Future Medical Artificial Intelligence Application Requirements and Expectations of Physicians in German University Hospitals: Web-Based Survey
Journal of Medical Internet Research, vol. 23, pp. e26646
Abstract
BACKGROUND: The increasing development of artificial intelligence (AI) systems in medicine driven by researchers and entrepreneurs goes along with enormous expectations for medical care advancement. AI might change the clinical practice of physicians from almost all medical disciplines and in most areas of health care. While expectations for AI in medicine are high, practical implementations of AI for clinical practice are still scarce in Germany. Moreover, physicians' requirements and expectations of AI in medicine and their opinion on the usage of anonymized patient data for clinical and biomedical research have not been investigated widely in German university hospitals. OBJECTIVE: This study aimed to evaluate physicians' requirements and expectations of AI in medicine and their opinion on the secondary usage of patient data for (bio)medical research (eg, for the development of machine learning algorithms) in university hospitals in Germany. METHODS: A web-based survey was conducted addressing physicians of all medical disciplines in 8 German university hospitals. Answers were given using Likert scales and general demographic responses. Physicians were asked to participate locally via email in the respective hospitals. RESULTS: =48.3, P<.001). A vast majority of physicians expected the future of medicine to be a mix of human and artificial intelligence (273/303, 90.1%) but also requested a scientific evaluation before the routine implementation of AI-based systems (276/303, 91.1%). Physicians were most optimistic that AI applications would identify drug interactions (280/303, 92.4%) to improve patient care substantially but were quite reserved regarding AI-supported diagnosis of psychiatric diseases (62/303, 20.5%). Of the respondents, 82.5% (250/303) agreed that there should be open access to anonymized patient databases for medical and biomedical research. CONCLUSIONS: Physicians in stationary patient care in German university hospitals show a generally positive attitude towards using most AI applications in medicine. Along with this optimism comes several expectations and hopes that AI will assist physicians in clinical decision making. Especially in fields of medicine where huge amounts of data are processed (eg, imaging procedures in radiology and pathology) or data are collected continuously (eg, cardiology and intensive care medicine), physicians' expectations of AI to substantially improve future patient care are high. In the study, the greatest potential was seen in the application of AI for the identification of drug interactions, assumedly due to the rising complexity of drug administration to polymorbid, polypharmacy patients. However, for the practical usage of AI in health care, regulatory and organizational challenges still have to be mastered.
Authors 8
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Universitätsklinikum Aachen · RWTH Aachen University
Affiliation as printed
Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
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Forschungszentrum Jülich · Universitätsklinikum Aachen · Jülich Supercomputing Centre · RWTH Aachen University
Affiliation as printed
Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany
SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany.; Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany
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Affiliation as printed
Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital, Jena, Germany
SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
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Universitätsklinikum Aachen · RWTH Aachen University
Affiliation as printed
Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
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Universitätsklinikum Aachen · RWTH Aachen University
Affiliation as printed
Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
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University of Iceland · Forschungszentrum Jülich · Jülich Supercomputing Centre
Affiliation as printed
Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany
SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
School of Natural Sciences and Engineering, University of Iceland, Reykjavik, Iceland
Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany.; School of Natural Sciences and Engineering, University of Iceland, Reykjavik, Iceland
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Universitätsklinikum Aachen · RWTH Aachen University
Affiliation as printed
Institute for Computational Biomedicine II, University Hospital RWTH Aachen, Aachen, Germany
SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
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Universitätsklinikum Aachen · RWTH Aachen University
Affiliation as printed
Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
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