A Monosemantic Attribution Framework for Stable Interpretability in Clinical Neuroscience Transformer-Based Language Models
arXiv:2601.17952v2 Announce Type: replace Abstract: Interpretability remains a key challenge for deploying language models (LM) in clinical settings such as progression diagnosis of Alzheimer disease, where early and trustworthy predictions are essential. Existing attribution methods exhibit high inter-method variability and unstable explanations due to the polysemantic nature of Transformer-Based LM and LLM representations, while mechanistic interpretability approaches lack direct alignment with model inputs and outputs and do not provide explicit importance scores. We introduce a unified int
The increasing deployment of Large Language Models in sensitive domains like clinical medicine necessitates robust interpretability solutions to build trust and ensure safety. This research addresses a critical limitation in current AI model understanding.
Achieving stable interpretability in AI models is crucial for their ethical and effective integration into high-stakes clinical neuroscience, allowing for reliable diagnoses and treatment decisions. It directly impacts the trustworthiness and clinical utility of AI advancements.
This framework offers a path to more reliable and transparent AI explanations in medical contexts, potentially accelerating the adoption of transformer-based models in clinical diagnosis and research. It improves the foundational understanding of model behavior.
- · AI developers
- · Clinical neuroscience researchers
- · Patients with neurological conditions
- · Healthcare providers
- · AI models with unstable interpretability
- · Traditional diagnostic methods
- · Developers neglecting explainability
Increased confidence in AI-driven diagnostic tools for conditions like Alzheimer's disease due to enhanced explainability.
Faster and more accurate progression diagnosis could lead to earlier interventions and improved patient outcomes globally.
The methodology could be generalized to other complex AI applications in medicine, revolutionizing many diagnostic and treatment pipelines.
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