ArticleNPJ digital medicine2025
A GPT-4o-powered framework for identifying cognitive impairment stages in electronic health records.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Multimodal AI for Alzheimer Disease Diagnosis: Systematic Review of Datasets, Models, and Modalities.Journal of medical Internet research · 2026Pooled it
- Natural Language Processing Applied to Psychiatric Clinical Notes: Scoping Review.JMIR medical informatics · 2026Article
- Large Language Models and Their Applications in Mental Health: Scoping Review.JMIR mental health · 2026Article
- A retrieval-augmented generation large language model framework for accurate dementia identification from electronic health records.medRxiv : the preprint server for health sciences · 2026Article
- Phecoder: semantic retrieval for auditing and expanding ICD-based phenotypes in EHR biobanks.medRxiv : the preprint server for health sciences · 2026Article
- Article
- Locally deployed large language model for real-world lecanemab eligibility pre-screening.Alzheimer's & dementia (Amsterdam, Netherlands)Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
Alzheimer's Disease and Related Dementias (ADRD) pose a major public health challenge, with a critical need for accurate and scalable tools for detecting cognitive impairment (CI). Readily available electronic health records (EHRs) contain valuable cognitive health data, but much of it is embedded in unstructured clinical notes. To address this problem, we developed a GPT-4o-powered framework for CI stage classification, leveraging longitudinal patient history summarization, multi-step reasoning, and confidence-aware decision-making. Evaluated on 165,926 notes from 1002 Medicare patients from Mass General Brigham (MGB), our GPT-4o framework achieved high accuracy in CI stage classification (weighted Cohen's kappa = 0.95, Spearman correlation = 0.93), and outperformed two other language models (weighted Cohen's kappa 0.82-0.85). Our framework also achieved high performance on Clinical Dementia Rating (CDR) scoring on an independent dataset of 769 memory clinic patients (weighted Cohen's kappa = 0.83). Finally, to ensure reliability and safety, we designed an interactive AI agent integrating our GPT-4o-powered framework and clinician oversight. This collaborative approach has the potential to facilitate CI diagnoses in real-world clinical settings.
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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.