CoChat
Sign Up
Cited by 128Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AIArtificial Intelligence in Healthcare

The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.

Multinational Attitudes Toward AI in Health Care and Diagnostics Among Hospital Patients

Open access
Busch, Felix, Hoffmann, Lena, Xu, Lina, Zhang, Long Jiang, Hu, Bin, García-Juárez, Ignacio, Toapanta-Yanchapaxi, Liz N., Gorelik, Natalia, Gorelik, Valérie, Rodriguez-Granillo, Gaston A., Ferrarotti, Carlos, Cuong, Nguyen N., Thi, Chau A. P., Tuncel, Murat, Kaya, Gürsan, Solis-Barquero, Sergio M., Mendez Avila, Maria C., Ivanova, Nevena G., Kitamura, Felipe C., Hayama, Karina Y. I., Puntunet Bates, Monserrat L., Torres, Pedro Iturralde, Ortiz-Prado, Esteban, Izquierdo-Condoy, Juan S., Schwarz, Gilbert M., Hofstaetter, Jochen G., Hide, Michihiro, Takeda, Konagi, Perić, Barbara, Pilko, Gašper, Thulesius, Hans O., Lindow, Thomas, Kolawole, Israel K., Olatoke, Samuel Adegboyega, Grzybowski, Andrzej, Corlateanu, Alexandru, Iaconi, Oana-Simina, Li, Ting, Domitrz, Izabela, Kępczyńska, Katarzyna, Mihalčin, Matúš, Fašaneková, Lenka, Zatoński, Tomasz, Fułek, Katarzyna, Molnár, András, Maihoub, Stefani, da Silva Gama, Zenewton A., Saba, Luca, Sountoulides, Petros, Makowski, Marcus R., Aerts, Hugo J. W. L., Adams, Lisa C., Bressem, Keno K., COMFORT consortium, Navarro, Álvaro Aceña, Águas, Catarina, Aineseder, Martina, Alomar, Muaed, Al Sliman, Rashid, Anand, Gautam, Angkurawaranon, Salita, Aoki, Shuhei, Arkoh, Samuel, Ashraf, Gizem, Astri, Yesi, Bakhshi, Sameer, Bayramov, Nuru Y., Billis, Antonis, Bitencourt, Almir G. V., Bolejko, Anetta, Bollas Becerra, Antonio J., Bwambale, Joe, Capela, Andreia, Cau, Riccardo, Chacon-Acevedo, Kelly R., Chaunzwa, Tafadzwa L., Chojniak, Rubens, Clements, Warren, Cuocolo, Renato, Dahlblom, Victor, Sousa, Kelienny de Meneses, Villarrubia, Jorge Esteban, Desai, Vijay B., Dhakal, Ajaya K., Dignum, Virginia, Andrade, Rubens G. Feijo, Ferraioli, Giovanna, Ganguly, Shuvadeep, Garg, Harshit, Savevska, Cvetanka Gjerakaroska, Radovikj, Marija Gjerakaroska, Gkartzoni, Anastasia, Gorospe, Luis, Griffin, Ian, Hadamitzky, Martin, Ndahiro, Martin Hakorimana, Hering, Alessa, Hochhegger, Bruno, Huseynova, Mehriban R., Ishida, Fujimaro, Jha, Nisha, Jiang, Lili, Kader, Rawen, Kavnoudias, Helen, Klein, Clément, Kolostoumpis, George, Koshy, Abraham, Kruger, Nicholas A., Löser, Alexander, Lucijanic, Marko, Mantziari, Despoina, Margue, Gaelle, McFadden, Sonyia, Miyake, Masahiro, Morakote, Wipawee, Ngabonziza, Issa, Nguyen, Thao T., Niehues, Stefan M., Nortje, Marc, Palaian, Subish, Pentara, Natalia V., de Almeida, Rui P. Pereira, Poma, Gianluigi, Purwoko, Mitayani, Pyrgidis, Nikolaos, Rafailidis, Vasileios, Rainey, Clare, Ribeiro, João C., Agudelo, Nicolás Rozo, Sado, Keina, Saidman, Julia M., Saturno-Hernandez, Pedro J., Suryadevara, Vidyani, Schulz, Gerald B., Soric, Ena, Soto-Pérez-Olivares, Javier, Stanzione, Arnaldo, Struck, Julian Peter, Takaoka, Hiroyuki, Tanioka, Satoru, Huyen, Tran T. M., Truhn, Daniel, van Dijk, Elon H. C., van Wijngaarden, Peter, Wang, Yuan-Cheng, Weidlich, Matthias, Zhang, Shuhang·2025·JAMA Network Open
DOI 10.1001/jamanetworkopen.2025.14452PMC PMC12152705

