ArticlePLOS digital health2022
Predicting dementia from spontaneous speech using large language models.
Article in PLOS digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07200739 (Development of Deep Learning Models for Detection of Neurodegenerative Diseases Using Speech - a Danish Language-based Artificial Intelligence Study), which is not on this map. Cited by 68 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Development of Deep Learning Models for Detection of Neurodegenerative Diseases Using Speech - a Danish Language-based Artificial Intelligence Study (DetectAI)
Who cites it
68 citing papers in PubMed, 1 synthesis or guideline pooled it, 120 citations in OpenAlex.
- Applications and Concerns of ChatGPT and Other Conversational Large Language Models in Health Care: Systematic Review.Journal of medical Internet research · 2024Pooled it
- SpeechDETECT: an explainable automated speech processing pipeline for early detection of neurological and health changes.Health information science and systems · 2026Article
- Multimodal Dementia Prediction With Large Language Models: Cross-Attention Over Text, Audio, and Image.JMIR aging · 2026Article
- Artificial Intelligence for Alzheimer's Disease Diagnosis: From Traditional Machine Learning to Large Language Models.Biosensors · 2026Review
- Generating Alzheimer's narratives using large language models.BMC medical informatics and decision making · 2026Article
- Design of an artificial intelligence model to screen spontaneous speech to detect Alzheimer's Disease.PLOS digital health · 2026Article
- Advancing Alzheimer Disease Prediction With Large Language Model-Based Linguistic Feature Analysis: Development and Validation Study.JMIR medical informatics · 2026Article
- Next generation preventive neurology: how artificial intelligence and machine learning are reshaping Alzheimer's disease research.Behavioral and brain functions : BBF · 2026Review
- Large language models for bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- Review of Artificial Intelligence for Clinical Use in Alzheimer's Disease and Related Dementias.Seminars in neurology · 2026Review
- Towards a speech-based digital biomarker for cognitive impairment: speech as a proxy for cognitive assessment.NPJ digital medicine · 2026Article
- WATCH-SS: Developing a Trustworthy and Explainable Modular Framework for Detecting Cognitive Impairment from Spontaneous Speech.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
- Enhanced diagnostic interpretation of the MoCA using machine learning.Frontiers in neuroscience · 2026Article
- Prediction of cognitive impairment through speech data analysis: A comparative evaluation of deep learning models.PloS one · 2026Article
- Script Generation as an Efficient Measure of Cognition and Everyday Function in Older Adults.medRxiv : the preprint server for health sciences · 2025Article
- Evaluating spoken language as a biomarker for automated screening of cognitive impairment.Communications medicine · 2025Article
- Multi-modal data analysis for early detection of alzheimer's disease and related dementias.The journal of prevention of Alzheimer's disease · 2025Article
- Artificial Intelligence in and Beyond Healthcare Psychology.Journal of clinical psychology in medical settings · 2025Review
- WATCH-SS: Developing a Trustworthy and Explainable Modular Framework for Detecting Cognitive Impairment from Spontaneous Speech.medRxiv : the preprint server for health sciences · 2025Article
- NeuroNet-AD: A Multimodal Deep Learning Framework for Multiclass Alzheimer's Disease Diagnosis.Bioengineering (Basel, Switzerland) · 2025Article
8 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Language impairment is an important biomarker of neurodegenerative disorders such as Alzheimer's disease (AD). Artificial intelligence (AI), particularly natural language processing (NLP), has recently been increasingly used for early prediction of AD through speech. Yet, relatively few studies exist on using large language models, especially GPT-3, to aid in the early diagnosis of dementia. In this work, we show for the first time that GPT-3 can be utilized to predict dementia from spontaneous speech. Specifically, we leverage the vast semantic knowledge encoded in the GPT-3 model to generate text embedding, a vector representation of the transcribed text from speech, that captures the semantic meaning of the input. We demonstrate that the text embedding can be reliably used to (1) distinguish individuals with AD from healthy controls, and (2) infer the subject's cognitive testing score, both solely based on speech data. We further show that text embedding considerably outperforms the conventional acoustic feature-based approach and even performs competitively with prevailing fine-tuned models. Together, our results suggest that GPT-3 based text embedding is a viable approach for AD assessment directly from speech and has the potential to improve early diagnosis of dementia.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.