An Explainable Diagnostic Framework for Neurodegenerative Dementias via
  Reinforcement-Optimized LLM Reasoning
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.
6836ffd1ec0f4b6889526488
Andrew Zamai
admin_assigned
2025-05-28T12:26:16.791Z
65be17eb52edc430281c6a72
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Andrew Zamai
user
andrewzamai
6836ffd1ec0f4b6889526489
Nathanael Fijalkow
admin_assigned
2025-05-28T12:26:22.686Z
64897d743262c9f1566efb22
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Nathanaël Fijalkow
user
nathanael-fijalkow
6836ffd1ec0f4b688952648a
Boris Mansencal
admin_assigned
2025-05-28T12:26:28.609Z
67924a56c9b9adc06d1f51a4
/avatars/41d032829ed112f0b56a76c4f7d5861b.svg
BorisMansencal
user
BorisMansencal
6836ffd1ec0f4b688952648b
Laurent Simon
admin_assigned
2025-05-28T12:26:35.638Z
664c867efc66a5a3eee5f16a
/avatars/913aacea32932e54b9144a69995e6733.svg
Laurent Simon
user
lcysimon
6836ffd1ec0f4b688952648c
Eloi Navet
admin_assigned
2025-05-28T12:26:41.553Z
6641e54c24555ed542945a5a
/avatars/a04a19a3bd2aad05acec7e675e597415.svg
Eloi Navet
user
eloinavet
6836ffd1ec0f4b688952648d
Pierrick Coupe
An Explainable Diagnostic Framework for Neurodegenerative Dementias via
  Reinforcement-Optimized LLM Reasoning
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.
A framework using modular pipelines and reinforcement learning enhances the diagnostic clarity of deep learning models for neurodegenerative dementias by generating causally grounded explanations.
Convolutional Neural Networks Vision Transformers brain MRIs Frontotemporal dementia Alzheimer's disease Large Language Models reinforcement learning modular pipeline