What Do LLMs Know About Alzheimer's Disease? Multi-loss Fine-Tuning and Probing for AD Detection
arXiv:2602.11177v2 Announce Type: replace Abstract: Reliable early detection of Alzheimer's disease (AD) is challenging, particularly due to the limited availability of labeled data. While large language models (LLMs) have shown strong transfer capabilities across do mains, adapting them to the AD domain through supervised fine-tuning remains largely unexplored. In this work, we empirically evaluate various model architectures across three heterogeneous transcript corpora (Pitt, CCC, ADRC) to investigate their effectiveness for text-based AD detection and analyze how task-relevant information
The increasing availability of large language models and advancements in fine-tuning techniques are making it possible to adapt AI to complex medical challenges like Alzheimer's detection.
This work demonstrates the potential of LLMs to significantly improve early and reliable detection of Alzheimer's disease, which has profound implications for patient care and research.
The ability to leverage LLMs for text-based diagnostic support in difficult-to-detect diseases changes the landscape of medical diagnostics and AI application in healthcare.
- · AI developers in healthcare
- · Patients with Alzheimer's disease
- · Medical diagnostic companies
- · Research institutions
- · Traditional diagnostic methods
- · Healthcare systems unprepared for AI integration
Improved early detection rates for Alzheimer's disease lead to better treatment outcomes and quality of life for patients.
The success of LLMs in AD detection could accelerate their application across other challenging medical diagnoses, particularly those involving textual or speech data.
Ethical and regulatory frameworks for AI in medical diagnostics will need rapid development and standardization to manage widespread adoption and ensure equitable access.
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Read at arXiv cs.CL