ReviewAnnals of medicine and surgery (2012)2026
Integration of AI diagnostic tools into clinical practice for Alzheimer's disease: barriers and solutions.
Review in Annals of medicine and surgery (2012), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's disease is a progressive neurodegenerative disorder that affects millions of people worldwide and remains difficult to diagnose in its earliest stages. This narrative review examines developments in artificial intelligence diagnostic tools designed to support clinicians in the detection of Alzheimer's disease. It evaluates systems that analyze brain imaging scans, genetic information, and cognitive assessments, as well as emerging approaches that monitor speech patterns and data from wearable devices. The review identifies six challenges to clinical adoption: limited and unrepresentative data sets; limited transparency of algorithmic decisions; disruption of established clinical workflows; unclear regulatory frameworks; high implementation costs and infrastructure demands; and the potential to widen health disparities. To address these issues, we propose the creation of large collaborative data repositories, the advancement of transparent model interpretation methods, comprehensive clinician education programs, the establishment of clear regulatory pathways, and strategic investment in scalable infrastructure. By confronting these technical, human, and system-level challenges through coordinated efforts, artificial intelligence diagnostic tools can be incorporated into Alzheimer's disease care to enhance early diagnosis and improve patient outcomes across diverse healthcare 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.