An Explainable AI Framework for Treatment Failure Model for Oncology Patients

The black box nature of current AI models has raised serious concerns about accountability, bias and trust in the models that might undermine their relevance and usefulness in the field of medicine where human lives are at risk. AI in medicine has the ability to derive meaningful inferences from real world data – an emerging school of thought namely Real World Evidence (RWE) studies – that can assist medical practitioners to improve evidence based quality of care. In the field of oncology, the accuracy and performance of inference models are as important as clinically relevant and sound explanations of the inference. In this paper, we present an Explainable AI (XAI) framework for our AI model that predicts the suitability of a chemotherapy treatment at the time of its prescription based on RWE. The framework provides explanations both for a specific patient and also for the model. It provides explanations like feature analysis, counterfactual, and top risk factors that contribute to a treatment failure. As a result, the framework adds an explainability layer between treatment failure predictive model and oncologists, thereby enabling evidence based assistance to oncologists in designing chemotherapy plans.