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Assessing the Impact of Sociodemographic Factors on Artificial Intelligence Models in Predicting Dementia: Retrospective Cohort Study
Xingyi Liu, Muskan Garg, Maria E. Vassilaki, Jennifer L. St. Sauver, Ronald C. Petersen, Momin M. Malik, Chung‐Il Wi, Young J. Juhn et al.. JMIR Medical Informatics, 2026-01-07
Topic: Risk, prevention and care in population studies
Across both cohorts, individuals with lower SES generally exhibited higher BERs (worse performance) than high SES groups, confirming the presence of bias. In the MCSA cohort, males with high SES, as indicated by the HOUSES Index, consistently exhibited the lowest BERs across all evaluated models. Balancing the training data based on a specific SES measure showed a trend toward reducing the BER disparity when evaluated using that same measure. However, this targeted improvement demonstrated nonuniversal benefits; in some cases, it exacerbated disparities when evaluated using other, unbalanced SES measures. This pattern suggests that fairness interventions are not universally beneficial across different definitions of the protected attribute. While the balancing approach improved fairness in model performance for lower SES groups, it often came at the cost of a slight reduction in overall model performance. However, an exception was observed in the MCSA cohort when balancing based on the HOUSES Index using logistic regression, support vector machine, and Naïve Bayes, where the performances of both the high and low SES groups improved. This research highlights the importance of incorporating sociodemographic context into AI modeling in health care. The choice of SES measure may lead to different assessments of algorithmic bias. The HOUSES Index, as a validated individual-level SES measure, may be more effective for bias mitigation than area-level measures. Future AI development should integrate bias mitigation strategies to ensure models do not reinforce existing disparities in health outcomes.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Liu, X., Garg, M., Vassilaki, M. E., Sauver, J. L. S., Petersen, R. C., Malik, M. M., et al. (2026). Assessing the Impact of Sociodemographic Factors on Artificial Intelligence Models in Predicting Dementia: Retrospective Cohort Study. JMIR Medical Informatics. https://doi.org/10.2196/80405
Vancouver
Liu X, Garg M, Vassilaki ME, Sauver JLS, Petersen RC, Malik MM, et al. Assessing the Impact of Sociodemographic Factors on Artificial Intelligence Models in Predicting Dementia: Retrospective Cohort Study. JMIR Medical Informatics. 2026. doi:10.2196/80405. PMID: 41701928.
BibTeX
@article{liu2026-41701928,
author = {Liu, Xingyi and Garg, Muskan and Vassilaki, Maria E. and Sauver, Jennifer L. St. and Petersen, Ronald C. and Malik, Momin M. and Wi, Chung‐Il and Juhn, Young J. and others},
title = {{Assessing the Impact of Sociodemographic Factors on Artificial Intelligence Models in Predicting Dementia: Retrospective Cohort Study}},
journal = {JMIR Medical Informatics},
year = {2026},
doi = {10.2196/80405},
pmid = {41701928},
url = {https://doi.org/10.2196/80405}
}
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Key findings
- Across both cohorts, individuals with lower SES generally exhibited higher BERs (worse performance) than high SES groups, confirming the presence of bias.
- In the MCSA cohort, males with high SES, as indicated by the HOUSES Index, consistently exhibited the lowest BERs across all evaluated models.
- Balancing the training data based on a specific SES measure showed a trend toward reducing the BER disparity when evaluated using that same measure.
- However, this targeted improvement demonstrated nonuniversal benefits; in some cases, it exacerbated disparities when evaluated using other, unbalanced SES measures.
- This pattern suggests that fairness interventions are not universally beneficial across different definitions of the protected attribute.
Why it matters
This study found that AI models predicting dementia performed worse for people with lower socioeconomic status, showing bias in the models. Balancing the training data reduced this disparity for one measure of socioeconomic status but sometimes made it worse for other measures, and sometimes slightly lowered overall model performance.
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Study details
- Participants3,041
- Evidence strengthModerate
How this study scored 45/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomized / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (JMIR Medical Informatics) |
| Journal's citation record | 1 / 4 | JMIR Medical Informatics: journal h-index 82 (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 | 5 / 5 | 2026 — within 1 year(s) of the newest study here |
| Sample size | 3 / 4 | 3,041 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomized / blinded | 0 / 2 | No randomization or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 1 / 7 | Cited 1 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.5 citations a year over 2 year(s) since 2026 |
| Impact for its field and year | 3 / 6 | Cited 6.2× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 1 other studies in this library |
| Still being cited | 0 / 4 | Cited 1 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Sunghwan Sohn: h-index 41 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | Mayo Clinic: institution h-index 1,606 (OpenAlex) |
| Multicenter collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 41701928) |
| Free to read | 2 / 2 | Free to read |
| Total | 45 / 100 | — |
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