Data canyons were the applied machine learning white-box model because they provide a graphical explanation layer that can be easily understood and interpreted. Additionally, Data canyons allow easy manipulation of attributes and their inclusion or restriction while offering a clear instance-based visualization of instance alignment. Medicine presents a field of application of machine learning algorithms where there is a great need for XAI because of the high-risk decision-making nature of the domain. Providing that the end user of the agile framework is an expert in the domain of application, the framework presents a great potential for creating new expert-infused machine learning models. All three presented parts are equally important for the development of an agile machine learning framework. Without an intuitive and transparent white-box model, the expert cannot easily understand and manipulate the model. If the field of application has no inherent need for transparency and the decision-making process has low risks associated with it, there is no need for white-box machine learning algorithms or agile expert-infused models. Lastly, if the user is not a domain expert, his knowledge and experiences cannot be infused into the final model. The field of XAI is gradually gaining traction, as evidenced by the increasing number of publications in recent years. A literature search conducted on Scopus using the term \"explainable artificial intelligence\" revealed that the most relevant subject area for its application is medicine, preceded by computer science, engineering, and mathematics, which primarily focus on method development. Within the domain of medicine, a significant majority (over 90%) of the publications have been published within the last 3 years, indicating a current and pressing interest in this area. Notably, one highly cited article has expressed concerns regarding the limitations of existing XAI approaches [13], while other relevant articles have primarily focused on showcasing XAI applications in medicine. However, the inclusion of the term \"agile\" in the search string did not yield any results, suggesting a lack of research on agile frameworks for XAI in the context of medicine. This literature survey highlights the currency and relevance of XAI in medicine and indicates the need for further advancements and novel approaches in this domain, including the exploration of agile methodologies. The aim of this paper is to present the process of continuous improvement of a novel white-box machine learning algorithm, namely Data canyons, using agile medical expert interaction. Data canyons allow for a unique presentation of the created model that enables experts to interpret the model as such and manipulate it based on their own understanding and expert knowledge in order to better understand the dynamics of the attributes interaction and to infuse their knowledge that lies outside of the boundaries of the used data. This approach combines XAI's objectives and agile development principles to form a symbiotic human-computer machine learning agent. In this test scenario, a labeled dataset using patient bloodwork obtained during infarction and reinfarction was used to create a model to classify the possibility of reinfarction.