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Furthermore, our framework provides transparent, biologically grounded rationales for its routing decisions, bridging the gap between hig","title":"iLENS: Interpretable LLM-Guided Mixture-of-Experts for Neuroimaging Survival Analysis","url":"https://arxiv.org/abs/2607.08778","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.08778v1 Announce Type: cross \nAbstract: Alzheimer's Disease (AD) is a complex neurodegenerative disorder that continues to impact millions of people worldwide. Predicting AD conversion during the prodromal stage remains critical for disease understanding and patient care. As such, survival models are widely used for AD risk prediction, yet they are typically static predictors with limited interpretability and no capacity for natural language reasoning. In this work, we propose iLENS, an interpretable large language model (LLM) guided framework based on mixture-of-experts (MoE) for survival prediction in AD conversion. 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