Neuroimaging data repositories and AI-driven healthcare—Global aspirations vs. ethical considerations in machine learning models of neurological disease
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 d...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2024-02-01
|
| Series: | Frontiers in Artificial Intelligence |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2023.1286266/full |
| _version_ | 1797303292620963840 |
|---|---|
| author | Christine Lock Nicole Si Min Tan Ian James Long Nicole C. Keong Nicole C. Keong |
| author_facet | Christine Lock Nicole Si Min Tan Ian James Long Nicole C. Keong Nicole C. Keong |
| author_sort | Christine Lock |
| collection | DOAJ |
| description | 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—ML 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 (n = 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 (n = 68; 47% anosmic), (ii) predominant non-anosmic (n = 66; 73% disproportionate), and (iii) predominant anosmic (n = 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 “AI ethics.” Further work is required to expand the approach proposed here into a reproducible strategy. |
| first_indexed | 2024-03-07T23:50:48Z |
| format | Article |
| id | doaj.art-1e01c2b9422c4b9b99bed7b07cbdcf86 |
| institution | Directory Open Access Journal |
| issn | 2624-8212 |
| language | English |
| last_indexed | 2024-03-07T23:50:48Z |
| publishDate | 2024-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj.art-1e01c2b9422c4b9b99bed7b07cbdcf862024-02-19T04:55:36ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122024-02-01610.3389/frai.2023.12862661286266Neuroimaging data repositories and AI-driven healthcare—Global aspirations vs. ethical considerations in machine learning models of neurological diseaseChristine Lock0Nicole Si Min Tan1Ian James Long2Nicole C. Keong3Nicole C. Keong4Department of Neurosurgery, National Neuroscience Institute, Singapore, SingaporeDepartment of Neurosurgery, National Neuroscience Institute, Singapore, SingaporeDepartment of Neurosurgery, National Neuroscience Institute, Singapore, SingaporeDepartment of Neurosurgery, National Neuroscience Institute, Singapore, SingaporeDuke-NUS Medical School, Singapore, SingaporeNeuroimaging 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—ML 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 (n = 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 (n = 68; 47% anosmic), (ii) predominant non-anosmic (n = 66; 73% disproportionate), and (iii) predominant anosmic (n = 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 “AI ethics.” Further work is required to expand the approach proposed here into a reproducible strategy.https://www.frontiersin.org/articles/10.3389/frai.2023.1286266/fulldata repositoriesneuroimagingmachine learningartificial intelligence (AI)AI ethics |
| spellingShingle | Christine Lock Nicole Si Min Tan Ian James Long Nicole C. Keong Nicole C. Keong Neuroimaging data repositories and AI-driven healthcare—Global aspirations vs. ethical considerations in machine learning models of neurological disease Frontiers in Artificial Intelligence data repositories neuroimaging machine learning artificial intelligence (AI) AI ethics |
| title | Neuroimaging data repositories and AI-driven healthcare—Global aspirations vs. ethical considerations in machine learning models of neurological disease |
| title_full | Neuroimaging data repositories and AI-driven healthcare—Global aspirations vs. ethical considerations in machine learning models of neurological disease |
| title_fullStr | Neuroimaging data repositories and AI-driven healthcare—Global aspirations vs. ethical considerations in machine learning models of neurological disease |
| title_full_unstemmed | Neuroimaging data repositories and AI-driven healthcare—Global aspirations vs. ethical considerations in machine learning models of neurological disease |
| title_short | Neuroimaging data repositories and AI-driven healthcare—Global aspirations vs. ethical considerations in machine learning models of neurological disease |
| title_sort | neuroimaging data repositories and ai driven healthcare global aspirations vs ethical considerations in machine learning models of neurological disease |
| topic | data repositories neuroimaging machine learning artificial intelligence (AI) AI ethics |
| url | https://www.frontiersin.org/articles/10.3389/frai.2023.1286266/full |
| work_keys_str_mv | AT christinelock neuroimagingdatarepositoriesandaidrivenhealthcareglobalaspirationsvsethicalconsiderationsinmachinelearningmodelsofneurologicaldisease AT nicolesimintan neuroimagingdatarepositoriesandaidrivenhealthcareglobalaspirationsvsethicalconsiderationsinmachinelearningmodelsofneurologicaldisease AT ianjameslong neuroimagingdatarepositoriesandaidrivenhealthcareglobalaspirationsvsethicalconsiderationsinmachinelearningmodelsofneurologicaldisease AT nicoleckeong neuroimagingdatarepositoriesandaidrivenhealthcareglobalaspirationsvsethicalconsiderationsinmachinelearningmodelsofneurologicaldisease AT nicoleckeong neuroimagingdatarepositoriesandaidrivenhealthcareglobalaspirationsvsethicalconsiderationsinmachinelearningmodelsofneurologicaldisease |