The adoption of machine learning in healthcare requires models that not only perform well but also provide insights that practitioners can trust. FireGNN addresses this challenge by embedding fuzzy rules into GNNs, thus enabling a form of symbolic reasoning that is accessible to medical professionals. This framework leverages topological features to make predictions more transparent, a significant step forward in the realm of medical imaging.
Through extensive benchmarking, including five MedMNIST benchmarks and the synthetic MorphoMNIST dataset, FireGNN showcases strong predictive performance while generating interpretable rule-based explanations. Such transparency is critical as it reduces the risks associated with black-box models, allowing clinicians to understand and trust the AI’s reasoning. As healthcare continues to become more data-driven, the implications of enhancing model interpretability with such frameworks could lead to broader acceptance of AI technologies in clinical practices.
👉 Pročitaj original: arXiv AI Papers
