This study presents insights gathered from surveys and discussions with specialists in three domains, aiming to find essential elements for a universal explanation framework that could be applied to these and other similar use cases. The insights are incorporated into a software tool that utilizes GP algorithms, known for their interpretability. The applications analyzed include a medical scenario (involving predictive ML), a retail use case (involving prescriptive ML), and an energy use case (also involving predictive ML). We interviewed professionals from each sector, transcribing their conversations for further analysis. Additionally, experts and non-experts in these fields filled out questionnaires designed to probe various dimensions of explanatory methods. The findings indicate a universal preference for sacrificing a degree of accuracy in favor of greater explainability. Additionally, we highlight the significance of feature importance and counterfactual explanations as critical components of such a framework. Our questionnaires are publicly available to facilitate the dissemination of knowledge in the field of XAI.

Volume measurement of a paraganglioma (a rare neuroendocrine tumor that typically forms along major blood vessels and nerve pathways in the head and neck region) is crucial for monitoring and modeling tumor growth in the long term. However, in clinical practice, using available tools to do these measurements is time-consuming and suffers from tumor-shape assumptions and observer-to-observer variation. Growth modeling could play a significant role in solving a decades-old dilemma (stemming from uncertainty regarding how the tumor will develop over time). By giving paraganglioma patients treatment, severe symptoms can be prevented. However, treating patients who do not actually need it, comes at the cost of unnecessary possible side effects and complications. Improved measurement techniques could enable growth model studies with a large amount of tumor volume data, possibly giving valuable insights into how these tumors develop over time. Therefore, we propose an automated tumor volume measurement method based on a deep learning segmentation model using no-new-UNnet (nnUNet). We assess the performance of the model based on visual inspection by a senior otorhinolaryngologist and several quantitative metrics by comparing model outputs with manual delineations, including a comparison with variation in manual delineation by multiple observers. Our findings indicate that the automatic method performs (at least) equal to manual delineation. Finally, using the created model, and a linking procedure that we propose to track the tumor over time, we show how additional volume measurements affect the fit of known growth functions.