Consumer opinion on the use of machine learning in healthcare settings: A qualitative systematic review
Introduction Given the increasing number of artificial intelligence and machine learning (AI/ML) tools in healthcare, we aimed to gain an understanding of consumer perspectives on the use of AI/ML tools for healthcare diagnostics. Methods We conducted a qualitative systematic review, following estab...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-01-01
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| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076241288631 |
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| _version_ | 1841556492118917120 |
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| author | Jacqueline H Stephens Celine Northcott Brianna F Poirier Trent Lewis |
| author_facet | Jacqueline H Stephens Celine Northcott Brianna F Poirier Trent Lewis |
| author_sort | Jacqueline H Stephens |
| collection | DOAJ |
| description | Introduction Given the increasing number of artificial intelligence and machine learning (AI/ML) tools in healthcare, we aimed to gain an understanding of consumer perspectives on the use of AI/ML tools for healthcare diagnostics. Methods We conducted a qualitative systematic review, following established standardized methods, of the existing literature indexed in the following databases up to 4 April 2022: OVID MEDLINE, OVID EMBASE, Scopus and Web of Science. Results Fourteen studies were identified as appropriate for inclusion in the meta-synthesis and systematic review. Most studies ( n = 12) were conducted in high-income countries, with data extracted from both mixed methods (42.9%) and qualitative (57.1%) studies. The meta-synthesis identified four overarching themes across the included studies: (1) Trust, fear, and uncertainty; (2) Data privacy and ML governance; (3) Impact on healthcare delivery and access; and (4) Consumers want to be engaged. Conclusion The current evidence demonstrates consumers’ understandings of AI/ML for medical diagnosis are complex. Consumers express a complex combination of both hesitancy and support towards AI/ML in healthcare diagnosis. Importantly, their views of the use of AI/ML in medical diagnosis are influenced by the perceived trustworthiness of their healthcare providers who use these AI/ML tools. Consumers recognize the potential for AI/ML tools to improve diagnostic accuracy, efficiency and access, and express a strong interest to be engaged in the development and implementation process of AI/ML into routine healthcare. |
| format | Article |
| id | doaj-art-4f517bc73e1940e693e14e62225ee436 |
| institution | Kabale University |
| issn | 2055-2076 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Digital Health |
| spelling | doaj-art-4f517bc73e1940e693e14e62225ee4362025-01-07T08:04:23ZengSAGE PublishingDigital Health2055-20762025-01-011110.1177/20552076241288631Consumer opinion on the use of machine learning in healthcare settings: A qualitative systematic reviewJacqueline H Stephens0Celine Northcott1Brianna F Poirier2Trent Lewis3 , College of Medicine and Public Health, , Adelaide, Australia , Adelaide, Australia The University of Adelaide, Adelaide, Australia College of Science and Engineering, , Adelaide, AustraliaIntroduction Given the increasing number of artificial intelligence and machine learning (AI/ML) tools in healthcare, we aimed to gain an understanding of consumer perspectives on the use of AI/ML tools for healthcare diagnostics. Methods We conducted a qualitative systematic review, following established standardized methods, of the existing literature indexed in the following databases up to 4 April 2022: OVID MEDLINE, OVID EMBASE, Scopus and Web of Science. Results Fourteen studies were identified as appropriate for inclusion in the meta-synthesis and systematic review. Most studies ( n = 12) were conducted in high-income countries, with data extracted from both mixed methods (42.9%) and qualitative (57.1%) studies. The meta-synthesis identified four overarching themes across the included studies: (1) Trust, fear, and uncertainty; (2) Data privacy and ML governance; (3) Impact on healthcare delivery and access; and (4) Consumers want to be engaged. Conclusion The current evidence demonstrates consumers’ understandings of AI/ML for medical diagnosis are complex. Consumers express a complex combination of both hesitancy and support towards AI/ML in healthcare diagnosis. Importantly, their views of the use of AI/ML in medical diagnosis are influenced by the perceived trustworthiness of their healthcare providers who use these AI/ML tools. Consumers recognize the potential for AI/ML tools to improve diagnostic accuracy, efficiency and access, and express a strong interest to be engaged in the development and implementation process of AI/ML into routine healthcare.https://doi.org/10.1177/20552076241288631 |
| spellingShingle | Jacqueline H Stephens Celine Northcott Brianna F Poirier Trent Lewis Consumer opinion on the use of machine learning in healthcare settings: A qualitative systematic review Digital Health |
| title | Consumer opinion on the use of machine learning in healthcare settings: A qualitative systematic review |
| title_full | Consumer opinion on the use of machine learning in healthcare settings: A qualitative systematic review |
| title_fullStr | Consumer opinion on the use of machine learning in healthcare settings: A qualitative systematic review |
| title_full_unstemmed | Consumer opinion on the use of machine learning in healthcare settings: A qualitative systematic review |
| title_short | Consumer opinion on the use of machine learning in healthcare settings: A qualitative systematic review |
| title_sort | consumer opinion on the use of machine learning in healthcare settings a qualitative systematic review |
| url | https://doi.org/10.1177/20552076241288631 |
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