title: An Explainable Diagnostic Framework for Neurodegenerative Dementias via
    Reinforcement-Optimized LLM Reasoning
summary: The differential diagnosis of neurodegenerative dementias is a challenging
  clinical task, mainly because of the overlap in symptom presentation and the
  similarity of patterns observed in structural neuroimaging. To improve
  diagnostic efficiency and accuracy, deep learning-based methods such as
  Convolutional Neural Networks and Vision Transformers have been proposed for
  the automatic classification of brain MRIs. However, despite their strong
  predictive performance, these models find limited clinical utility due to their
  opaque decision making. In this work, we propose a framework that integrates
  two core components to enhance diagnostic transparency. First, we introduce a
  modular pipeline for converting 3D T1-weighted brain MRIs into textual
  radiology reports. Second, we explore the potential of modern Large Language
  Models (LLMs) to assist clinicians in the differential diagnosis between
  Frontotemporal dementia subtypes, Alzheimer's disease, and normal aging based
  on the generated reports. To bridge the gap between predictive accuracy and
  explainability, we employ reinforcement learning to incentivize diagnostic
  reasoning in LLMs. Without requiring supervised reasoning traces or
  distillation from larger models, our approach enables the emergence of
  structured diagnostic rationales grounded in neuroimaging findings. Unlike
  post-hoc explainability methods that retrospectively justify model decisions,
  our framework generates diagnostic rationales as part of the inference
  process-producing causally grounded explanations that inform and guide the
  model's decision-making process. In doing so, our framework matches the
  diagnostic performance of existing deep learning methods while offering
  rationales that support its diagnostic conclusions.
paper_authors._id: 6836ffd1ec0f4b6889526488
paper_authors.name: Andrew Zamai
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-28T12:26:16.791Z
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paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/65be17eb52edc430281c6a72/ZeSxgA25KnJ8pqvvy6rdl.png
paper_authors.fullname: Andrew Zamai
paper_authors.type: user
paper_authors.user: andrewzamai
paper_authors._id: 6836ffd1ec0f4b6889526489
paper_authors.name: Nathanael Fijalkow
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-28T12:26:22.686Z
paper_authors._id: 64897d743262c9f1566efb22
paper_authors.avatarUrl: /avatars/daa78961fe34d598ae5030efb6f431f8.svg
paper_authors.fullname: Nathanaël Fijalkow
paper_authors.type: user
paper_authors.user: nathanael-fijalkow
paper_authors._id: 6836ffd1ec0f4b688952648a
paper_authors.name: Boris Mansencal
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-28T12:26:28.609Z
paper_authors._id: 67924a56c9b9adc06d1f51a4
paper_authors.avatarUrl: /avatars/41d032829ed112f0b56a76c4f7d5861b.svg
paper_authors.fullname: BorisMansencal
paper_authors.type: user
paper_authors.user: BorisMansencal
paper_authors._id: 6836ffd1ec0f4b688952648b
paper_authors.name: Laurent Simon
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-28T12:26:35.638Z
paper_authors._id: 664c867efc66a5a3eee5f16a
paper_authors.avatarUrl: /avatars/913aacea32932e54b9144a69995e6733.svg
paper_authors.fullname: Laurent Simon
paper_authors.type: user
paper_authors.user: lcysimon
paper_authors._id: 6836ffd1ec0f4b688952648c
paper_authors.name: Eloi Navet
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-28T12:26:41.553Z
paper_authors._id: 6641e54c24555ed542945a5a
paper_authors.avatarUrl: /avatars/a04a19a3bd2aad05acec7e675e597415.svg
paper_authors.fullname: Eloi Navet
paper_authors.type: user
paper_authors.user: eloinavet
paper_authors._id: 6836ffd1ec0f4b688952648d
paper_authors.name: Pierrick Coupe
paper_title: An Explainable Diagnostic Framework for Neurodegenerative Dementias via
    Reinforcement-Optimized LLM Reasoning
paper_summary: The differential diagnosis of neurodegenerative dementias is a challenging
  clinical task, mainly because of the overlap in symptom presentation and the
  similarity of patterns observed in structural neuroimaging. To improve
  diagnostic efficiency and accuracy, deep learning-based methods such as
  Convolutional Neural Networks and Vision Transformers have been proposed for
  the automatic classification of brain MRIs. However, despite their strong
  predictive performance, these models find limited clinical utility due to their
  opaque decision making. In this work, we propose a framework that integrates
  two core components to enhance diagnostic transparency. First, we introduce a
  modular pipeline for converting 3D T1-weighted brain MRIs into textual
  radiology reports. Second, we explore the potential of modern Large Language
  Models (LLMs) to assist clinicians in the differential diagnosis between
  Frontotemporal dementia subtypes, Alzheimer's disease, and normal aging based
  on the generated reports. To bridge the gap between predictive accuracy and
  explainability, we employ reinforcement learning to incentivize diagnostic
  reasoning in LLMs. Without requiring supervised reasoning traces or
  distillation from larger models, our approach enables the emergence of
  structured diagnostic rationales grounded in neuroimaging findings. Unlike
  post-hoc explainability methods that retrospectively justify model decisions,
  our framework generates diagnostic rationales as part of the inference
  process-producing causally grounded explanations that inform and guide the
  model's decision-making process. In doing so, our framework matches the
  diagnostic performance of existing deep learning methods while offering
  rationales that support its diagnostic conclusions.
paper_ai_summary: A framework using modular pipelines and reinforcement learning enhances the diagnostic clarity of deep learning models for neurodegenerative dementias by generating causally grounded explanations.
paper_ai_keywords: Convolutional Neural Networks Vision Transformers brain MRIs Frontotemporal dementia Alzheimer's disease Large Language Models reinforcement learning modular pipeline