{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T02:12:56Z","timestamp":1784686376222,"version":"3.55.0"},"reference-count":71,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,2,19]],"date-time":"2024-02-19T00:00:00Z","timestamp":1708300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001349","name":"National Medical Research Council","doi-asserted-by":"publisher","award":["MOH-CSAINV18nov-0005"],"award-info":[{"award-number":["MOH-CSAINV18nov-0005"]}],"id":[{"id":"10.13039\/501100001349","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>Neuroimaging data repositories are data-rich resources comprising brain imaging with clinical and biomarker data. The potential for such repositories to transform healthcare is tremendous, especially in their capacity to support machine learning (ML) and artificial intelligence (AI) tools. Current discussions about the generalizability of such tools in healthcare provoke concerns of risk of bias\u2014ML models underperform in women and ethnic and racial minorities. The use of ML may exacerbate existing healthcare disparities or cause post-deployment harms. Do neuroimaging data repositories and their capacity to support ML\/AI-driven clinical discoveries, have both the potential to accelerate innovative medicine and harden the gaps of social inequities in neuroscience-related healthcare? In this paper, we examined the ethical concerns of ML-driven modeling of global community neuroscience needs arising from the use of data amassed within neuroimaging data repositories. We explored this in two parts; firstly, in a theoretical experiment, we argued for a South East Asian-based repository to redress global imbalances. Within this context, we then considered the ethical framework toward the inclusion vs. exclusion of the migrant worker population, a group subject to healthcare inequities. Secondly, we created a model simulating the impact of global variations in the presentation of anosmia risks in COVID-19 toward altering brain structural findings; we then performed a mini AI ethics experiment. In this experiment, we interrogated an actual pilot dataset (<jats:italic>n<\/jats:italic> = 17; 8 non-anosmic (47%) vs. 9 anosmic (53%) using an ML clustering model. To create the COVID-19 simulation model, we bootstrapped to resample and amplify the dataset. This resulted in three hypothetical datasets: (i) matched (<jats:italic>n<\/jats:italic> = 68; 47% anosmic), (ii) predominant non-anosmic (<jats:italic>n<\/jats:italic> = 66; 73% disproportionate), and (iii) predominant anosmic (<jats:italic>n<\/jats:italic> = 66; 76% disproportionate). We found that the differing proportions of the same cohorts represented in each hypothetical dataset altered not only the relative importance of key features distinguishing between them but even the presence or absence of such features. The main objective of our mini experiment was to understand if ML\/AI methodologies could be utilized toward modelling disproportionate datasets, in a manner we term \u201cAI ethics.\u201d Further work is required to expand the approach proposed here into a reproducible strategy.<\/jats:p>","DOI":"10.3389\/frai.2023.1286266","type":"journal-article","created":{"date-parts":[[2024,2,19]],"date-time":"2024-02-19T04:55:47Z","timestamp":1708318547000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Neuroimaging data repositories and AI-driven healthcare\u2014Global aspirations vs. ethical considerations in machine learning models of neurological disease"],"prefix":"10.3389","volume":"6","author":[{"given":"Christine","family":"Lock","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicole Si Min","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ian James","family":"Long","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicole C.","family":"Keong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,2,19]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1016\/j.neuroimage.2018.07.066","article-title":"Evaluation of the accuracy and precision of the diffusion parameter EStImation with Gibbs and NoisE removal pipeline","volume":"183","author":"Ades-Aron","year":"2018","journal-title":"Neuroimage"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pbio.3000344","article-title":"The Human Brain Project-Synergy between neuroscience, computing, informatics, and brain-inspired technologies","author":"Amunts","year":"2019","journal-title":"PLoS Biol"},{"key":"B3","unstructured":"AuA.\n          Work History Survey2016"},{"key":"B4","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1038\/s41597-019-0073-y","article-title":"The open diffusion data derivatives, brain data upcycling via integrated publishing of derivatives and reproducible open cloud services","volume":"6","author":"Avesani","year":"2019","journal-title":"Sci Data"},{"key":"B5","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1007\/s42844-020-00020-8","article-title":"Inclusion of American Indians and Alaskan Natives in large national studies: ethical considerations and implications for biospecimen collection in the HEALthy brain and child development study","volume":"1","author":"Bakhireva","year":"2020","journal-title":"Advers. 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