Published version of a preprint

Open this paper in CoChat — free account

Abstract

Importance: The successful implementation of artificial intelligence (AI) in health care depends on its acceptance by key stakeholders, particularly patients, who are the primary beneficiaries of AI-driven outcomes. Objectives: To survey hospital patients to investigate their trust, concerns, and preferences toward the use of AI in health care and diagnostics and to assess the sociodemographic factors associated with patient attitudes. Design, Setting, and Participants: This cross-sectional study developed and implemented an anonymous quantitative survey between February 1 and November 1, 2023, using a nonprobability sample at 74 hospitals in 43 countries. Participants included hospital patients 18 years of age or older who agreed with voluntary participation in the survey presented in 1 of 26 languages. Exposure: Information sheets and paper surveys handed out by hospital staff and posted in conspicuous hospital locations. Main Outcomes and Measures: The primary outcome was participant responses to a 26-item instrument containing a general data section (8 items) and 3 dimensions (trust in AI, AI and diagnosis, preferences and concerns toward AI) with 6 items each. Subgroup analyses used cumulative link mixed and binary mixed-effects models. Results: In total, 13 806 patients participated, including 8951 (64.8%) in the Global North and 4855 (35.2%) in the Global South. Their median (IQR) age was 48 (34-62) years, and 6973 (50.5%) were male. The survey results indicated a predominantly favorable general view of AI in health care, with 57.6% of respondents (7775 of 13 502) expressing a positive attitude. However, attitudes exhibited notable variation based on demographic characteristics, health status, and technological literacy. Female respondents (3511 of 6318 [55.6%]) exhibited fewer positive attitudes toward AI use in medicine than male respondents (4057 of 6864 [59.1%]), and participants with poorer health status exhibited fewer positive attitudes toward AI use in medicine (eg, 58 of 199 [29.2%] with rather negative views) than patients with very good health (eg, 134 of 2538 [5.3%] with rather negative views). Conversely, higher levels of AI knowledge and frequent use of technology devices were associated with more positive attitudes. Notably, fewer than half of the participants expressed positive attitudes regarding all items pertaining to trust in AI. The lowest level of trust was observed for the accuracy of AI in providing information regarding treatment responses (5637 of 13 480 respondents [41.8%] trusted AI). Patients preferred explainable AI (8816 of 12 563 [70.2%]) and physician-led decision-making (9222 of 12 652 [72.9%]), even if it meant slightly compromised accuracy. Conclusions and Relevance: In this cross-sectional study of patient attitudes toward AI use in health care across 6 continents, findings indicated that tailored AI implementation strategies should take patient demographics, health status, and preferences for explainable AI and physician oversight into account.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1001/jamanetworkopen.2025.14452

Cites

42 of 42 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1. Geographical Distribution of Participating Institutions on World Map The size of the blue dots refers to the proportion of respondents per institution relative to the total number of respondents. Countries with at least 1 participating institution are highlighted in green.
Figure 1. Geographical Distribution of Participating Institutions on World Map The size of the blue dots refers to the proportion of respondents per institution relative to the total number of respondents. Countries with at least 1 participating institution are highlighted in green.CC BY · PubMed Central
Figure 2. Gantt Diagrams Depicting the Results for Each Item in the Trust in Artificial Intelligence (AI) Section Q3 through Q6 represent question items 3 through 6.
Figure 2. Gantt Diagrams Depicting the Results for Each Item in the Trust in Artificial Intelligence (AI) Section Q3 through Q6 represent question items 3 through 6.CC BY · PubMed Central
Figure 3. Gantt Diagrams Depicting the Results for Each Item in the Concerns Toward AI Section Q17 through Q20 represent question items 17 through 20. AI indicates artificial intelligence.
Figure 3. Gantt Diagrams Depicting the Results for Each Item in the Concerns Toward AI Section Q17 through Q20 represent question items 17 through 20. AI indicates artificial intelligence.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 42 references

