iLENS: Interpretable LLM-Guided Mixture-of-Experts for Neuroimaging Survival Analysis
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.
- ▪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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Computer Science > Machine Learning arXiv:2607.08778 (cs) [Submitted on 12 Jun 2026] Title:iLENS: Interpretable LLM-Guided Mixture-of-Experts for Neuroimaging Survival Analysis Authors:Farica Zhuang, Seong Woo Han, Zixuan Wen, Shu Yang, Yize Zhao, Li Shen View a PDF of the paper titled iLENS: Interpretable LLM-Guided Mixture-of-Experts for Neuroimaging Survival Analysis, by Farica Zhuang and 5 other authors View PDF HTML (experimental) Abstract: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.
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