gms | German Medical Science

Gesundheit – gemeinsam. Kooperationstagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (GMDS), Deutschen Gesellschaft für Sozialmedizin und Prävention (DGSMP), Deutschen Gesellschaft für Epidemiologie (DGEpi), Deutschen Gesellschaft für Medizinische Soziologie (DGMS) und der Deutschen Gesellschaft für Public Health (DGPH)

08.09. - 13.09.2024, Dresden

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Attitudes of the German general population towards implementing artificial intelligence in medical care: a population-based survey

Meeting Abstract

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  • Sarah Negash - Institute for Medical Epidemiology, Biometrics and Informatics (IMEBI), Interdisciplinary Centre for Health Sciences, Medical Faculty of the Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
  • Henry Papon - Institute for Medical Epidemiology, Biometrics and Informatics (IMEBI), Interdisciplinary Centre for Health Sciences, Medical Faculty of the Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
  • Timo Apfelbacher - Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute for Medical Informatics, Biometrics and Epidemiology, Medical Informatics, Erlangen, Germany
  • Sude Eda Ko\u231 ?man - Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute for Medical Informatics, Biometrics and Epidemiology, Medical Informatics, Erlangen, Germany
  • Jana Gundlack - Institute of General Practice & Family Medicine, Interdisciplinary Center of Health Sciences, Medical Faculty of the Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
  • Charlotte Buch - Institute for History and Ethics of Medicine, Interdisciplinary Center for Health Sciences, Medical School of the Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
  • Jan Schildmann - Institute for History and Ethics of Medicine, Interdisciplinary Center for Health Sciences, Medical School of the Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
  • Jan Christoph - Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute for Medical Informatics, Biometrics and Epidemiology, Medical Informatics, Erlangen, Germany; Junior Research Group (Bio-)medical Data Science, Interdisciplinary Center for Health Sciences, Medical Faculty of the Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
  • Rafael Mikolajczyk - Institute for Medical Epidemiology, Biometrics and Informatics (IMEBI), Interdisciplinary Centre for Health Sciences, Medical Faculty of the Martin Luther University Halle-Wittenberg, Halle (Saale), Germany

Gesundheit – gemeinsam. Kooperationstagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (GMDS), Deutschen Gesellschaft für Sozialmedizin und Prävention (DGSMP), Deutschen Gesellschaft für Epidemiologie (DGEpi), Deutschen Gesellschaft für Medizinische Soziologie (DGMS) und der Deutschen Gesellschaft für Public Health (DGPH). Dresden, 08.-13.09.2024. Düsseldorf: German Medical Science GMS Publishing House; 2024. DocAbstr. 532

doi: 10.3205/24gmds632, urn:nbn:de:0183-24gmds6323

Published: September 6, 2024

© 2024 Negash et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. See license information at http://creativecommons.org/licenses/by/4.0/.

Outline

Text

Introduction: In recent years, implementation of artificial intelligence (AI) in medical care has gained significant attention [1]. As part of the PEAK project (Perspectives on the Use and Acceptance of Artificial Intelligence in Medical Care) this study aims to examine the attitudes of the general population regarding AI in medical care.

Methods: In the quantitative part of the explorative sequential mixed-methods study, participants of the population-based online panel HeReCa (Health Related Beliefs and Health Care Experiences in Germany) were asked to answer a questionnaire about their understanding of AI and their general acceptance of utilizing AI systems in their treatment.

Results: We report preliminary results based on 722 questionnaires, which were filled out until mid-April 2024. Respondents were between 25 and 84 years of age (mean = 58.9, SD = 13.6). 54.8% of the participants were female (42.7% male, 2.5% missing).

Overall, 53.6% of the respondents reported to have an average understanding of AI (32.4% very good/good, 13.7% very poor/poor). 79.9% of participants agreed or tended to agree with the statement that in principle they would be fine with the use of AI systems in their treatment. 11.9% disagreed or strongly disagreed, while 8.2% were undecided.

In the multivariable model, for each point of AI understanding (on a scale from “very poor” to “very good”), the agreement with the use of AI in their treatment increased by 0.32 points. With increasing age, the agreement with the use of AI decreased by 0.005 points per year. Men reported a higher agreement compared to women (by 0.19 points). The included variables accounted for approximately 12% of the variability in the outcome.

Conclusions: Our results suggest that the majority of participants of the population-based online panel seemed open to the use of AI systems in their treatment and that those agreeing also reported a better perceived understanding of AI. This could indicate that providing information about AI in the population could support positive attitudes towards AI.

The authors declare that they have no competing interests.

The authors declare that a positive ethics committee vote has been obtained.

Outline

References

1.
Crossnohere NL, Elsaid M, Paskett J, Bose-Brill S, Bridges JFP. Guidelines for Artificial Intelligence in Medicine: Literature Review and Content Analysis of Frameworks. J Med Internet Res. 2022;24(8):e36823.