ArticleProceedings of the National Academy of Sciences of the United States of America2023
Bias in machine learning models can be significantly mitigated by careful training: Evidence from neuroimaging studies.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Physiological influences on neurovascular coupling: A systematic review of multimodal imaging approaches and recommendations for future study designs.Experimental physiology · 2025Pooled it
- AI-bioinformatics synergy for precision medicine in brain diseases: integrating radiomics and multi-omics data.Briefings in bioinformatics · 2026Review
- Prediction of cognitive performance by demographics, sleep, and brain morphometry: machine learning findings from ENIGMA-Sleep Working Group.Research square · 2026Article
- AI-driven big data analysis and predictive modeling of infectious disease immunity: from correlates to causal, multiscale understanding.Archives of microbiology · 2026Review
- External validation improves generalizability, replicability and reproducibility in predictive models for neuroimaging.Nature methods · 2026Review
- Deep learning in fetal, infant, and toddler neuroimaging research.Developmental cognitive neuroscience · 2026Review
- Predicting Continuous Cognitive Decline: The Generalizability of a Multimodal Machine Learning Approach Including Structural MRI and Non-Brain Data.medRxiv : the preprint server for health sciences · 2026Article
- Mapping neighbourhood-level drivers of type 2 diabetes for precision public health using predictive and causal machine learning.Scientific reports · 2026Article
- Machine learning approaches for data-driven hydrocarbon bioaugmentation and phytoremediation: the role of multi-omics insights.Frontiers in microbiology · 2026Review
- Methodological innovations in studying marginalized populations in research on neurodegeneration and ageing.Frontiers in aging neuroscience · 2026Review
- Next-generation osteoarthritis models: integrating biological, computational, and engineering approaches.Stem cell research & therapy · 2025Review
- AI in Pediatric Spine Care: Clinical, Research, and Ethical Considerations.Journal of clinical medicine · 2025Article
- Increasing pathogenic germline variant diagnosis rates in precision medicine: current best practices and future opportunities.Human genomics · 2025Review
- Artificial Intelligence-Enabled Point-of-Care Echocardiography: Bringing Precision Imaging to the Bedside.Current atherosclerosis reports · 2025Review
- Towards machine learning fairness in classifying multicategory causes of deaths in colorectal or lung cancer patients.Briefings in bioinformatics · 2025Article
- Big Data, Small Bias: Harmonizing Diffusion MRI-Based Structural Connectomes to Mitigate Site-Related Bias in Data Integration.Human brain mapping · 2025Article
- Evidence-based personalised medicine in critical care: a framework for quantifying and applying individualised treatment effects in patients who are critically ill.The Lancet. Respiratory medicine · 2025Review
- Comparative analysis of personal protective equipment nonadherence detection: computer vision versus human observers.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Generative models of MRI-derived neuroimaging features and associated dataset of 18,000 samples.Scientific data · 2024Article
- Prediction of stroke severity: systematic evaluation of lesion representations.Annals of clinical and translational neurology · 2024Article
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Abstract
Despite the great promise that machine learning has offered in many fields of medicine, it has also raised concerns about potential biases and poor generalization across genders, age distributions, races and ethnicities, hospitals, and data acquisition equipment and protocols. In the current study, and in the context of three brain diseases, we provide evidence which suggests that when properly trained, machine learning models can generalize well across diverse conditions and do not necessarily suffer from bias. Specifically, by using multistudy magnetic resonance imaging consortia for diagnosing Alzheimer's disease, schizophrenia, and autism spectrum disorder, we find that well-trained models have a high area-under-the-curve (AUC) on subjects across different subgroups pertaining to attributes such as gender, age, racial groups and different clinical studies and are unbiased under multiple fairness metrics such as demographic parity difference, equalized odds difference, equal opportunity difference, etc. We find that models that incorporate multisource data from demographic, clinical, genetic factors, and cognitive scores are also unbiased. These models have a better predictive AUC across subgroups than those trained only with imaging features, but there are also situations when these additional features do not help.
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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.