Artificial Intelligence in Medicine: An Ethical Assessment of Developments and Challenges

Authors

Department of Law, Lahijan Branch, Islamic Azad University, Lahijan, Iran

10.22081/jare.2025.70650.1997

Abstract

Artificial intelligence (AI) as a transformative technology has had a profound impact on contemporary medicine and has led to fundamental changes in diagnostic, therapeutic, and management processes. This transformation, while improving the accuracy and efficiency of health systems, has also brought with it complex ethical issues.Key questions include the impact of AI on physician autonomy, accountability for diagnostic errors, protection of patient privacy, and equity in access to advanced technologies. This study examines the ethical challenges associated with the application of AI in the medical field.Findings show that despite significant benefits, such as increased diagnostic accuracy and reduced human error, there are numerous ethical concerns such as algorithmic bias, weakening of doctor-patient trust, and security risks associated with health data.In addition, the entry of AI into the medical field has led to fundamental changes in the doctor-patient relationship that may lead to the "digitalization of trust" and a reduction in the role of the doctor in decision-making.
The results suggest that for the ethical use of AI, it is essential to develop strict regulatory frameworks, transparent algorithms, observe fairness in the distribution of technology, and protect patients' rights. The development of these technologies should be carried out in partnership with doctors, patients, and policymakers to avoid ethical challenges

Keywords

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References

  1. References

    1. Abbasi, M. & Teymoori, M. (2023). “A Review of Ethical and Legal Challenges in the Application of Artificial Intelligence in the Healthcare System”. 17(48): 1-11.
    2. Amiri, M. et al. (2021). “Artificial intelligence in healthcare: Opportunities and challenges in low-income countries”. Global Health Journal, 5(1): 12-18.
    3. Bates, D. W. et al. (2020). “Artificial intelligence in hospital management: Opportunities and challenges”. New England Journal of Medicine, 383(10): 930-938.
    4. Biller-Andorno, N. & Biller, A. (2019). “Algorithm-aided prediction of patient preferences-An ethics sneak peek”. New England Journal of Medicine, 381(15): 1480-1485. https://doi.org/10.1056/NEJMms1904152
    5. Cath, C. et al. (2020). “Artificial intelligence and the ‘good society’: The US, EU, and UK approach”. Science and Public Policy, 47(1): 12-20.
    6. Char, D. S. et al. (2020). “Implementing machine learning in health care-Addressing ethical challenges”. New England Journal of Medicine, 382(11): 100-105.
    7. Cohen, I. G. et al. (2021). “Big data and privacy in healthcare: Ethical implications”. Nature Medicine, 27(2): 308-314.
    8. Deutscher, Ethikrat (2017). Big data and health-Data sovereignty as the shaping of informational freedom. Berlin: Deutscher Ethikrat.
    9. Ebrahimnejad, P., Amirkhanlou, M. S. & Shaki, F. (2023). “Applications of artificial intelligence in pharmacy education: Legal and ethical challenges”. Journal of Mazandaran University of Medical Sciences, 33(227): 174-186.
    10. Eghtedar, S., Mesgarzadeh, M. & Aparnak, F. S. (2023). “Artificial intelligence in nursing and midwifery care: A new solution or new ethical challenges?”. Journal of Nursing and Midwifery, 21(4): 272-276.
    11. Esteva, A. et al. (2021). “Deep learning for dermatological diagnosis: A review”. Lancet Digital Health, 3(1): 65-72.
    12. Ficuciello, F. et al. (2022). “AI-enhanced robotic surgery: Precision and ethics”. IEEE Transactions on Medical Robotics, 4(2): 85-93.
    13. Hartmann, F. (1984). Patient, Arzt und Medizin: Beiträge zur ärztlichen Anthropologie. Berlin: Springer.
    14. Hashizume, M. & Kishida, A. (2022). “Roles of artificial intelligence in surgery in the era of computer-integrated surgery”. Journal of Hepato-Biliary-Pancreatic Sciences, 29(1): 10-19.
    15. Holzinger, A. et al. (2022). “Explainable AI in healthcare: Bridging the trust gap”. Artificial Intelligence in Medicine, 125: 10-18.
    16. Hosny, A. et al. (2018). “Artificial intelligence in radiology”. Nature Reviews Cancer, 18(8): 500-510.
    17. İlkılıç, İ. & Kucur, C. (2019). Hasta mahremiyeti. Ankara: Nobel Akademik Yayıncılık.
    18. Karches, K. E. (2018). “Against the iDoctor: Why artificial intelligence should not replace physician judgment”. Theoretical Medicine and Bioethics, 39(2): 91-110. https://doi.org/10.1007/s11017-018-9446-3
    19. Klang, E. et al. (2021). “AI-based decision support systems in emergency medicine: A retrospective analysis”. Journal of Medical Systems, 45(12): 120-128.
    20. London, A. J. (2022). “Artificial intelligence in medicine: Overcoming or recapitulating structural challenges to improving patient care?”. Cell Reports Medicine, 3(5): 1-8.
    21. Marchetti, M. A. et al. (2018). “Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images”. Journal of the American Academy of Dermatology, 78(2): 270-277.e1. https://doi.org/10.1016/j.jaad.2017.08.016
    22. Obermeyer, Z. et al. (2019). “Dissecting racial bias in an algorithm used to manage the health of populations”. Science, 366(6464): 447-453.
    23. Rajkomar, A. et al. (2019). “Ensuring fairness in machine learning to advance health equity”. Annals of Internal Medicine, 171(6): 417-423.
    24. Russell, S. & Norvig, P. (2020). Artificial intelligence: A modern approach. 4th edition. Boston: Pearson.
    25. Sarrafzadeh, S. & Aboutaleb, E. (2023). “The importance of ethics in using artificial intelligence in medical education”. Research in Medical Science Education, 15(2): 1-14.
    26. Shademan, A. et al. (2020). “Autonomous surgery: The role of AI and robotics”. Annals of Surgery, 271(1): 20-25.
    27. Siciliano, B. et al. (2020). “Ethics and liability in AI-driven surgery”. Journal of Robotic Surgery, 14(1): 30-36.
    28. Somashekhar, S. P. et al. (2018). “Watson for Oncology and breast cancer treatment recommendations”. Journal of Clinical Oncology, 36(4): 385-390.
    29. Topol, E. J. (2019). “High-performance medicine: The convergence of human and artificial intelligence”. Nature Medicine, 25(1): 44-56.
    30. Vayena, E. et al. (2022). “Health data governance in the age of artificial intelligence”. Nature Medicine, 28(1): 76-82.

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History

  • Receive Date: 30 December 2024
  • Revise Date: 30 May 2025
  • Accept Date: 21 May 2025

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