Explainable machine learning for leukaemia diagnosis: enhancing interpretability and trust

Abstract

One of the major challenges in applying machine learning (ML) to leukaemia diagnosis is the limited interpretability of deep learning models such as artificial neural networks (ANNs) and convolutional neural networks (CNNs). While these models excel at detecting complex patterns in medical data, they often function as “black boxes,” providing little transparency into their decision-making processes. This lack of explainability raises concerns in clinical practice, where understanding why a model predicts a specific leukaemia subtype is critical for guiding treatment decisions, including chemotherapy and bone marrow transplantation. To address these concerns, researchers are increasingly adopting explainable AI (XAI) techniques. Methods such as saliency maps, feature importance rankings, and attention mechanisms allow clinicians to visualize how specific factors such as genetic mutations, biomarkers, or cell morphology contribute to diagnostic outcomes. These interpretability tools not only enhance transparency but also foster trust among healthcare providers and patients. By bridging the gap between computational accuracy and clinical reliability, XAI ensures that ML models can be safely integrated into diagnostic workflows. Ultimately, explainable approaches strengthen confidence in AI-driven leukaemia diagnosis, supporting more informed medical decisions and improving patient care.