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Integrating artificial intelligence across the bladder cancer continuum: progress, promise, and pitfalls
Sri Saran Manivasagam1, Jay D Raman1, Alireza Aminsharifi1
1Department of Urology, Penn State Health Milton S. Hershey Medical Center, Pennsylvania State University, Hershey, PA, USA.
Expert Review of Anticancer Therapy
|December 14, 2025
Summary
Artificial intelligence (AI) shows promise in improving bladder cancer diagnosis and treatment. Challenges remain for clinical integration, but AI is expected to enhance precision oncology for bladder cancer care.
Area of Science:
- Urology
- Oncology
- Medical Imaging
- Pathology
- Computational Biology
Background:
- Bladder cancer presents significant challenges in early detection, staging, and personalized treatment.
- Artificial intelligence (AI) offers potential solutions to these limitations across the bladder cancer care continuum.
Purpose of the Study:
- To review and synthesize current applications of AI in bladder cancer management.
- To identify the potential benefits and challenges of AI adoption in clinical practice.
Main Methods:
- Comprehensive literature search of major databases (PubMed, MEDLINE, Embase, Scopus, Google Scholar) from 2005 to 2025.
- Inclusion of 49 studies focusing on AI applications in cystoscopy, radiology, pathology, biomarker profiling, treatment response, and survival prediction.
Main Results:
- AI demonstrates potential in enhancing diagnostic accuracy, reducing variability, and personalizing treatment strategies for bladder cancer.
- Studies explore AI for lesion detection, staging (CT/MRI), grading, biomarker analysis, and predicting treatment outcomes and survival.
Conclusions:
- AI shows significant promise for bladder cancer precision oncology but faces barriers to clinical adoption, including data quality, validation, EHR integration, and regulatory issues.
- Future research should focus on explainable AI, prospective validation, and demonstrating clinical and economic benefits for widespread integration.
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