Research works printed but not linked: 3

  1. Sahni, 2024, The Economics of Artificial Intelligence: Health Care Challenges, 192
  2. 2021, Ethics and governance of artificial intelligence for health: WHO guidance: executive summary
  3. United Nations, Department of Economic and Social Affairs, Statistics Division, 1999, Standard Country or Area Codes for Statistics Use, 1999 (Revision 4)

Cited by

Sorted by citations

Related

See all

Metadata

Authors
Busch, F., Hoffmann, L., Xu, L., Zhang, L. J., Hu, B., García-Juárez, I., Toapanta-Yanchapaxi, L. N., Gorelik, N., Gorelik, V., Rodriguez-Granillo, G. A., Ferrarotti, C., Cuong, N. N., Thi, C. A. P., Tuncel, M., Kaya, G., Solis-Barquero, S. M., Mendez Avila, M. C., Ivanova, N. G., Kitamura, F. C., Hayama, K. Y. I., Puntunet Bates, M. L., Torres, P. I., Ortiz-Prado, E., Izquierdo-Condoy, J. S., Schwarz, G. M., Hofstaetter, J. G., Hide, M., Takeda, K., Perić, B., Pilko, G., Thulesius, H. O., Lindow, T., Kolawole, I. K., Olatoke, S. A., Grzybowski, A., Corlateanu, A., Iaconi, O.-S., Li, T., Domitrz, I., Kępczyńska, K., Mihalčin, M., Fašaneková, L., Zatoński, T., Fułek, K., Molnár, A., Maihoub, S., da Silva Gama, Z. A., Saba, L., Sountoulides, P., Makowski, M. R., Aerts, H. J. W. L., Adams, L. C., Bressem, K. K., COMFORT consortium, Navarro, Á. A., Águas, C., Aineseder, M., Alomar, M., Al Sliman, R., Anand, G., Angkurawaranon, S., Aoki, S., Arkoh, S., Ashraf, G., Astri, Y., Bakhshi, S., Bayramov, N. Y., Billis, A., Bitencourt, A. G. V., Bolejko, A., Bollas Becerra, A. J., Bwambale, J., Capela, A., Cau, R., Chacon-Acevedo, K. R., Chaunzwa, T. L., Chojniak, R., Clements, W., Cuocolo, R., Dahlblom, V., Sousa, K. D. M., Villarrubia, J. E., Desai, V. B., Dhakal, A. K., Dignum, V., Andrade, R. G. F., Ferraioli, G., Ganguly, S., Garg, H., Savevska, C. G., Radovikj, M. G., Gkartzoni, A., Gorospe, L., Griffin, I., Hadamitzky, M., Ndahiro, M. H., Hering, A., Hochhegger, B., Huseynova, M. R., Ishida, F., Jha, N., Jiang, L., Kader, R., Kavnoudias, H., Klein, C., Kolostoumpis, G., Koshy, A., Kruger, N. A., Löser, A., Lucijanic, M., Mantziari, D., Margue, G., McFadden, S., Miyake, M., Morakote, W., Ngabonziza, I., Nguyen, T. T., Niehues, S. M., Nortje, M., Palaian, S., Pentara, N. V., de Almeida, R. P. P., Poma, G., Purwoko, M., Pyrgidis, N., Rafailidis, V., Rainey, C., Ribeiro, J. C., Agudelo, N. R., Sado, K., Saidman, J. M., Saturno-Hernandez, P. J., Suryadevara, V., Schulz, G. B., Soric, E., Soto-Pérez-Olivares, J., Stanzione, A., Struck, J. P., Takaoka, H., Tanioka, S., Huyen, T. T. M., Truhn, D., van Dijk, E. H. C., van Wijngaarden, P., Wang, Y.-C., Weidlich, M., & Zhang, S.
Affiliations
  • TUM Klinikum (DE)
  • Humboldt-Universität zu Berlin (DE)
  • Nanjing General Hospital of Nanjing Military Command (CN)
  • Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán (MX)
  • McGill University Health Centre (CA)
  • Dawson College (CA)
  • Center for Medical Education and Clinical Research-National Council for Scientific and Technical Research (CEMIC-CONICET), Autonomous City of Buenos Aires, Argentina
  • Centro de Educación Médica e Investigaciones Clínicas Norberto Quirno (AR)
  • Hanoi Medical University Hospital (VN)
  • Hanoi Medical University (VN)
  • Hacettepe University (TR)
  • Universidad de Costa Rica (CR)
  • Department of Diagnostic and Therapeutic Imaging, Escuela de Tecnologias en Salud, Universidad de Costa Rica, San José, Costa Rica
  • Medical University Plovdiv (BG)
  • DASA (Brazil) (BR)
  • Universidade Federal de São Paulo (BR)
  • Unidad de Calidad, National Institute of Cardiology Ignacio Chávez, Mexico City, Mexico
  • Instituto Nacional de Cardiología (MX)
