KANEx: Translating Kolmogorov-Arnold Networks' Interpretability to Medical Explainability
Computer vision models have become highly effective for medical applications, yet their black-box nature continues to undermine clinician trust. In clinical workflows, chest X-ray classifiers are increasingly paired with Vision-Language Models (VLMs) to generate natural-language explanations. However, these systems add linguistic fluency without addressing the underlying opacity of the visual model. With the emergence of Kolmogorov-Arnold Networks (KANs), whose spline-based components provide in
Record details
Published: 27 July 2026
Source: arXiv cs.AI
Category: Research
Topics: Safety & alignment · Healthcare · Transparency
Retrieved: 28 July 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
LEX-EC: A Lexical Evidence-Channel Audit Framework for Zero-Shot LLM Personality Classification in Black-Box Settings
arXiv cs.AI · 27 July 2026
Enhancing Explainable Cardiac Diagnosis with Guide-Grounded Multimodal LLMs
arXiv · 23 July 2026
xMICD: Explainable Representation of Multiple ICD Codes
arXiv cs.LG · 2 August 2026
MOF-Sleuth: Tool-Grounded Reward Alignment for Explainable Fine-Grained MOF CIF Auditing
arXiv · 22 July 2026
Beyond Simulations: What 20,000 Real Conversations Reveal About Mental Health AI Safety
arXiv cs.CY · 5 August 2026
Self-Evolving Human-Centered Framework for Explainable Depression Symptom Annotation
arXiv cs.HC · 16 July 2026
How to cite this record
ethics.ai (27 July 2026), “KANEx: Translating Kolmogorov-Arnold Networks' Interpretability to Medical Explainability,” evidence record 14028, https://ethics.ai/record/14028 (originally published by arXiv cs.AI).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.