeasures in Artificial Intelligence-Assisted Healthcare

Xinyu Zhou
Vol.6, No.8, pp.29-35, 2026 DOI: 10.62051/ijphmr.v6n8.04 September 29, 2026
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Abstract: Since emerging in the 1950s, medical artificial intelligence (AI) has been deeply integrated into all segments of healthcare systems. It delivers prominent advantages in medical image diagnosis, pathological analysis, intelligent surgery, drug research and development and other fields, effectively lifting the efficiency, accuracy and overall quality of medical services, and becoming a core driving force for the digital and intelligent transformation of the healthcare industry. Nevertheless, the extensive application and technological iteration of AI have also brought new governance challenges. Focusing on ethical risks arising from AI-assisted medical services, this paper analyzes the relevant ethical dilemmas, root causes and corresponding solutions. When diagnostic and therapeutic errors occur in AI-aided medical activities, liabilities involve three stakeholders: technology developers, medical institutions and patients. The complicated liability structure renders the traditional single-subject liability model inapplicable, creating obstacles to liability identification. In addition, medical AI features complex technical frameworks and the "black box" algorithm effect: its internal processing logic cannot be fully interpreted by humans, leading to untraceable decision-making processes. Meanwhile, safety and ethical problems including medical data leakage, algorithmic bias and clinicians’ over-reliance on technology have become increasingly prominent in China. To address the above ethical risks, this paper compares and draws on mature regulatory practices of the United States, the European Union and Japan for medical AI. It puts forward targeted suggestions to improve the liability system and regulatory mechanism of medical AI, clarify the rights and obligations of all stakeholders, standardize the clinical application of artificial intelligence, and strike a balance between technological innovation and medical safety. This research provides references for the standardized development of AI-assisted healthcare in China.
Keywords: Medical artificial intelligence, Ethical issues, Countermeasure research
APA Citation: Xinyu Zhou (2026). Research on Ethical Issues and Countermeasures in Artificial Intelligence-Assisted Healthcare. International Journal of Public Health and Medical Research, 6(8), 29-35. https://doi.org/10.62051/ijphmr.v6n8.04

References

  1. Xu, L. L., Huang, D. Q., Liao, Y. L., & Dai, H. M. (2026). Application status of artificial intelligence in the medical field. Clinical Medical Engineering, 33(4), 503–508.
  2. Jan, Z., El Assadi, F., Abd-Alrazaq, A., & Jithesh, P. V. (2023). Artificial intelligence for the prediction and early diagnosis of pancreatic cancer: Scoping review. Journal of Medical Internet Research, 25, e44248.
  3. Tang, H. R., & Huang, H. (2026). Application of artificial intelligence technology in the medical field. Clinical Research and Practice, 11(4), 195–198.
  4. Chen, X. P., Cao, L., & Chen, Y. C. (2026). Innovative applications and challenges of artificial intelligence medical devices. China Plant Engineering, (8), 31–34.
  5. Chen, Z. L. (2026). Research on responsibility allocation of medical artificial intelligence [Master’s thesis, Jiangxi University of Finance and Economics].
  6. Zeng, W. Y., Zheng, S. Y., Zhao, X. Q., Wang, Z., Zhang, Y. J., & Ma, Z. Y. (2026). Advances in generative biology and bio-inspired artificial intelligence. Chinese Bulletin of Life Sciences, 38(2), 248–267.
  7. Al Kuwaiti, A., Nazer, K., Al-Reedy, A., Al-Shehri, S., Al-Muhanna, A., Subbarayalu, A. V., Al Muhanna, D., & Al-Muhanna, F. A. (2023). A review of the role of artificial intelligence in healthcare. Journal of Personalized Medicine, 13(6), 951.
  8. Weidener, L., & Fischer, M. (2023). Teaching AI ethics in medical education: A scoping review of current literature and practices. Perspectives on Medical Education, 12(1), 399–410.
  9. Chatterjee, S., Fruhling, A., Kotiadis, K., & Gartner, D. (2024). Towards new frontiers of healthcare systems research using artificial intelligence and generative AI. Health Systems (Basingstoke, England), 13(4), 263–273.
  10. Cao, Y., Guo, Y. D., & Song, H. L. (2026). Supervision of artificial intelligence-aided drug R&D by the U.S. FDA and its enlightenment. Chinese Journal of New Drugs and Clinical Remedies, 45(4), 257–263.
  11. Wang, G., & Deng, C. D. (2026). Value alignment of generative artificial intelligence: Generation logic, practical obstacles and systematic governance. Journal of Hainan University (Humanities & Social Sciences), 1–10.
  12. Duan, W. X. (2020). Research on liability risks of medical artificial intelligence [Master’s thesis, Tianjin Medical University].
  13. Sung, J. (2023). Artificial intelligence in medicine: Ethical, social and legal perspectives. Annals of the Academy of Medicine, Singapore, 52(12), 695–699.
  14. Wu, X. L., Wang, L. T., & Zeng, E. Q. (2026). Analysis of ethical dilemmas and governance paths of generative artificial intelligence in medical decision-making. Policy & Scientific Consult, (10), 17–21.