The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
The use of artificial intelligence applications in medicine and the standard required for healthcare provider-patient briefings—an exploratory study
Open accessAbstract
Introduction: Digital Health Technologies (DHTs) are currently being funneled through legacy regulatory processes that are not adapted to the unique particularities of this new technology class. In the absence of adequate regulation of DHTs, the briefing of a patient by their healthcare provider (HCP) as a component of informed consent can present the last line of defense before potentially harmful technologies are employed on a patient. Methods: This exploratory study utilizes a case vignette of a machine learning-based technology for the diagnosis of ischemic heart disease that is presented to a group of medical students, physicians, and bioethicists. What constitutes the necessary standard and content of the HCP–patient briefings is explored using a survey ( N = 34). Whether participants actually provide a sufficient HCP–patient briefing is evaluated based on audio recordings. Results and Conclusions: We find that participants deem artificial intelligence use in medical context should be declared to patients and argue that the explanation should currently follow the standard required of other experimental procedures. Further, since our study provides indications that implementation of HCP–patient briefings lacks behind the identified standard, opportunities for incorporation of training on the use of DHTs into medical curricula and continuous training schedules should be considered.
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“Although the value of empirical research on patients’ views regarding information disclosure in the use of AI/ML in medicine has been recognized, 12 no research has yet explored what information patients consider important or significant for making treatment decisions when AI is used in a clinical setting.26–28”
From Patient perspectives on informed consent for medical AI: A web-based experiment (2024) · cites this paper as [28]
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Patient apprehensions about the use of artificial intelligence in healthcare
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Artificial intelligence in medicine and the disclosure of risks
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The regulatory gap in digital health and alternative pathways to bridge it
Iqbal, J. D., & Biller‐Andorno, N. (2022) · Health Policy and Technology · doi:10.1016/j.hlpt.2022.100663 · Cited by 67
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Patient perspectives on informed consent for medical AI: A web-based experiment
Park, H. J. (2024) · DIGITAL HEALTH · doi:10.1177/20552076241247938 · Cited by 95
AI-Enhanced Healthcare: Not a new Paradigm for Informed Consent
Pruski, M. (2024) · Journal of Bioethical Inquiry · doi:10.1007/s11673-023-10320-0 · Cited by 45
Bečulić, H., Begagić, E., Skomorac, R., Mašović, A., Selimović, E., & Pojskić, M. (2023) · Medicinski Glasnik · doi:10.17392/1661-23 · Cited by 37
Huang, A. A., & Huang, S. Y. (2023) · Health Science Reports · doi:10.1002/hsr2.1635 · Cited by 23
Integrative Medicine in Diagnostics: Current Advances and Future Prospects
Youn, B.-Y. (2023) · Diagnostics · doi:10.3390/diagnostics13193052 · Cited by 2
Metadata
- Authors
- Iqbal, J. D., & Christen, M.
- Affiliations
- University of Zurich (CH)
- Corresponding author
- Iqbal, Jeffrey David
- Venue
- DIGITAL HEALTH
- Publisher
- SAGE Publications
- Published
- 2022-01
- Volume
- 8
- Pages
- 205520762211474
- ISSN
- 2055-2076 (ISSN-L)
- PMC
- PMC9806394
- OpenAlex
- W4313055579
- Cited by
- 5
- References
- 14
- Citations by year
- 2024: 2 · 2023: 3
- FWCI
- 0.38
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Digital Health (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, cc-by-nc)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by-nc)
- Zurich Open Repository and Archive (University of Zurich)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ethics and bioethics in healthcare
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ethics in Clinical Research
- Keywords
- artificial intelligence in healthcare, digital health technologies, informed consent, patient-provider communication, ischemic heart disease diagnosis, machine learning, regulatory standards, medical education, physician training, bioethics
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