Can Kolmogorov–Arnold Networks (KANs) improve medical image analysis—and how can we distinguish their contribution from improvements elsewhere in a model?
We address these questions in our new paper, “A review of Kolmogorov–Arnold networks for medical image analysis,” published in Computerized Medical Imaging and Graphics.
KANs introduce learnable univariate functions in place of selected scalar weights or fixed activations, offering a different approach to nonlinear representation learning. Our review examines 138 independent medical-imaging studies published from January 2024 to February 2026, covering segmentation, classification, detection, reconstruction, registration, image fusion, and image-derived outcome prediction.
What does our review offer?
• A practical architectural map: We organize KAN integration into four categories—backbone, bottleneck/token, physics-informed or implicit representation, and task-specific head—to clarify where and why these modules are used.
• A critical assessment of the evidence: We distinguish whole-model performance from the independent contribution of KAN. Although 68 studies reported favorable whole-model results, none met our strictest standard for a complete, resource-controlled factorial comparison.
• A framework for stronger evaluation: We outline priorities for matched ablations, external validation, calibration, function stability, and transparent reporting of latency and memory.
Our central finding: Current evidence supports KAN as a task-dependent module within established imaging pipelines. Its value should be demonstrated through controlled comparisons, and inspectable functions require further validation before they can support claims of clinical explainability.
For researchers developing KAN-based medical imaging methods, this review provides an evidence map, a complete supplementary study matrix, and practical guidance for designing more reproducible experiments.
📄 Read and cite the paper:
Bai, J., Cai, Z., Yang, R., Lekadir, K., & Li, S. (2026). A review of Kolmogorov–Arnold networks for medical image analysis. Computerized Medical Imaging and Graphics, 136, 102827.
https://doi.org/10.1016/j.compmedimag.2026.102827
We welcome discussion and collaboration on rigorous benchmarking and clinically meaningful evaluation of KAN-based models.
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