Artificial intelligence-driven digital pathology in urological cancers: current trends and future directions

Area of Science:

• Digital pathology
• Artificial intelligence
• Urological oncology

Background:

• AI is increasingly explored for its potential in diagnosing and managing urological cancers.
• Digital pathology provides the foundation for AI applications in this field.

Purpose of the Study:

• To review the applications and challenges of AI in three major urological cancers: prostate, renal, and bladder cancer.
• To identify current limitations and future directions for AI in urological cancer research.

Main Methods:

• Literature review of studies on AI in prostate, renal, and bladder cancer.
• Analysis of AI's diagnostic performance, prognosis prediction, generalizability, and data requirements.

Main Results:

• Prostate cancer AI studies show reliable diagnostic performance and prognosis prediction.
• Renal cancer AI shows potential but faces generalizability and prognosis challenges.
• Bladder cancer AI research is limited by small datasets.

Conclusions:

• AI demonstrates significant potential in urological cancer diagnosis and management.
• Key challenges include data availability, prognosis accuracy, and generalizability across different datasets.
• Future research should focus on multimodal data, larger datasets, and multi-institutional collaborations to advance AI in urological oncology.