Sources of bias in artificial intelligence that perpetuate healthcare disparities—A global review
While artificial intelligence (AI) offers possibilities of advanced clinical prediction and decision-making in healthcare, models trained on relatively homogeneous datasets, and populations poorly-representative of underlying diversity, limits generalisability and risks biased AI-based decisions. He...
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| Published in | PLOS digital health Vol. 1; no. 3; p. e0000022 |
|---|---|
| Main Authors | , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
01.03.2022
PLOS Public Library of Science (PLoS) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2767-3170 2767-3170 |
| DOI | 10.1371/journal.pdig.0000022 |
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| Abstract | While artificial intelligence (AI) offers possibilities of advanced clinical prediction and decision-making in healthcare, models trained on relatively homogeneous datasets, and populations poorly-representative of underlying diversity, limits generalisability and risks biased AI-based decisions. Here, we describe the landscape of AI in clinical medicine to delineate population and data-source disparities.
We performed a scoping review of clinical papers published in PubMed in 2019 using AI techniques. We assessed differences in dataset country source, clinical specialty, and author nationality, sex, and expertise. A manually tagged subsample of PubMed articles was used to train a model, leveraging transfer-learning techniques (building upon an existing BioBERT model) to predict eligibility for inclusion (original, human, clinical AI literature). Of all eligible articles, database country source and clinical specialty were manually labelled. A BioBERT-based model predicted first/last author expertise. Author nationality was determined using corresponding affiliated institution information using Entrez Direct. And first/last author sex was evaluated using the Gendarize.io API.
Our search yielded 30,576 articles, of which 7,314 (23.9%) were eligible for further analysis. Most databases came from the US (40.8%) and China (13.7%). Radiology was the most represented clinical specialty (40.4%), followed by pathology (9.1%). Authors were primarily from either China (24.0%) or the US (18.4%). First and last authors were predominately data experts (i.e., statisticians) (59.6% and 53.9% respectively) rather than clinicians. And the majority of first/last authors were male (74.1%).
U.S. and Chinese datasets and authors were disproportionately overrepresented in clinical AI, and almost all of the top 10 databases and author nationalities were from high income countries (HICs). AI techniques were most commonly employed for image-rich specialties, and authors were predominantly male, with non-clinical backgrounds. Development of technological infrastructure in data-poor regions, and diligence in external validation and model re-calibration prior to clinical implementation in the short-term, are crucial in ensuring clinical AI is meaningful for broader populations, and to avoid perpetuating global health inequity. |
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| AbstractList | BackgroundWhile artificial intelligence (AI) offers possibilities of advanced clinical prediction and decision-making in healthcare, models trained on relatively homogeneous datasets, and populations poorly-representative of underlying diversity, limits generalisability and risks biased AI-based decisions. Here, we describe the landscape of AI in clinical medicine to delineate population and data-source disparities.MethodsWe performed a scoping review of clinical papers published in PubMed in 2019 using AI techniques. We assessed differences in dataset country source, clinical specialty, and author nationality, sex, and expertise. A manually tagged subsample of PubMed articles was used to train a model, leveraging transfer-learning techniques (building upon an existing BioBERT model) to predict eligibility for inclusion (original, human, clinical AI literature). Of all eligible articles, database country source and clinical specialty were manually labelled. A BioBERT-based model predicted first/last author expertise. Author nationality was determined using corresponding affiliated institution information using Entrez Direct. And first/last author sex was evaluated using the Gendarize.io API.ResultsOur search yielded 30,576 articles, of which 7,314 (23.9%) were eligible for further analysis. Most databases came from the US (40.8%) and China (13.7%). Radiology was the most represented clinical specialty (40.4%), followed by pathology (9.1%). Authors were primarily from either China (24.0%) or the US (18.4%). First and last authors were predominately data experts (i.e., statisticians) (59.6% and 53.9% respectively) rather than clinicians. And the majority of first/last authors were male (74.1%).InterpretationU.S. and Chinese datasets and authors were disproportionately overrepresented in clinical AI, and almost all of the top 10 databases and author nationalities were from high income countries (HICs). AI techniques were most commonly employed for image-rich specialties, and authors were predominantly male, with non-clinical backgrounds. Development of technological infrastructure in data-poor regions, and diligence in external validation and model re-calibration prior to clinical implementation in the short-term, are crucial in ensuring clinical AI is meaningful for broader populations, and to avoid perpetuating global health inequity. Artificial Intelligence (AI) creates opportunities for accurate, objective and immediate decision support in healthcare with little expert input–especially valuable in resource-poor settings where there is shortage of specialist care. Given that AI poorly generalises to cohorts outside those whose data was used to train and validate the algorithms, populations in data-rich regions stand to benefit substantially more vs data-poor regions, entrenching existing healthcare disparities. Here, we show that more than half of the datasets used for clinical AI originate from either the US or China. In addition, the U.S. and China contribute over 40% of the authors of the publications. While the models may perform on-par/better than clinician decision-making in the well-represented regions, benefits elsewhere are not guaranteed. Further, we show discrepancies in gender and specialty representation–notably that almost three-quarters of the coveted first/senior authorship positions were held by men, and radiology accounted for 40% of all clinical AI manuscripts. We emphasize that building equitable sociodemographic representation in data repositories, in author nationality, gender and expertise, and in clinical specialties is crucial in ameliorating health inequities. While artificial intelligence (AI) offers possibilities of advanced clinical prediction and decision-making in healthcare, models trained on relatively homogeneous datasets, and populations poorly-representative of underlying diversity, limits generalisability and risks biased AI-based decisions. Here, we describe the landscape of AI in clinical medicine to delineate population and data-source disparities. We performed a scoping review of clinical papers published in PubMed in 2019 using AI techniques. We assessed differences in dataset country source, clinical specialty, and author nationality, sex, and expertise. A manually tagged subsample of PubMed articles was used to train a model, leveraging transfer-learning techniques (building upon an existing BioBERT model) to predict eligibility for inclusion (original, human, clinical AI literature). Of all eligible articles, database country source and clinical specialty were manually labelled. A BioBERT-based model predicted first/last author expertise. Author nationality was determined using corresponding affiliated institution information using Entrez Direct. And first/last author sex was evaluated using the Gendarize.io API. Our search yielded 30,576 articles, of which 7,314 (23.9%) were eligible for further analysis. Most databases came from the US (40.8%) and China (13.7%). Radiology was the most represented clinical specialty (40.4%), followed by pathology (9.1%). Authors were primarily from either China (24.0%) or the US (18.4%). First and last authors were predominately data experts (i.e., statisticians) (59.6% and 53.9% respectively) rather than clinicians. And the majority of first/last authors were male (74.1%). U.S. and Chinese datasets and authors were disproportionately overrepresented in clinical AI, and almost all of the top 10 databases and author nationalities were from high income countries (HICs). AI techniques were most commonly employed for image-rich specialties, and authors were predominantly male, with non-clinical backgrounds. Development of technological infrastructure in data-poor regions, and diligence in external validation and model re-calibration prior to clinical implementation in the short-term, are crucial in ensuring clinical AI is meaningful for broader populations, and to avoid perpetuating global health inequity. Background While artificial intelligence (AI) offers possibilities of advanced clinical prediction and decision-making in healthcare, models trained on relatively homogeneous datasets, and populations poorly-representative of underlying diversity, limits generalisability and risks biased AI-based decisions. Here, we describe the landscape of AI in clinical medicine to delineate population and data-source disparities. Methods We performed a scoping review of clinical papers published in PubMed in 2019 using AI techniques. We assessed differences in dataset country source, clinical specialty, and author nationality, sex, and expertise. A manually tagged subsample of PubMed articles was used to train a model, leveraging transfer-learning techniques (building upon an existing BioBERT model) to predict eligibility for inclusion (original, human, clinical AI literature). Of all eligible articles, database country source and clinical specialty were manually labelled. A BioBERT-based model predicted first/last author expertise. Author nationality was determined using corresponding affiliated institution information using Entrez Direct. And first/last author sex was evaluated using the Gendarize.io API. Results Our search yielded 30,576 articles, of which 7,314 (23.9%) were eligible for further analysis. Most databases came from the US (40.8%) and China (13.7%). Radiology was the most represented clinical specialty (40.4%), followed by pathology (9.1%). Authors were primarily from either China (24.0%) or the US (18.4%). First and last authors were predominately data experts (i.e., statisticians) (59.6% and 53.9% respectively) rather than clinicians. And the majority of first/last authors were male (74.1%). Interpretation U.S. and Chinese datasets and authors were disproportionately overrepresented in clinical AI, and almost all of the top 10 databases and author nationalities were from high income countries (HICs). AI techniques were most commonly employed for image-rich specialties, and authors were predominantly male, with