Innovative solutions in cancer diagnosis, therapy planning, interventions and outcomes can be achieved by pooling, linking, and using existing cancer patient imaging and other relevant data for the development of robust AI/ML-based algorithms and enhancing of image-guided tools in clinical settings. A key point underpinning the use of AI and ML in the fight against cancer is access to high quality data. Furthermore, there are limited recognised validation and performance evaluation frameworks for AI/ML-based diagnostic algorithms.

Proposals should also focus on understanding challenges and propose sustainable solutions to close gaps in algorithm validation and algorithm evaluation in the context of developing AI/ML-based tools for cancer diagnosis and outcome prediction. The proposal should aim to improve AI/ML-enabled imaging and image guided solutions in order to assist and guide clinicians during diagnosis, staging, patient monitoring, therapy planning, intervention and follow-up. Where appropriate, proposals should demonstrate novel ways to interact with the imaging data.

The proposed research and innovation (R&I) activities should result in simplified clinical workflows, for instance through enhanced or complementary robotic-assisted procedures, thus resulting in more precise therapeutic and interventional procedures for patients, reduced workload on staff, a reduction in therapy planning and intervention time, and shorter recovery times/hospital stays.