Applying the hallmarks of artificial intelligence to genitourinary medical oncology: an evidence-maturity framework for precision cancer care
5 affiliations
- 1Genitourinary Oncology Department, H. Lee Moffitt Cancer Center
- 2Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida
- 3Department of Genitourinary Oncology, Orlando Health Cancer Institute
- 4Internal Medicine Department, St Louis University
- 5Morsani College of Medicine, University of South Florida
Abstract
Artificial intelligence (AI) is increasingly reshaping genitourinary (GU) medical oncology across the cancer-care continuum. In this structured narrative review, we apply the ten-hallmark precision-oncology taxonomy proposed by Chang et al. to GU malignancies. We further define GU-specific hallmark boundaries and introduce study-design and validation descriptors that span proof-of-concept, internal and external validation, prospective clinical evaluation, and prospective interventional evaluation, while distinguishing evidence derived from randomized trials. Across prostate, urothelial, and kidney cancers, AI has shown promising technical performance in imaging, pathology, molecular inference, and outcome modeling. General-oncology trial-matching benchmarks offer additional research opportunities, although evidence remains uneven. Among the examples reviewed, validation designs highly vary, and strong discrimination should not be interpreted as evidence of clinical benefit. We therefore emphasize cohort size, validation design, head-to-head comparator evidence, calibration when reported, and implementation considerations including regulation, model drift, fairness, clinician oversight, cybersecurity, workflow integration, and cost. The next phase of AI in GU oncology will depend on demonstrating incremental clinical utility and patient benefit, not only higher accuracy.
Related
- Advancing Genitourinary Cancer Surgery: The Role of Artificial Intelligence and Robotics
- Editorial: Artificial intelligence and anti-cancer drugs: advancing discovery, personalization, and precision oncology
- Toward Precision Colorectal Cancer Screening: Integrating Clinical Risk Stratification, Molecular Biomarkers, and Artificial Intelligence
- Artificial Intelligence in Oncologic Imaging: Deep Learning, Radiomics, and Clinical Integration for Precision Cancer Diagnosis
- Clinical Translation of Artificial Intelligence in Oncology: Real-World Validation, Workflow Integration, and Precision Medicine Applications
BibTeX
@article{hatoum2026applying,
title = {{Applying the hallmarks of artificial intelligence to genitourinary medical oncology: an evidence-maturity framework for precision cancer care}},
author = {Firas Hatoum and Sola Maria Saleh and Mohamad Sadek Zoghbi and Arielle Pekofsky and Nadine Mahmoud and Jeffrey S. Johnson and Justin W. Miller and Casey Le and Hatem Hassanein and Barbara Broome and Raji Shameem and Philippe E. Spiess and Jad Chahoud},
journal = {Frontiers in Oncology},
year = {2026},
month = nov,
doi = {10.3389/fonc.2026.1961809},
url = {https://www.frontiersin.org/articles/10.3389/fonc.2026.1961809/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Applying the hallmarks of artificial intelligence to genitourinary medical oncology: an evidence-maturity framework for precision cancer care
AU - Firas Hatoum
AU - Sola Maria Saleh
AU - Mohamad Sadek Zoghbi
AU - Arielle Pekofsky
AU - Nadine Mahmoud
AU - Jeffrey S. Johnson
AU - Justin W. Miller
AU - Casey Le
AU - Hatem Hassanein
AU - Barbara Broome
AU - Raji Shameem
AU - Philippe E. Spiess
AU - Jad Chahoud
PY - 2026
DA - 2026/11/09
JO - Frontiers in Oncology
DO - 10.3389/fonc.2026.1961809
UR - https://www.frontiersin.org/articles/10.3389/fonc.2026.1961809/full
AB - Artificial intelligence (AI) is increasingly reshaping genitourinary (GU) medical oncology across the cancer-care continuum. In this structured narrative review, we apply the ten-hallmark precision-oncology taxonomy proposed by Chang et al. to GU malignancies. We further define GU-specific hallmark boundaries and introduce study-design and validation descriptors that span proof-of-concept, internal and external validation, prospective clinical evaluation, and prospective interventional evaluation, while distinguishing evidence derived from randomized trials. Across prostate, urothelial, and kidney cancers, AI has shown promising technical performance in imaging, pathology, molecular inference, and outcome modeling. General-oncology trial-matching benchmarks offer additional research opportunities, although evidence remains uneven. Among the examples reviewed, validation designs highly vary, and strong discrimination should not be interpreted as evidence of clinical benefit. We therefore emphasize cohort size, validation design, head-to-head comparator evidence, calibration when reported, and implementation considerations including regulation, model drift, fairness, clinician oversight, cybersecurity, workflow integration, and cost. The next phase of AI in GU oncology will depend on demonstrating incremental clinical utility and patient benefit, not only higher accuracy.
SN - 2234-943X
ER -