  • One Health Research Group, Faculty of Health Science, Universidad de Las Américas, Quito, Ecuador
  • Medical University of Vienna (AT)
  • Orthopaedic Hospital Speising (AT)
  • Hiroshima City Hiroshima Citizens Hospital (JP)
  • University of Ljubljana (SI)
  • Linnaeus University (SE)
  • Lund University (SE)
  • University of Ilorin (NG)
  • Foundation for the Development of Ophthalmology (PL)
  • Department of Respiratory Medicine and Allergology, Nicolae Testemițanu State University of Medicine and Pharmacy, Chișinău, Republic of Moldova
  • Nicolae Testemițanu State University of Medicine and Pharmacy (MD)
  • Shanghai Jiao Tong University (CN)
  • Medical University of Warsaw (PL)
  • Masaryk University (CZ)
  • Wroclaw Medical University (PL)
  • Semmelweis University (HU)
  • Department of Collective Health, Federal University of Rio Grande do Norte, Natal, Brazil
  • Azienda Ospedaliero-Universitaria Cagliari (IT)
  • Aristotle University of Thessaloniki (GR)
  • Brigham and Women's Hospital (US)
  • Hospital Universitario Fundación Jiménez Díaz (ES)
  • Department of Radiology, Algarve University Hospital Center, Faro, Portugal
  • Hospital Italiano de Buenos Aires (AR)
  • Ajman University (AE)
  • Department of Urology, Faculty of Health Sciences Brandenburg, Brandenburg Medical School Theodor Fontane, Brandenburg an der Havel, Germany
  • Max Healthcare (IN)
  • Chiang Mai University (TH)
  • Chiba University (JP)
  • Methodist Hospital (GH)
  • The Royal Victorian Eye & Ear Hospital (AU)
  • Universitas Muhammadiyah Palembang (ID)
  • DR. B.R.A. Institute Rotary Cancer Hospital (IN)
  • Azerbaijan Medical University (AZ)
  • AC Camargo Hospital (BR)
  • Department of Cardiology, Hospital Universitario Fundación Jiménez Díaz, Madrid, Spain
  • Mulago Hospital (UG)
  • Centro Hospitalar de Vila Nova de Gaia (PT)
  • Instituto Global de Excelencia Clínica, Grupo de investigación Traslacional, Keralty, Bogotá D.C., Colombia
  • The Alfred Hospital (AU)
  • University of Salerno (IT)
  • Universidade Federal do Rio Grande do Norte (BR)
  • Department of Medical Oncology, Hospital Universitario 12 de Octubre, Madrid, Spain
  • KIST Medical College (NP)
  • Umeå University (SE)
  • Department of Radiology, Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Brazil
  • University of Pavia (IT)
  • Ss. Cyril and Methodius University in Skopje (MK)
  • Instituto Ramón y Cajal de Investigación Sanitaria (ES)
  • University of Florida Health (US)
  • Deutsches Herzzentrum München (DE)
  • Ministry of Health (RW)
  • Radboud University Medical Center (NL)
  • Department of Radiology, University of Florida, Gainesville
  • Mie Chuo Medical Center (JP)
  • University College London (GB)
  • Hôpital Pellegrin (FR)
  • European Cancer Patient Coalition (ECPC), Brussels, Belgium
  • Lakeshore Hospital (IN)
  • University of Cape Town (ZA)
  • Berliner Hochschule für Technik (DE)
  • Department of Hematology, Clinical Hospital Dubrava, Zagreb, Croatia
  • School of Health Sciences, Londonderry, Northern Ireland, United Kingdom
  • Kyoto University (JP)
  • Hue University (VN)
  • Department of Radiology, University of Algarve, Faro, Portugal
  • Policlinico San Matteo Fondazione (IT)
  • Ludwig-Maximilians-Universität München (DE)
  • University of Coimbra (PT)
  • AXA Chair in Healthcare Quality, CIEE, National Institute of Public Health, Cuernavaca, Mexico
  • Stanford Medicine (US)
  • Department of Radiology, Ramón y Cajal University Hospital, IRYCIS, Madrid, Spain
  • University of Naples Federico II (IT)
  • Medizinische Hochschule Brandenburg Theodor Fontane (DE)
  • Chiba University Hospital (JP)
  • Mie University (JP)
  • Universitätsklinikum Aachen (DE)
  • Leiden University Medical Center (NL)