non-clinical backgrounds. Development of technological infrastructure in data-poor regions, and diligence in external validation and model re-calibration prior to clinical implementation in the short-term, are crucial in ensuring clinical AI is meaningful for broader populations, and to avoid perpetuating global health inequity. While artificial intelligence (AI) offers possibilities of advanced clinical prediction and decision-making in healthcare, models trained on relatively homogeneous datasets, and populations poorly-representative of underlying diversity, limits generalisability and risks biased AI-based decisions. Here, we describe the landscape of AI in clinical medicine to delineate population and data-source disparities.BACKGROUNDWhile artificial intelligence (AI) offers possibilities of advanced clinical prediction and decision-making in healthcare, models trained on relatively homogeneous datasets, and populations poorly-representative of underlying diversity, limits generalisability and risks biased AI-based decisions. Here, we describe the landscape of AI in clinical medicine to delineate population and data-source disparities.We performed a scoping review of clinical papers published in PubMed in 2019 using AI techniques. We assessed differences in dataset country source, clinical specialty, and author nationality, sex, and expertise. A manually tagged subsample of PubMed articles was used to train a model, leveraging transfer-learning techniques (building upon an existing BioBERT model) to predict eligibility for inclusion (original, human, clinical AI literature). Of all eligible articles, database country source and clinical specialty were manually labelled. A BioBERT-based model predicted first/last author expertise. Author nationality was determined using corresponding affiliated institution information using Entrez Direct. And first/last author sex was evaluated using the Gendarize.io API.METHODSWe performed a scoping review of clinical papers published in PubMed in 2019 using AI techniques. We assessed differences in dataset country source, clinical specialty, and author nationality, sex, and expertise. A manually tagged subsample of PubMed articles was used to train a model, leveraging transfer-learning techniques (building upon an existing BioBERT model) to predict eligibility for inclusion (original, human, clinical AI literature). Of all eligible articles, database country source and clinical specialty were manually labelled. A BioBERT-based model predicted first/last author expertise. Author nationality was determined using corresponding affiliated institution information using Entrez Direct. And first/last author sex was evaluated using the Gendarize.io API.Our search yielded 30,576 articles, of which 7,314 (23.9%) were eligible for further analysis. Most databases came from the US (40.8%) and China (13.7%). Radiology was the most represented clinical specialty (40.4%), followed by pathology (9.1%). Authors were primarily from either China (24.0%) or the US (18.4%). First and last authors were predominately data experts (i.e., statisticians) (59.6% and 53.9% respectively) rather than clinicians. And the majority of first/last authors were male (74.1%).RESULTSOur search yielded 30,576 articles, of which 7,314 (23.9%) were eligible for further analysis. Most databases came from the US (40.8%) and China (13.7%). Radiology was the most represented clinical specialty (40.4%), followed by pathology (9.1%). Authors were primarily from either China (24.0%) or the US (18.4%). First and last authors were predominately data experts (i.e., statisticians) (59.6% and 53.9% respectively) rather than clinicians. And the majority of first/last authors were male (74.1%).U.S. and Chinese datasets and authors were disproportionately overrepresented in clinical AI, and almost all of the top 10 databases and author nationalities were from high income countries (HICs). AI techniques were most commonly employed for image-rich specialties, and authors were predominantly male, with non-clinical backgrounds. Development of technological infrastructure in data-poor regions, and diligence in external validation and model re-calibration prior to clinical implementation in the short-term, are crucial in ensuring clinical AI is meaningful for broader populations, and to avoid perpetuating global health inequity.INTERPRETATIONU.S. and Chinese datasets and authors were disproportionately overrepresented in clinical AI, and almost all of the top 10 databases and author nationalities were from high income countries (HICs). AI techniques were most commonly employed for image-rich specialties, and authors were predominantly male, with non-clinical backgrounds. Development of technological infrastructure in data-poor regions, and diligence in external validation and model re-calibration prior to clinical implementation in the short-term, are crucial in ensuring clinical AI is meaningful for broader populations, and to avoid perpetuating global health inequity. |
| Author | Charpignon, Marie-Laure Situ, Julia Dernoncourt, Franck Moukheiber, Lama Celi, Leo Anthony Mitchell, William Greig Yao, Seth Eber, Rene Schirmer, Julian Wawira, Judy Gichoya Dee, Edward Christopher Park, Joel Cellini, Jacqueline Paguio, Joseph |
