Within the healthcare sector, Artificial Intelligence (AI) has seen a substantial rise in development over the past years due to growing interest and its potential impact on healthcare delivery and effectiveness. Innovations supporting the digitization of health data have led to new opportunities and challenges in healthcare delivery. Challenges originate from the realization that the growth in digital health data is quickly exceeding the human capacity to process and analyze it in routine clinical practice. Specifically in medical image analysis, the increasing amount of imaging data generated by a range of modalities is becoming a bottleneck for diagnosis, therapy-planning and follow-up. Therefore, processing digital health data with AI could support the delivery of effective and efficient healthcare. Nonetheless, even though the advancement of AI carries much potential, what value AI can and will deliver in actual clinical practice remains a fundamental question. Therefore, this research aims to generate and substantiate evidence supporting clinical effectiveness, specifically comparative effectiveness, cost-effectiveness or other formal health technology assessment (HTA) of AI in a clinical healthcare setting in oncology.