  • The University of Melbourne (AU)
  • Department of Radiology, Zhongda Hospital Southeast University, Nanjing, China
  • Zhongda Hospital Southeast University (CN)
Corresponding author
Bressem, Keno K.
ORCID
Venue
JAMA Network Open
Publisher
American Medical Association (AMA)
Published
2025-06-10
Volume
8
Issue
6
Pages
e2514452
ISSN
2574-3805 (ISSN-L)
DOI
10.1001/jamanetworkopen.2025.14452
PMC
PMC12152705
OpenAlex
W4411177851
Cited by
124
References
42
Citations by year
2026: 99 · 2025: 24
FWCI
29.61
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JAMA Network Open (publishedVersion, cc-by)
  • medRxiv (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Research Publications (Maastricht University) (publishedVersion, cc-by)
  • Ulster University Research Portal (Ulster University) (publishedVersion, cc-by)
  • Diva portal (Dalarna University Library) (submittedVersion, other-oa)
  • Leiden Repository (Leiden University) (submittedVersion, cc-by)
  • UNICA IRIS Institutional Research Information System (University of Cagliari) (submittedVersion, cc-by)
  • Open Repository and Bibliography (University of Luxembourg) (submittedVersion, cc-by-nc)
  • Radboud Repository (Radboud University) (submittedVersion)
  • Hispana (submittedVersion, cc-by)
  • Minerva Access (University of Melbourne) (submittedVersion, cc-by)
  • Lund University Publications (Lund University) (submittedVersion)
  • mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
  • Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
  • RWTH Publications (RWTH Aachen) (submittedVersion, other-oa)
  • Biblos-e Archivo (Universidad Autónoma de Madrid) (submittedVersion, public-domain)
  • Universidade do Algarve (submittedVersion, cc-by)
  • Leiden Repository (Leiden University) (submittedVersion)
Funders
  • Brigham and Women's Hospital
  • Massachusetts General Hospital
  • Università degli Studi di Napoli Federico II
  • University College London
  • European Commission (101079894)
  • Monash University
  • Universiteit Maastricht
  • Semmelweis Egyetem
  • Lunds Universitet
  • Korea Institute of Science and Technology
  • Warszawski Uniwersytet Medyczny
  • Università degli Studi di Pavia
  • Umeå Universitet
  • Shanghai Jiao Tong University
  • Leids Universitair Medisch Centrum
  • Technische Universität München
  • Radboud Universitair Medisch Centrum
  • Aristotle University of Thessaloniki
  • Università degli Studi di Salerno
  • University of Cape Town
  • Freie Universität Berlin
  • Klinička Bolnica Dubrava
  • Medicinski Fakultet, Sveučilište u Zagrebu
  • Southeast University
  • Chiba University
  • Universidade Federal do Rio Grande do Norte
  • Uniwersytet Medyczny im. Piastów Slaskich we Wroclawiu
  • Lékařská fakulta, Masarykova univerzita
  • Đại học Huế
  • Ajman University
  • Renji Hospital
  • School of Medicine, Stanford University
  • Dipartimento di Scienze Biomediche Avanzate, Università degli Studi di Napoli Federico II
  • School of Medicine, Shanghai Jiao Tong University
  • Faculty of Medicine, Chiang Mai University
  • CARIM School for Cardiovascular Diseases, Universiteit Maastricht
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
artificial intelligence in healthcare, trust in AI, patient preferences, patient concerns, sociodemographic factors, gender differences, health status, technological literacy, AI knowledge, hospitalized patients, cross-cultural comparison, multilingual survey
MeSH terms
Adult, Aged, Artificial Intelligence, Cross-Sectional Studies, Delivery of Health Care, Female, Hospitals, Humans, Male, Middle Aged, Surveys and Questionnaires, Trust, Internationality

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review