Digital pathology enables using AI for improved cancer diagnostics and ultimately better treatment decisions. We intend to develop a clinical-grade prognostic AI system for prostate cancer pathology by integrating: 1) Large-scale datasets of digitized prostate biopsies from a network of health care providers, 2) Cohorts with follow-up for clinical outcomes, 3) Genomic profiling of the samples, 4) Methods for calibration of scanner instruments, 5) Methods for estimating the reliability of predictions and 6) Methodology for deploying the algorithm on low-cost, portable scanners to also cover resource-constrained clinical settings. This will allow extending prognostication beyond human capacity by directly predicting disease progress from images and by integrating molecular data with tissue morphology. Utilizing diverse international data and calibration methods will allow training robust models and evaluating them in view of real-world sources of variation. Moreover, estimating the reliability of predictions is key for handling outliers and artefacts which are unavoidable in real-world use of diagnostic AI, and for providing useful feedback to the medical experts using the software in the clinic. Once the system design and training is complete, we plan on validating the resulting AI solution in a real-world environment by conducting a clinical pilot study (2023), followed by a clinical trial (2024). This will initially focus on diagnosis and grading of prostate cancer, and direct prognostication of outcome in the second phase (2024-2025). We have started preparatory work for doing this at Karolinska University Hospital.