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Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia
Winchester LM, Harshfield EL, Shi L, Badhwar A, Khleifat AA, Clarke N, Dehsarvi A, Lengyel I et al.. Alzheimer's & dementia : the journal of the Alzheimer's Association, 2023-08-31
Topic: Amyloid, tau and blood-test diagnosis
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.
Key findings
- 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.
Why it matters
This review discusses how artificial intelligence and machine learning may help discover novel biomarkers for Alzheimer's disease and dementia, while noting challenges such as lack of diversity in data sets and poor reporting. It concerns biomarker discovery methods and does not test any treatment, cure, or prevention for Alzheimer's disease.
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Study details
- Evidence strengthModerate
How this study scored 56/100
Published in a leading specialty journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 9 / 10 | Tier 2 — leading specialty journal (Alzheimer's & dementia : the journal of the Alzheimer's Association) |
| Journal's citation record | 2 / 4 | Alzheimer s & Dementia: journal h-index 228 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 3 / 5 | 2023 — within 6 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 5 / 7 | Cited 130 times (OpenAlex, 2026-10-10) |
| Citations per year | 4 / 5 | 26.0 citations a year over 5 year(s) since 2023 |
| Impact for its field and year | 5 / 6 | Cited 23.4× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 6 other studies in this library |
| Still being cited | 3 / 4 | Cited 127 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author David J. Llewellyn: h-index 68 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | University of Oxford: institution h-index 2,205 (OpenAlex) |
| Multi-centre collaboration | 3 / 3 | OpenAlex lists 15 institution(s) in 3 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 37654029) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 56 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
Winchester, L. M., Harshfield, E. L., Shi, L., Badhwar, A., Khleifat, A. A., Clarke, N., et al. (2023). Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia. Alzheimer's & dementia : the journal of the Alzheimer's Association. https://doi.org/10.1002/alz.13390
Vancouver
Winchester LM, Harshfield EL, Shi L, Badhwar A, Khleifat AA, Clarke N, et al. Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia. Alzheimer's & dementia : the journal of the Alzheimer's Association. 2023. doi:10.1002/alz.13390. PMID: 37654029.
BibTeX
@article{winchester2023-37654029,
author = {Winchester, L. M. and Harshfield, E. L. and Shi, L. and Badhwar, A. and Khleifat, A. A. and Clarke, N. and Dehsarvi, A. and Lengyel, I. and others},
title = {{Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia}},
journal = {Alzheimer's \& dementia : the journal of the Alzheimer's Association},
year = {2023},
doi = {10.1002/alz.13390},
pmid = {37654029},
url = {https://doi.org/10.1002/alz.13390}
}
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