Examining inclusivity: the use of AI and diverse populations in health and social care: a systematic review
Abstract Background Artificial intelligence (AI)-based systems are being rapidly integrated into the fields of health and social care. Although such systems can substantially improve the provision of care, diverse and marginalized populations are often incorrectly or insufficiently represented withi...
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| Format: | Article |
| Language: | English |
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BMC
2025-02-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | https://doi.org/10.1186/s12911-025-02884-1 |
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| author | John Gabriel O. Marko Ciprian Daniel Neagu P. B. Anand |
| author_facet | John Gabriel O. Marko Ciprian Daniel Neagu P. B. Anand |
| author_sort | John Gabriel O. Marko |
| collection | DOAJ |
| description | Abstract Background Artificial intelligence (AI)-based systems are being rapidly integrated into the fields of health and social care. Although such systems can substantially improve the provision of care, diverse and marginalized populations are often incorrectly or insufficiently represented within these systems. This review aims to assess the influence of AI on health and social care among these populations, particularly with regard to issues related to inclusivity and regulatory concerns. Methods We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Six leading databases were searched, and 129 articles were selected for this review in line with predefined eligibility criteria. Results This research revealed disparities in AI outcomes, accessibility, and representation among diverse groups due to biased data sources and a lack of representation in training datasets, which can potentially exacerbate inequalities in care delivery for marginalized communities. Conclusion AI development practices, legal frameworks, and policies must be reformulated to ensure that AI is applied in an equitable manner. A holistic approach must be used to address disparities, enforce effective regulations, safeguard privacy, promote inclusion and equity, and emphasize rigorous validation. |
| format | Article |
| id | doaj-art-6a4946ad6aab49c2b3bf8e1b6c6ea786 |
| institution | Kabale University |
| issn | 1472-6947 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj-art-6a4946ad6aab49c2b3bf8e1b6c6ea7862025-02-09T12:40:18ZengBMCBMC Medical Informatics and Decision Making1472-69472025-02-0125111510.1186/s12911-025-02884-1Examining inclusivity: the use of AI and diverse populations in health and social care: a systematic reviewJohn Gabriel O. Marko0Ciprian Daniel Neagu1P. B. Anand2University of Bradford Facility of Engineering and Digital TechnologyUniversity of Bradford Facility of Engineering and Digital TechnologyUniversity of Bradford Faculty of Management Law and Social SciencesAbstract Background Artificial intelligence (AI)-based systems are being rapidly integrated into the fields of health and social care. Although such systems can substantially improve the provision of care, diverse and marginalized populations are often incorrectly or insufficiently represented within these systems. This review aims to assess the influence of AI on health and social care among these populations, particularly with regard to issues related to inclusivity and regulatory concerns. Methods We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Six leading databases were searched, and 129 articles were selected for this review in line with predefined eligibility criteria. Results This research revealed disparities in AI outcomes, accessibility, and representation among diverse groups due to biased data sources and a lack of representation in training datasets, which can potentially exacerbate inequalities in care delivery for marginalized communities. Conclusion AI development practices, legal frameworks, and policies must be reformulated to ensure that AI is applied in an equitable manner. A holistic approach must be used to address disparities, enforce effective regulations, safeguard privacy, promote inclusion and equity, and emphasize rigorous validation.https://doi.org/10.1186/s12911-025-02884-1Artificial intelligenceDiverse populationHealthcareInclusivity in artificial intelligenceMarginalized population |
| spellingShingle | John Gabriel O. Marko Ciprian Daniel Neagu P. B. Anand Examining inclusivity: the use of AI and diverse populations in health and social care: a systematic review BMC Medical Informatics and Decision Making Artificial intelligence Diverse population Healthcare Inclusivity in artificial intelligence Marginalized population |
| title | Examining inclusivity: the use of AI and diverse populations in health and social care: a systematic review |
| title_full | Examining inclusivity: the use of AI and diverse populations in health and social care: a systematic review |
| title_fullStr | Examining inclusivity: the use of AI and diverse populations in health and social care: a systematic review |
| title_full_unstemmed | Examining inclusivity: the use of AI and diverse populations in health and social care: a systematic review |
| title_short | Examining inclusivity: the use of AI and diverse populations in health and social care: a systematic review |
| title_sort | examining inclusivity the use of ai and diverse populations in health and social care a systematic review |
| topic | Artificial intelligence Diverse population Healthcare Inclusivity in artificial intelligence Marginalized population |
| url | https://doi.org/10.1186/s12911-025-02884-1 |
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