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Artificial Intelligence in Oncology - Supporting scientific research
In short, Paul van Diest's research entails the following:
AI has shown great promise in improving speed and quality of pathology diagnostics. However, implementation of AI is yet lacking because of a problematic business case: license costs for AI algorithms on top of an expensive fully digital diagnostics workflow cannot easily be paid, requiring tangible cost savings by AI.
In this project, we run two prospective clinical trials in daily pathology practice that will, for the first time, make clear to which extent two commercially available clinical grade AI algorithms for detection of prostate cancer and metastases in breast cancer sentinel lymph nodes lead to tangible cost savings by obviating immunohistochemistry. In the control arm, cases will be reviewed as usual, applying immunohistochemistry-or-not at the discretion of the pathologist. In the intervention arm, pathologists will assess cases after the algorithm has processed these. We expect pathologists to need less immunohistochemistry to detect/reliably measure tumor load of prostate cancer and find SN metastases in breast cancer.
With the expected reduction in immunohistochemistry, we hope to show that AI implementation and running costs can be earned back in 1-2 years. These trials will help to build the business case for implementation of these AI algorithms in clinical pathology practice, and thereby close the translational gap.