| AuthorAffiliation | 1 Massachusetts Institute of Technology, Institute for Medical Engineering and Science, Cambridge, MA, United States of America 4 Harvard Medical School, Department of Library Services, Boston, MA, United States of America 7 Adobe Inc, Adobe Research, San Jose, CA, United States of America 9 Harvard TH Chan School of Public Health, Boston, MA, United States of America 6 Harvard Medical School, Boston, MA, United States of America 10 Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America 14 Emory University, Department of Radiology and Biomedical Informatics, Atlanta, GA, United States of America 3 Beth Israel Deaconess Medical Center, Department of Medicine, Boston, MA, United States of America 11 Massachusetts Institute of Technology, Department of Computer Science and Molecular Biology, Cambridge, MA, United States of America 2 Harvard TH Chan School of Public Health, Department of Biostatistics, Boston, MA, United States of America 8 Montpellier Universit |
| AuthorAffiliation_xml | – name: 3 Beth Israel Deaconess Medical Center, Department of Medicine, Boston, MA, United States of America – name: 9 Harvard TH Chan School of Public Health, Boston, MA, United States of America – name: 10 Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America – name: 6 Harvard Medical School, Boston, MA, United States of America – name: 12 Einstein Medical Center Philadelphia, Department of Medicine, Philadelphia, PA, United States of America – name: 14 Emory University, Department of Radiology and Biomedical Informatics, Atlanta, GA, United States of America – name: 7 Adobe Inc, Adobe Research, San Jose, CA, United States of America – name: 4 Harvard Medical School, Department of Library Services, Boston, MA, United States of America – name: 2 Harvard TH Chan School of Public Health, Department of Biostatistics, Boston, MA, United States of America – name: Brown University, UNITED STATES – name: 13 BeiGene, Applied Innovation, Cambridge, MA, United States of America – name: 1 Massachusetts Institute of Technology, Institute for Medical Engineering and Science, Cambridge, MA, United States of America – name: 5 Massachusetts Institute of Technology, Institute for Data, Systems and Society, Cambridge, MA, United States of America – name: 8 Montpellier University, Montpellier Research in Management, Montpellier, France – name: 11 Massachusetts Institute of Technology, Department of Computer Science and Molecular Biology, Cambridge, MA, United States of America |
| Author_xml | – sequence: 1 givenname: Leo Anthony surname: Celi fullname: Celi, Leo Anthony – sequence: 2 givenname: Jacqueline surname: Cellini fullname: Cellini, Jacqueline – sequence: 3 givenname: Marie-Laure orcidid: 0000-0002-5786-2627 surname: Charpignon fullname: Charpignon, Marie-Laure – sequence: 4 givenname: Edward Christopher orcidid: 0000-0001-6119-0889 surname: Dee fullname: Dee, Edward Christopher – sequence: 5 givenname: Franck orcidid: 0000-0002-1119-1346 surname: Dernoncourt fullname: Dernoncourt, Franck – sequence: 6 givenname: Rene orcidid: 0000-0002-4347-0198 surname: Eber fullname: Eber, Rene – sequence: 7 givenname: William Greig orcidid: 0000-0002-2122-6741 surname: Mitchell fullname: Mitchell, William Greig – sequence: 8 givenname: Lama surname: Moukheiber fullname: Moukheiber, Lama – sequence: 9 givenname: Julian surname: Schirmer fullname: Schirmer, Julian – sequence: 10 givenname: Julia surname: Situ fullname: Situ, Julia – sequence: 11 givenname: Joseph orcidid: 0000-0003-4054-5112 surname: Paguio fullname: Paguio, Joseph – sequence: 12 givenname: Joel orcidid: 0000-0003-1319-6893 surname: Park fullname: Park, Joel – sequence: 13 givenname: Judy Gichoya surname: Wawira fullname: Wawira, Judy Gichoya – sequence: 14 givenname: Seth orcidid: 0000-0003-0851-6223 surname: Yao fullname: Yao, Seth |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36812532$$D View this record in MEDLINE/PubMed https://hal.science/hal-03857753$$DView record in HAL |
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| Copyright | Copyright: © 2022 Celi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 2022 Celi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Distributed under a Creative Commons Attribution 4.0 International License 2022 Celi et al 2022 Celi et al |
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| Snippet | While artificial intelligence (AI) offers possibilities of advanced clinical prediction and decision-making in healthcare, models trained on relatively... BackgroundWhile artificial intelligence (AI) offers possibilities of advanced clinical prediction and decision-making in healthcare, models trained on... Background While artificial intelligence (AI) offers possibilities of advanced clinical prediction and decision-making in healthcare, models trained on... Artificial Intelligence (AI) creates opportunities for accurate, objective and immediate decision support in healthcare with little expert input–especially... |
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| SubjectTerms | Artificial intelligence Automation Bias Biology and Life Sciences Clinical decision making Computer and Information Sciences Computer Science Deep learning Ecology and Environmental Sciences Engineering and Technology Global health Health care Machine learning Medical Subject Headings-MeSH Medicine and Health Sciences Natural language processing Neural networks Patients Radiology |
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| Title | Sources of bias in artificial intelligence that perpetuate healthcare disparities—A global review |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/36812532 https://www.proquest.com/docview/3085683087 https://www.proquest.com/docview/2779351711 https://hal.science/hal-03857753 https://pubmed.ncbi.nlm.nih.gov/PMC9931338 https://doaj.org/article/76b4d2b3d6a34ad8adad2334ae643a4d |
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