Use and Control of Artificial Intelligence in Patients Across the Medical Workflow: Single-Center Questionnaire Study of Patient Perspectives
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Abstract
BACKGROUND: Artificial intelligence (AI) is gaining increasing importance in many medical specialties, yet data on patients' opinions on the use of AI in medicine are scarce. OBJECTIVE: This study aimed to investigate patients' opinions on the use of AI in different aspects of the medical workflow and the level of control and supervision under which they would deem the application of AI in medicine acceptable. METHODS: Patients scheduled for computed tomography or magnetic resonance imaging voluntarily participated in an anonymized questionnaire between February 10, 2020, and May 24, 2020. Patient information, confidence in physicians vs AI in different clinical tasks, opinions on the control of AI, preference in cases of disagreement between AI and physicians, and acceptance of the use of AI for diagnosing and treating diseases of different severity were recorded. RESULTS: In total, 229 patients participated. Patients favored physicians over AI for all clinical tasks except for treatment planning based on current scientific evidence. In case of disagreement between physicians and AI regarding diagnosis and treatment planning, most patients preferred the physician's opinion to AI (96.2% [153/159] vs 3.8% [6/159] and 94.8% [146/154] vs 5.2% [8/154], respectively; P=.001). AI supervised by a physician was considered more acceptable than AI without physician supervision at diagnosis (confidence rating 3.90 [SD 1.20] vs 1.64 [SD 1.03], respectively; P=.001) and therapy (3.77 [SD 1.18] vs 1.57 [SD 0.96], respectively; P=.001). CONCLUSIONS: Patients favored physicians over AI in most clinical tasks and strongly preferred an application of AI with physician supervision. However, patients acknowledged that AI could help physicians integrate the most recent scientific evidence into medical care. Application of AI in medicine should be disclosed and controlled to protect patient interests and meet ethical standards.
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Patients have very strong preferences for physicians' opinions over an AI's opinion for most clinical tasks, and for diagnostic AI to be used under physician supervision rather than autonomously.
“They found very strong preferences for physicians' opinions over an AI's opinion for most clinical tasks, and for diagnostic AI to be used under physician supervision rather than to be used autonomously.”
From Medical AI and human dignity: Contrasting perceptions of human and artificially intelligent (AI) decision making in diagnostic and medical resource allocation contexts (2022) · cites this paper as Lennartz et al., 2021
Existing literature shows that people prefer human decision makers over AI in various diagnostic contexts.
“Existing literature shows that people prefer human decision makers over AI in various diagnostic contexts (e.g., Lennartz et al., 2021;Yokoi et al., 2021).”
From Medical AI and human dignity: Contrasting perceptions of human and artificially intelligent (AI) decision making in diagnostic and medical resource allocation contexts (2022) · cites this paper as Lennartz et al., 2021
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- Authors
- Lennartz, S., Dratsch, T., Zopfs, D., Persigehl, T., Maintz, D., Große Hokamp, N., & Pinto dos Santos, D.
- Affiliations
- University of Cologne (DE)
- ORCID
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2021-02-17
- Volume
- 23
- Issue
- 2
- Pages
- e24221
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC7929746
- OpenAlex
- W3110169284
- Cited by
- 153
- References
- 29
- Citations by year
- 2026: 33 · 2025: 46 · 2024: 33 · 2023: 20 · 2022: 17 · 2021: 3
- FWCI
- 2.94
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Kölner Universitäts PublikationsServer (Universität zu Köln) (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Deutsche Forschungsgemeinschaft (426969820, LE 4401/1-1)
- Universität zu Köln
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- artificial intelligence in healthcare, patient perspectives, clinical workflow, diagnostic decision-making, treatment planning, trust in AI, AI disclosure, AI ethics, computed tomography, magnetic resonance imaging
- MeSH terms
- Adolescent, Adult, Aged, Aged, 80 and over, Artificial Intelligence, Delivery of Health Care, Humans, Medicine, Middle Aged, Patient Participation, Surveys and Questionnaires, Young Adult, Workflow
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