ReviewAlzheimer's & dementia : the journal of the Alzheimer's Association2023
Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia.
Review in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 2 of them syntheses 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
57 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- AI-Derived Blood Biomarkers for Ovarian Cancer Diagnosis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- The application of artificial intelligence in diagnosis of Alzheimer's disease: a bibliometric analysis.Frontiers in neurology · 2024Pooled it
- Machine Learning and Multimodal Biomarker Discovery in Alzheimer's Disease.Brain sciences · 2026Review
- The global research of artificial intelligence on Alzheimer disease: A 25-year bibliometric analysis.Medicine · 2026Article
- Advances in Machine Learning-Assisted Optical Sensing Arrays for Disease Diagnosis.Biomimetics (Basel, Switzerland) · 2026Review
- Low-burden AI approach for cross-national early identification of cognitive impairment using real-world questionnaire response behaviours.Nature communications · 2026Article
- Salivary protein macromolecules as diagnostic and therapeutic biomarkers linking oral health and neurodegenerative diseases-emerging mechanisms and future perspectives.Inflammopharmacology · 2026Review
- Enhancing Early Detection of Alzheimer's Disease: An Ensemble Model for Multi-Domain Cognitive Assessment Using Voice and Video.Sensors (Basel, Switzerland) · 2026Article
- Artificial intelligence in drug research and development: a review of methods and applications in drug repurposing.Briefings in bioinformatics · 2026Review
- Deep learning for psychiatric genomics: from tools to applications.Current opinion in genetics & development · 2026Review
- Next generation preventive neurology: how artificial intelligence and machine learning are reshaping Alzheimer's disease research.Behavioral and brain functions : BBF · 2026Review
- Artificial intelligence-based biomarkers for the diagnosis and treatment of neurological conditions: a narrative review.Molecular brain · 2026Review
- Systems biology of Alzheimer's Disease: a scoping review of key pathways and mechanisms.Molecular neurodegeneration · 2026Article
- Review
- ATN Classification and Machine-Learned Plasma Biomarker Phenotypes Reveal Distinct Alzheimer's Pathology in a Population-Based Cohort.medRxiv : the preprint server for health sciences · 2026Article
- Integration of AI diagnostic tools into clinical practice for Alzheimer's disease: barriers and solutions.Annals of medicine and surgery (2012) · 2026Review
- Alzheimer's disease detection using a quantum deep neural network with Haralick feature extraction and simulated annealing optimization.PeerJ. Computer science · 2026Article
- Digital Technology in Cognitive Decline: Bibliometric and Visualization Study.Current Alzheimer research · 2026Review
- Graphomotor performance as a quantitative marker of Alzheimer's disease: a tablet-based case-control ROC study.Frontiers in neuroscience · 2026Article
- Interpretable machine learning model based on multimodal MRI radiomics for Alzheimer's disease diagnosis.Frontiers in aging neuroscience · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors.
Funding
Abstract
With the increase in large multimodal cohorts and high-throughput technologies, the potential for discovering novel biomarkers is no longer limited by data set size. Artificial intelligence (AI) and machine learning approaches have been developed to detect novel biomarkers and interactions in complex data sets. We discuss exemplar uses and evaluate current applications and limitations of AI to discover novel biomarkers. Remaining challenges include a lack of diversity in the data sets available, the sheer complexity of investigating interactions, the invasiveness and cost of some biomarkers, and poor reporting in some studies. Overcoming these challenges will involve collecting data from underrepresented populations, developing more powerful AI approaches, validating the use of noninvasive biomarkers, and adhering to reporting guidelines. By harnessing rich multimodal data through AI approaches and international collaborative innovation, we are well positioned to identify clinically useful biomarkers that are accurate, generalizable, unbiased, and acceptable in clinical practice. HIGHLIGHTS: Artificial intelligence and machine learning approaches may accelerate dementia biomarker discovery. Remaining challenges include data set suitability due to size and bias in cohort selection. Multimodal data, diverse data sets, improved machine learning approaches, real-world validation, and interdisciplinary collaboration are required.
Indexed as
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.