# Linguistic Features Extracted by GPT-4 Improve Alzheimer's Disease Detection based on Spontaneous Speech

arXiv: https://arxiv.org/abs/2412.15772 · HTML: https://mlsanity.com/paper/2412.15772/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 62 references that are in the map.
- First posted on arXiv 20 Dec 2024.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Capabilities of GPT-4 on Medical Challenge Problems](https://mlsanity.com/paper/2303.13375/) (2023)
- [Alzheimer's Dementia Recognition through Spontaneous Speech: The ADReSS Challenge](https://mlsanity.com/paper/2004.06833/) (2020)
- [What Did I Do Wrong? Quantifying LLMs' Sensitivity and Consistency to Prompt Engineering](https://mlsanity.com/paper/2406.12334/) (2024)
- [Detecting cognitive decline using speech only: The ADReSSo Challenge](https://mlsanity.com/paper/2104.09356/) (2021)
- [To BERT or Not To BERT: Comparing Speech and Language-based Approaches for Alzheimer's Disease Detection](https://mlsanity.com/paper/2008.01551/) (2020)
- [Sensitivity and Robustness of Large Language Models to Prompt Template in Japanese Text Classification Tasks](https://mlsanity.com/paper/2305.08714/) (2023)
- [Can LLMs like GPT-4 outperform traditional AI tools in dementia diagnosis? Maybe, but not today](https://mlsanity.com/paper/2306.01499/) (2023)
- [Exploring Multimodal Approaches for Alzheimer's Disease Detection Using Patient Speech Transcript and Audio Data](https://mlsanity.com/paper/2307.02514/) (2023)

## Built on by (most cited first, 8 of 8)

- [AD-CARE: A Guideline-grounded, Modality-agnostic LLM Agent for Real-world Alzheimer's Disease Diagnosis with Multi-cohort Assessment, Fairness Analysis, and Reader Study](https://mlsanity.com/paper/2603.25322/) (2026)
- [Leveraging Large Language Models for Spontaneous Speech-Based Suicide Risk Detection](https://mlsanity.com/paper/2507.00693/) (2025)
- [Beyond surface form: A pipeline for semantic analysis in Alzheimer's Disease detection from spontaneous speech](https://mlsanity.com/paper/2512.13685/) (2025)
- [A Multimodal Framework for Dementia Detection via Linguistic and Acoustic Representation Learning](https://mlsanity.com/paper/2605.25540/) (2026)
- [Fair Cognitive Impairment Detection Through Unlearning](https://mlsanity.com/paper/2606.18571/) (2026)
- [Activation-Guided Neuron Intervention to Induce Alzheimer's-Related Computational Language Phenotypes in a Large Language Model](https://mlsanity.com/paper/2608.03067/) (2026)
- [Cleaner Speech, Weaker Generalization: Revisiting Pitt-Derived Benchmarks for Alzheimer's Disease Detection](https://mlsanity.com/paper/2609.00276/) (2026)
- [Explainable and Generalisable LLM-based Cognitive Decline Detection with Spontaneous Speech](https://mlsanity.com/paper/2609.34217/) (2026)

## Abstract (arXiv)

Alzheimer's Disease (AD) is a significant and growing public health concern. Investigating alterations in speech and language patterns offers a promising path towards cost-effective and non-invasive early detection of AD on a large scale. Large language models (LLMs), such as GPT, have enabled powerful new possibilities for semantic text analysis. In this study, we leverage GPT-4 to extract five semantic features from transcripts of spontaneous patient speech. The features capture known symptoms of AD, but they are difficult to quantify effectively using traditional methods of computational linguistics. We demonstrate the clinical significance of these features and further validate one of them ("Word-Finding Difficulties") against a proxy measure and human raters. When combined with established linguistic features and a Random Forest classifier, the GPT-derived features significantly improve the detection of AD. Our approach proves effective for both manually transcribed and automatically generated transcripts, representing a novel and impactful use of recent advancements in LLMs for AD speech analysis.