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

  • COMFORT Consortium
  • Technical University of Munich
  • Charité – Universitätsmedizin Berlin
  • Nanjing University
  • Instituto Nacional de Ciencias Medicas y Nutricion Salvador Zubiran
  • McGill University
  • Dawson College
  • Consejo Nacional de Investigaciones Científicas y Técnicas
  • Centro de Educación Médica e Investigaciones Clínicas Norberto Quirno
  • Medical University Hospital Hanoi
  • Hanoi Medical University
  • Hacettepe University
  • University of Costa Rica
  • Medical University of Plovdiv
  • St Karidad MHAT
  • Universidade Federal de São Paulo
  • Diagnosticos da America SA (DASA)
  • Instituto Nacional de Cardiologia Ignacio Chavez
  • Universidad de las Américas - Ecuador
  • Medical University of Vienna
  • Hospital Vienna-Speising
  • Orthopaedic Hospital Speising
  • Hiroshima City Hiroshima Citizens Hospital
  • Institute of Oncology Ljubljana
  • University of Ljubljana
  • Research and Development Department Region Kronoberg
  • Linnaeus University
  • Växjö Central Hospital
  • Lund University
  • University of Ilorin
  • Foundation for Ophthalmology Development
  • State University of Medicine and Pharmacy "Nicolae Testemiţanu"
  • Shanghai Jiao Tong University
  • Medical University of Warsaw
  • Bielanski Hospital
  • Masaryk University
  • Wrocław Medical University
  • Semmelweis University
  • Universidade Federal do Rio Grande do Norte
  • University Hospital of Cagliari
  • Aristotle University of Thessaloniki
  • Harvard University
  • Brigham and Women’s Hospital
  • Maastricht University
  • Dana-Farber Cancer Institute
  • Universidad Autónoma de Madrid
  • Algarve University Hospital Center
  • Hospital Italiano de Buenos Aires
  • Brandenburg Medical School Theodor Fontane
  • Max Healthcare
  • Chiang Mai University
  • Chiba University
  • Wenchi Methodist Hospital
  • University of Melbourne
  • Universitas Muhammadiyah Palembang
  • All India Institute of Medical Sciences, New Delhi
  • Azerbaycan Tibb Universiteti
  • Hospital A.C. Camargo
  • Mulago National Referral Hospital
  • Centro Hospitalar de Vila Nova de Gaia
  • Associação de Investigação de Cuidados de Suporte em Oncologia (AICSO)
  • Grupo de Investigación Traslacional
  • Alfred Health
  • Monash University
  • National Trauma Research Institute
  • University of Salerno
  • Hospital Universitario
  • KIST Medical College
  • Umeå University
  • Pontifícia Universidade Católica do Rio Grande do Sul
  • University of Pavia
  • SS Cyril and Methodius University in Skopje
  • Hospital Ramon y Cajal
  • University of Florida
  • Byumba Hospital
  • Radboud University Nijmegen
  • Fraunhofer Institute for Digital Medicine
  • National Hospital Organization Mie Chuo Medical Center
  • University College London
  • Groupe hospitalier Pellegrin
  • European Cancer Patient Coalition
  • Lakeshore Hospital
  • University of Cape Town
  • Beuth University of Applied Sciences Berlin
  • Clinical Hospital Dubrava
  • University of Zagreb
  • School of Health Sciences
  • Kyoto University
  • Hue University
  • University of Évora
  • University of Algarve
  • IRCCS Fondazione Policlinico San Matteo - Pavia
  • Ludwig Maximilian University of Munich
  • University of Coimbra
  • Instituto Nacional de Salud Publica
  • Stanford University
  • University of Naples Federico II
  • Free University of Berlin
  • Mie University
  • RWTH Aachen University
  • Alrijne Hospital
  • Leiden University
  • Department of Surgery
  • Southeast University, Nanjing

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

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.

Original languageEnglish
Article numbere2514452
JournalJAMA Network Open
Volume8
Issue number6
DOIs
StatePublished - Jun 2025

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