Challenges of Medical Ethics in the Era of Artificial Intelligence (AI)

Main Article Content

Siti Lukmanah

Abstract

Background: The rapid development of the digital world has penetrated various sectors of human life, including the world of health. The emergence of AI innovation has become a real form of technological innovation designed to help humans solve various problems including decision making, providing recommendations, and even providing advice based on processing available data. In the world of medical ethics, AI raises concerns about bias, injustice, data privacy, patient protection, and informed consent. This research aims to identify and analyze the challenges of medical ethics caused by the spread of AI and provide recommendations for medical ethics in the use of AI. Methods: This research uses a qualitative approach through literature analysis by collecting secondary data sourced from scientific journals, books, articles. Result: From this study it can be concluded that the use of AI provides many benefits and provides many challenges. In the world of medical ethics, AI provides its own challenges to answer ethical issues in the future, especially regarding patient data privacy and security, transparency and accountability, bias and fairness. Conclusion: Therefore, it is necessary to design a new strategy to help navigate the transition to AI-based healthcare, create regulations and create clear minimum standards or laws to report the responsibilities of all stakeholders in the event of certain errors in healthcare cases, and design specific methods to integrate ethical standards into the AI design system.


 

Article Details

Section
Articles

References

1. Alowais SA. Revolutionizing healthcare: the role of artificial intelligence in clinical practice [Internet]. Vol. 23, BMC Medical Education. 2023. Available from: https://api.elsevier.com/content/article/eid/1-s2.0-S1472692023004303

2. Esmaeilzadeh P. Challenges and strategies for wide-scale artificial intelligence (AI) deployment in healthcare practices: A perspective for healthcare organizations. Artif Intell Med [Internet]. 2024;151. Available from: https://api.elsevier.com/content/article/eid/1-s2.0-S0933365724001039

3. Elendu C. Ethical implications of AI and robotics in healthcare: A review [Internet]. Vol. 102, Medicine United States. 2023. Available from: https://api.elsevier.com/content/article/eid/1-s2.0-S0025797421005285

4. Khan MM. Towards secure and trusted AI in healthcare: A systematic review of emerging innovations and ethical challenges [Internet]. Vol. 195, International Journal of Medical Informatics. 2025. Available from: https://api.elsevier.com/content/article/eid/1-s2.0-S138650562400443X

5. Zhang J. Ethics and governance of trustworthy medical artificial intelligence. BMC Med Inform Decis Mak [Internet]. 2023;23(1). Available from: https://api.elsevier.com/content/article/eid/1-s2.0-S1472694723001747

6. Mittal S. On responsible machine learning datasets emphasizing fairness, privacy and regulatory norms with examples in biometrics and healthcare. Nat Mach Intell [Internet]. 2024;6(8):936–49. Available from: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85201289171&origin=inward

7. Chauhan A. Evaluating trustworthiness in AI-Based diabetic retinopathy screening: addressing transparency, consent, and privacy challenges. BMC Med Ethics [Internet]. 2025;26(1). Available from: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=105019113702&origin=inward

8. Naik N. Legal and Ethical Consideration in Artificial Intelligence in Healthcare: Who Takes Responsibility? Front Surg [Internet]. 2022;9. Available from: https://api.elsevier.com/content/article/eid/1-s2.0-S2296875X22001765

9. Kiseleva A. Transparency of AI in Healthcare as a Multilayered System of Accountabilities: Between Legal Requirements and Technical Limitations [Internet]. Vol. 5, Frontiers in Artificial Intelligence. 2022. Available from: https://api.elsevier.com/content/article/eid/1-s2.0-S2624821222001016

10. Nouis SCE. Evaluating accountability, transparency, and bias in AI-assisted healthcare decision- making: a qualitative study of healthcare professionals’ perspectives in the UK. BMC Med Ethics [Internet]. 2025;26(1). Available from: https://api.elsevier.com/content/article/eid/1-s2.0-S1472693925000014

11. Chen RJ. Algorithmic fairness in artificial intelligence for medicine and healthcare. Nat Biomed Eng [Internet]. 2023;7(6):719–42. Available from: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85163638540&origin=inward

12. Giovanola B. Beyond bias and discrimination: redefining the AI ethics principle of fairness in healthcare machine-learning algorithms. AI Soc [Internet]. 2023;38(2):549–63. Available from: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85130223028&origin=inward

13. Wang Y. Ethical and legal challenges of medical AI on informed consent: China as an example. Dev World Bioeth [Internet]. 2025;25(1):46–54. Available from: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85190860549&origin=inward

14. Chau M. Ethical, legal, and regulatory landscape of artificial intelligence in Australian healthcare and ethical integration in radiography: A narrative review [Internet]. Vol. 55, Journal of Medical Imaging and Radiation Sciences. 2024. Available from: https://api.elsevier.com/content/article/eid/1-s2.0-S1939865424004648

15. Micco F De. Robotics and AI into healthcare from the perspective of European regulation: who is responsible for medical malpractice? Front Med [Internet]. 2024;11. Available from: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85204704632&origin=inward

16. Blackman J. On the practical, ethical, and legal necessity of clinical Artificial Intelligence explainability: an examination of key arguments. BMC Med Inform Decis Mak [Internet]. 2025;25(1). Available from: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=86000107227&origin=inward

17. Economou-Zavlanos NJ. Translating ethical and quality principles for the effective, safe and fair development, deployment and use of artificial intelligence technologies in healthcare. J Am Med Informatics Assoc [Internet]. 2024;31(3):705–13. Available from: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85185342697&origin=inward

18. Bouderhem R. Shaping the future of AI in healthcare through ethics and governance. Humanit Soc Sci Commun [Internet]. 2024;11(1). Available from: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85187899065&origin=inward

19. Aquino YSJ. Practical, epistemic and normative implications of algorithmic bias in healthcare artificial intelligence: A qualitative study of multidisciplinary expert perspectives. J Med Ethics [Internet]. 2025;51(6):420–8. Available from: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85152709771&origin=inward

20. Flores L. Addressing bias in artificial intelligence for public health surveillance. J Med Ethics [Internet]. 2023;50(3):190–4. Available from: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85159161897&origin=inward

21. Weissglass DE. Contextual bias, the democratization of healthcare, and medical artificial intelligence in low- and middle-income countries. Bioethics [Internet]. 2022;36(2):201–9. Available from: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85113823239&origin=inward

22. Pham T. Ethical and legal considerations in healthcare AI: Innovation and policy for safe and fair use [Internet]. Vol. 12, Royal Society Open Science. 2025. Available from: https://api.elsevier.com/content/article/eid/1-s2.0-S2054570324002728

23. Goktas P. Shaping the Future of Healthcare: Ethical Clinical Challenges and Pathways to Trustworthy AI. J Clin Med [Internet]. 2025;14(5). Available from: https://api.elsevier.com/content/article/eid/1-s2.0-S2077038325011488

24. Corfmat M. High-reward, high-risk technologies? An ethical and legal account of AI development in healthcare [Internet]. Vol. 26, BMC Medical Ethics. 2025. Available from: https://api.elsevier.com/content/article/eid/1-s2.0-S1472693924001414