Multimodal artificial intelligence for prostate cancer imaging: workflow-relevant fusion of mpMRI, PSMA PET, ultrasound, and clinical data for diagnosis, local staging, and treatment personalization

The authors aim to explore how multimodal artificial intelligence can enhance prostate cancer imaging by integrating various data sources, including mpMRI, PSMA PET, ultrasound, and clinical information, to improve diagnosis, local staging, and treatment personalization. They highlight the advantages of these integrated approaches over single-modality AI tools, demonstrating improved triage, targeted biopsy accuracy, and risk assessment for treatment outcomes. The study advocates for the adoption of modular multimodal systems that enhance clinical decision-making in abdominal radiology.