ReviewReviews on recent clinical trials2025
Unraveling the Mysteries of Alzheimer's Disease Using Artificial Intelligence.
Review in Reviews on recent clinical trials, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 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.
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, 1 synthesis or guideline pooled it.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's disease (AD) is a multidimensional, complex condition that affects individuals all over the world. Despite decades of experimental and clinical research that has revealed various processes, many concerns concerning the origin of Alzheimer's disease remain unresolved. Despite the notion that there isn't a complete set of jigsaw pieces, the growing number of public data-sharing initiatives that collect biological, clinical, and lifestyle data from those suffering from Alzheimer's disease has resulted in virtually endless volumes of knowledge about the disorder, far beyond what humans can comprehend. Furthermore, combining Big Data from multi- -omics research gives a chance to investigate the pathophysiological processes underlying the whole biological spectrum of Alzheimer's disease. To improve knowledge on the subject of Alzheimer's disease, Artificial Intelligence (AI) offers a wide variety of approaches for evaluating complex and significant data. The introduction of next-generation sequencing and microarray technologies has resulted in significant growth in genetic data research. When it comes to assessing such complex projects, AI technology beats conventional statistical techniques of data processing. This review focuses on current research and potential challenges for AI in Alzheimer's disease research. This article, in particular, examines how AI may assist healthcare practitioners with patient stratification, estimating an individual's chance of AD conversion, and diagnosing AD using computer-aided diagnostic methodologies. Ultimately, scientists want to develop individualized, efficient medicines.
Indexed as
Identifiers
39563218What 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.