Endocrinologist at a crossroads: professional obsolescence challenged by artificial intelligence

Author contributions

Xuelian Xiong (Conceptualization, Data curation, Formal analysis, Investigation, Resources, Software, Supervision, Visualization, Writing—original draft, Writing—review & editing).

Conflict of interest

None declared.

References

[1] Jumper J, Evans R, Pritzel A et al. Highly accurate protein structure prediction with AlphaFold. Nature 2021;596:583-9. https://doi.org/10.1038/s41586-021-03819-2

[2] Gulshan V, Peng L, Coram M et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 2016;316:2402-10. https://doi.org/10.1001/jama.2016.17216

[3] Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med 2019;380:1347-58. https://doi.org/10.1056/NEJMra1814259

[4] Li J, Guan Z, Wang J et al. Integrated image-based deep learning and language models for primary diabetes care. Nat Med 2024;30:2886-96. https://doi.org/10.1038/s41591-024-03139-8

[5] Wang G, Liu X, Ying Z et al. Optimized glycemic control of type 2 diabetes with reinforcement learning: a proof-of-concept trial. Nat Med 2023;29:2633-42. https://doi.org/10.1038/s41591-023-02552-9

[6] Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med 2019;25:44-56. https://doi.org/10.1038/s41591-018-0300-7

[7] Verghese A, Shah NH, Harrington RA. What this computer needs is a physician: humanism and artificial intelligence. JAMA 2018;319:19-20. https://doi.org/10.1001/jama.2017.19198