References
1. Topol EJ. High-performance: the convergence of
human and artificial intelligence. Nat Med. 2019: 25:
44–56.
2.Obermeyer Z, Powers B, Vogeli C, Mullainathan S.
Dissecting racial bias in a clinical prediction
algorithm. Science. 2019;366(6464):447-53.
3. Esteva A, Kuprel B, Novoa RA, et al. Dermatologistlevel classification of skin cancer with deep neural
networks. Nature. 2017;542(7639):115-8.
4. Wang H, Chen Y, Zhang L. Artificial intelligence in
medical education: A bibliometric analysis of research
trends and hotspots (2000-2024). Med Educ Online.
2024;29(1):2356789.
5. Ghassemi M, Oakden-Rayner L, Beam AL. The false
hope of current approaches to explainable artificial
intelligence in health care. Lancet Digit Health.
2021;3(11):745-50.
6. Obermeyer Z, Powers B, Vogeli C, Mullainathan S.
Dissecting racial bias in an algorithm used to manage
the health of populations. Science.
2019;366(6464):447-53.
7. Vayena E, Blasimme A, Cohen IG. Machine learning
in medicine: Addressing ethical challenges. PLoS
Med. 2018;15(11): e1002689.
8. Rajkomar A, Hardt M, Howell MD, Corrado G, Chin
MH. Ensuring fairness in machine learning to advance
health equity. Ann Intern Med. 2018;169(12):866-72.
9. Kumar A, Krishnamurthi R, Nayyar A, et al. Artificial
intelligence bias in medical system designs: a
systematic review. Multimed Tools Appl.
2024;83:18005-57.
10. Burlina P, Joshi N, Paul W, Pacheco KD, Bressler
NM. Addressing artificial intelligence bias in retinal
diagnostics. Transl Vis Sci Technol. 2021;10(2):13.