Integrating artificial intelligence with human reasoning in oncology: questions on real-world implementation and patient-centric evidence

Carlos M. Ardila; Anny Marcela Vivares-Builes; Eliana Pineda-Vélez

doi:10.1186/s40779-025-00663-7

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Integrating artificial intelligence with human reasoning in oncology: questions on real-world implementation and patient-centric evidence

COMMENTARY | Updated：2026-05-08

- Integrating artificial intelligence with human reasoning in oncology: questions on real-world implementation and patient-centric evidence
- Military Medical Research Vol. 13, Issue 3, Pages: 515-516(2026)
- Affiliations：
  
  1.Department of Periodontics, Saveetha Dental College, and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Saveetha 600077, India
  2.Basic Sciences Department, Biomedical Stomatology Research Group, Faculty of Dentistry, Universidad de Antioquia U de A, 050010 Medellín, Colombia
  3.Faculty of Dentistry, Institución Universitaria Visión de las Américas, 050040 Medellín, Colombia
- Author bio：
  
  * Carlos M. Ardila martin.ardila@udea.edu.co
- Funds：
- DOI：10.1186/s40779-025-00663-7
  CLC：
- Received：04 October 2025，
  
  Accepted：26 October 2025，
  
  Online First：04 November 2025，
  
  Published：2026-03
- Accepted：
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Carlos M. Ardila, Anny Marcela Vivares-Builes, Eliana Pineda-Vélez. Integrating artificial intelligence with human reasoning in oncology: questions on real-world implementation and patient-centric evidence[J]. Military Medical Research, 2026, 13(3): 515-516.
DOI：

Carlos M. Ardila, Anny Marcela Vivares-Builes, Eliana Pineda-Vélez. Integrating artificial intelligence with human reasoning in oncology: questions on real-world implementation and patient-centric evidence[J]. Military Medical Research, 2026, 13(3): 515-516. DOI： 10.1186/s40779-025-00663-7.

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Jiang YL , Zhao G , Wang SH , Li N . Leveraging artificial intelligence for clini‑cal decision support in personalized standard regimen recommendation for cancer . Mil Med Res . 2025 ; 12 ( 1 ): 31 .

Boehm KM , Khosravi P , Vanguri R , Gao J , Shah SP . Harnessing multi‑modal data integration to advance precision oncology . Nat Rev Cancer . 2022 ; 22 ( 2 ): 114 – 26 .

Sherman RE , Anderson SA , Dal Pan GJ , Gray GW , Gross T , Hunter NL , et al . Real‑world evidence ‑ what is it and what can it tell us? . N Engl J Med . 2016 ; 375 ( 23 ): 2293 – 7 .

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 ): e745 – 50 .

Shah MA , Oliver TK , Peterson DE , Einhaus K , Schneider BJ , Denduluri N , et al . Asco clinical practice guideline endorsements and adaptations . J Clin Oncol . 2020 ; 38 ( 8 ): 834 – 40 .

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