Artificial Intelligence Bias in Health Care: Web-Based Survey
BackgroundResources are increasingly spent on artificial intelligence (AI) solutions for medical applications aiming to improve diagnosis, treatment, and prevention of diseases. While the need for transparency and reduction of bias in data and algorithm development has been a...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
JMIR Publications
2023-06-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2023/1/e41089 |
| _version_ | 1797733977356763136 |
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| author | Carina Nina Vorisek Caroline Stellmach Paula Josephine Mayer Sophie Anne Ines Klopfenstein Dominik Martin Bures Anke Diehl Maike Henningsen Kerstin Ritter Sylvia Thun |
| author_facet | Carina Nina Vorisek Caroline Stellmach Paula Josephine Mayer Sophie Anne Ines Klopfenstein Dominik Martin Bures Anke Diehl Maike Henningsen Kerstin Ritter Sylvia Thun |
| author_sort | Carina Nina Vorisek |
| collection | DOAJ |
| description |
BackgroundResources are increasingly spent on artificial intelligence (AI) solutions for medical applications aiming to improve diagnosis, treatment, and prevention of diseases. While the need for transparency and reduction of bias in data and algorithm development has been addressed in past studies, little is known about the knowledge and perception of bias among AI developers.
ObjectiveThis study’s objective was to survey AI specialists in health care to investigate developers’ perceptions of bias in AI algorithms for health care applications and their awareness and use of preventative measures.
MethodsA web-based survey was provided in both German and English language, comprising a maximum of 41 questions using branching logic within the REDCap web application. Only the results of participants with experience in the field of medical AI applications and complete questionnaires were included for analysis. Demographic data, technical expertise, and perceptions of fairness, as well as knowledge of biases in AI, were analyzed, and variations among gender, age, and work environment were assessed.
ResultsA total of 151 AI specialists completed the web-based survey. The median age was 30 (IQR 26-39) years, and 67% (101/151) of respondents were male. One-third rated their AI development projects as fair (47/151, 31%) or moderately fair (51/151, 34%), 12% (18/151) reported their AI to be barely fair, and 1% (2/151) not fair at all. One participant identifying as diverse rated AI developments as barely fair, and among the 2 undefined gender participants, AI developments were rated as barely fair or moderately fair, respectively. Reasons for biases selected by respondents were lack of fair data (90/132, 68%), guidelines or recommendations (65/132, 49%), or knowledge (60/132, 45%). Half of the respondents worked with image data (83/151, 55%) from 1 center only (76/151, 50%), and 35% (53/151) worked with national data exclusively.
ConclusionsThis study shows that the perception of biases in AI overall is moderately fair. Gender minorities did not once rate their AI development as fair or very fair. Therefore, further studies need to focus on minorities and women and their perceptions of AI. The results highlight the need to strengthen knowledge about bias in AI and provide guidelines on preventing biases in AI health care applications. |
| first_indexed | 2024-03-12T12:37:32Z |
| format | Article |
| id | doaj.art-9ab27f7f99cf4d1b8116445b7d454d3f |
| institution | Directory Open Access Journal |
| issn | 1438-8871 |
| language | English |
| last_indexed | 2024-03-12T12:37:32Z |
| publishDate | 2023-06-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Journal of Medical Internet Research |
| spelling | doaj.art-9ab27f7f99cf4d1b8116445b7d454d3f2023-08-29T00:05:49ZengJMIR PublicationsJournal of Medical Internet Research1438-88712023-06-0125e4108910.2196/41089Artificial Intelligence Bias in Health Care: Web-Based SurveyCarina Nina Vorisekhttps://orcid.org/0000-0001-7499-8115Caroline Stellmachhttps://orcid.org/0000-0001-6798-8533Paula Josephine Mayerhttps://orcid.org/0000-0002-6441-5913Sophie Anne Ines Klopfensteinhttps://orcid.org/0000-0002-8470-2258Dominik Martin Bureshttps://orcid.org/0009-0005-5930-2120Anke Diehlhttps://orcid.org/0000-0002-7890-6107Maike Henningsenhttps://orcid.org/0009-0008-9826-4472Kerstin Ritterhttps://orcid.org/0000-0001-7115-0020Sylvia Thunhttps://orcid.org/0000-0002-3346-6806 BackgroundResources are increasingly spent on artificial intelligence (AI) solutions for medical applications aiming to improve diagnosis, treatment, and prevention of diseases. While the need for transparency and reduction of bias in data and algorithm development has been addressed in past studies, little is known about the knowledge and perception of bias among AI developers. ObjectiveThis study’s objective was to survey AI specialists in health care to investigate developers’ perceptions of bias in AI algorithms for health care applications and their awareness and use of preventative measures. MethodsA web-based survey was provided in both German and English language, comprising a maximum of 41 questions using branching logic within the REDCap web application. Only the results of participants with experience in the field of medical AI applications and complete questionnaires were included for analysis. Demographic data, technical expertise, and perceptions of fairness, as well as knowledge of biases in AI, were analyzed, and variations among gender, age, and work environment were assessed. ResultsA total of 151 AI specialists completed the web-based survey. The median age was 30 (IQR 26-39) years, and 67% (101/151) of respondents were male. One-third rated their AI development projects as fair (47/151, 31%) or moderately fair (51/151, 34%), 12% (18/151) reported their AI to be barely fair, and 1% (2/151) not fair at all. One participant identifying as diverse rated AI developments as barely fair, and among the 2 undefined gender participants, AI developments were rated as barely fair or moderately fair, respectively. Reasons for biases selected by respondents were lack of fair data (90/132, 68%), guidelines or recommendations (65/132, 49%), or knowledge (60/132, 45%). Half of the respondents worked with image data (83/151, 55%) from 1 center only (76/151, 50%), and 35% (53/151) worked with national data exclusively. ConclusionsThis study shows that the perception of biases in AI overall is moderately fair. Gender minorities did not once rate their AI development as fair or very fair. Therefore, further studies need to focus on minorities and women and their perceptions of AI. The results highlight the need to strengthen knowledge about bias in AI and provide guidelines on preventing biases in AI health care applications.https://www.jmir.org/2023/1/e41089 |
| spellingShingle | Carina Nina Vorisek Caroline Stellmach Paula Josephine Mayer Sophie Anne Ines Klopfenstein Dominik Martin Bures Anke Diehl Maike Henningsen Kerstin Ritter Sylvia Thun Artificial Intelligence Bias in Health Care: Web-Based Survey Journal of Medical Internet Research |
| title | Artificial Intelligence Bias in Health Care: Web-Based Survey |
| title_full | Artificial Intelligence Bias in Health Care: Web-Based Survey |
| title_fullStr | Artificial Intelligence Bias in Health Care: Web-Based Survey |
| title_full_unstemmed | Artificial Intelligence Bias in Health Care: Web-Based Survey |
| title_short | Artificial Intelligence Bias in Health Care: Web-Based Survey |
| title_sort | artificial intelligence bias in health care web based survey |
| url | https://www.jmir.org/2023/1/e41089 |
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