ArticleThe journal of prevention of Alzheimer's disease2025
Artificial intelligence-enabled safety monitoring in Alzheimer's disease clinical trials.
Article in The journal of prevention of Alzheimer's disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- AI and innovation in clinical trials.NPJ digital medicine · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundInvestigators conducting clinical trials have an ethical, scientific, and regulatory obligation to protect the safety of trial participants. Traditionally, safety monitoring includes manual review and coding of adverse event data by expert clinicians.
objectivesOur study explores the use of natural language processing (NLP) and artificial intelligence (AI) methods to streamline and standardize clinician coding of adverse event data in Alzheimer's disease (AD) clinical trials.
designOur quantitative retrospective study aimed to develop a gold standard AD adverse event data set, evaluate the predictive performance of NLP-based models to classify adverse events, and determine whether automated coding is more efficient, accurate, reliable, and consistent than clinician coding.
settingOur study was conducted at the University of Southern California's Alzheimer's Therapeutic Research Institute (ATRI). ATRI serves as the clinical and data coordinating center for the Alzheimer's Clinical Trial Consortium (ACTC).
participantsWe collected demographic and adverse event data from eight completed clinical trials in participants (n=1920) with symptomatic AD conducted between 2005 and 2020. MEASUREMENTS: Original expert clinician-confirmed codes were used for all model performance comparisons. F1 score was used as the primary model selection metric. Final classifier performance was evaluated using predictive accuracy. Clinician effort was measured in time to code, review, and confirm coded adverse events.
resultsIn a sample of 1000 adverse events, AI-based AE coding achieved higher accuracy (∼20% increase in accuracy) and was more cost-effective (∼80% cost reduction) than traditional clinician coding.
conclusionsOur study results demonstrate how approaches that effectively combine AI and human expertise can improve the efficiency and quality of adverse event coding and clinical trial safety monitoring.
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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.