Felix Busch, Lena Hoffmann, Lina Xu, Long Jiang Zhang, Bin Hu, Ignacio García-Juárez, Liz N Toapanta-Yanchapaxi, Natalia Gorelik, Valérie Gorelik, Gaston A Rodriguez-Granillo, Carlos Ferrarotti, Nguyen N Cuong, Chau A P Thi, Murat Tuncel, Gürsan Kaya, Sergio M Solis-Barquero, Maria C Mendez Avila, Nevena G Ivanova, Felipe C Kitamura, Karina Y I Hayama, Monserrat L Puntunet Bates, Pedro Iturralde Torres, Esteban Ortiz-Prado, Juan S Izquierdo-Condoy, Gilbert M Schwarz, Jochen G Hofstaetter, Michihiro Hide, Konagi Takeda, Barbara Peric, Gašper Pilko, Hans O Thulesius, Thomas Lindow, Israel K Kolawole, Samuel Adegboyega Olatoke, Andrzej Grzybowski, Alexandru Corlateanu, Oana-Simina Iaconi, Ting Li, Izabela Domitrz, Katarzyna Kepczynska, Matúš Mihalcin, Lenka Fašaneková, Tomasz Zatonski, Katarzyna Fulek, András Molnár, Stefani Maihoub, Zenewton A da Silva Gama, Luca Saba, Petros Sountoulides, Marcus R Makowski, Hugo J W L Aerts, Lisa C Adams, Keno K Bressem, Álvaro Aceña Navarro, Catarina Águas, Martina Aineseder, Muaed Alomar, Rashid Al Sliman, Gautam Anand, Salita Angkurawaranon, Shuhei Aoki, Samuel Arkoh, Gizem Ashraf, Yesi Astri, Sameer Bakhshi, Nuru Y Bayramov, Antonis Billis, Almir G V Bitencourt, Anetta Bolejko, Antonio J Bollas Becerra, Joe Bwambale, Andreia Capela, Riccardo Cau, Kelly R Chacon-Acevedo, Tafadzwa L Chaunzwa, Rubens Chojniak, Warren Clements, Renato Cuocolo, Victor Dahlblom, Kelienny de Meneses Sousa, Jorge Esteban Villarrubia, Vijay B Desai, Ajaya K Dhakal, Virginia Dignum, Rubens G Feijo Andrade, Giovanna Ferraioli, Shuvadeep Ganguly, Harshit Garg, Cvetanka Gjerakaroska Savevska, Marija Gjerakaroska Radovikj, Anastasia Gkartzoni, Luis Gorospe, Ian Griffin, Martin Hadamitzky, Martin Hakorimana Ndahiro, Alessa Hering, Bruno Hochhegger, Mehriban R Huseynova, Fujimaro Ishida, Nisha Jha, Lili Jiang, Rawen Kader, Helen Kavnoudias, Clément Klein, George Kolostoumpis, Abraham Koshy, Nicholas A Kruger, Alexander Löser, Marko Lucijanic, Despoina Mantziari, Gaelle Margue, Sonyia McFadden, Masahiro Miyake, Wipawee Morakote, Issa Ngabonziza, Thao T Nguyen, Stefan M Niehues, Marc Nortje, Subish Palaian, Natalia V Pentara, Rui P Pereira de Almeida, Gianluigi Poma, Mitayani Purwoko, Nikolaos Pyrgidis, Vasileios Rafailidis, Clare Rainey, João C Ribeiro, Nicolás Rozo Agudelo, Keina Sado, Julia M Saidman, Pedro J Saturno-Hernandez, Vidyani Suryadevara, Gerald B Schulz, Ena Soric, Javier Soto-Pérez-Olivares, Arnaldo Stanzione, Julian Peter Struck, Hiroyuki Takaoka, Satoru Tanioka, Tran T M Huyen, Daniel Truhn, Elon H C van Dijk, Peter van Wijngaarden, Yuan-Cheng Wang, Matthias Weidlich, Shuhang Zhang
{"title":"Multinational Attitudes Toward AI in Health Care and Diagnostics Among Hospital Patients.","authors":"Felix Busch, Lena Hoffmann, Lina Xu, Long Jiang Zhang, Bin Hu, Ignacio García-Juárez, Liz N Toapanta-Yanchapaxi, Natalia Gorelik, Valérie Gorelik, Gaston A Rodriguez-Granillo, Carlos Ferrarotti, Nguyen N Cuong, Chau A P Thi, Murat Tuncel, Gürsan Kaya, Sergio M Solis-Barquero, Maria C Mendez Avila, Nevena G Ivanova, Felipe C Kitamura, Karina Y I Hayama, Monserrat L Puntunet Bates, Pedro Iturralde Torres, Esteban Ortiz-Prado, Juan S Izquierdo-Condoy, Gilbert M Schwarz, Jochen G Hofstaetter, Michihiro Hide, Konagi Takeda, Barbara Peric, Gašper Pilko, Hans O Thulesius, Thomas Lindow, Israel K Kolawole, Samuel Adegboyega Olatoke, Andrzej Grzybowski, Alexandru Corlateanu, Oana-Simina Iaconi, Ting Li, Izabela Domitrz, Katarzyna Kepczynska, Matúš Mihalcin, Lenka Fašaneková, Tomasz Zatonski, Katarzyna Fulek, András Molnár, Stefani Maihoub, Zenewton A da Silva Gama, Luca Saba, Petros Sountoulides, Marcus R Makowski, Hugo J W L Aerts, Lisa C Adams, Keno K Bressem, Álvaro Aceña Navarro, Catarina Águas, Martina Aineseder, Muaed Alomar, Rashid Al Sliman, Gautam Anand, Salita Angkurawaranon, Shuhei Aoki, Samuel Arkoh, Gizem Ashraf, Yesi Astri, Sameer Bakhshi, Nuru Y Bayramov, Antonis Billis, Almir G V Bitencourt, Anetta Bolejko, Antonio J Bollas Becerra, Joe Bwambale, Andreia Capela, Riccardo Cau, Kelly R Chacon-Acevedo, Tafadzwa L Chaunzwa, Rubens Chojniak, Warren Clements, Renato Cuocolo, Victor Dahlblom, Kelienny de Meneses Sousa, Jorge Esteban Villarrubia, Vijay B Desai, Ajaya K Dhakal, Virginia Dignum, Rubens G Feijo Andrade, Giovanna Ferraioli, Shuvadeep Ganguly, Harshit Garg, Cvetanka Gjerakaroska Savevska, Marija Gjerakaroska Radovikj, Anastasia Gkartzoni, Luis Gorospe, Ian Griffin, Martin Hadamitzky, Martin Hakorimana Ndahiro, Alessa Hering, Bruno Hochhegger, Mehriban R Huseynova, Fujimaro Ishida, Nisha Jha, Lili Jiang, Rawen Kader, Helen Kavnoudias, Clément Klein, George Kolostoumpis, Abraham Koshy, Nicholas A Kruger, Alexander Löser, Marko Lucijanic, Despoina Mantziari, Gaelle Margue, Sonyia McFadden, Masahiro Miyake, Wipawee Morakote, Issa Ngabonziza, Thao T Nguyen, Stefan M Niehues, Marc Nortje, Subish Palaian, Natalia V Pentara, Rui P Pereira de Almeida, Gianluigi Poma, Mitayani Purwoko, Nikolaos Pyrgidis, Vasileios Rafailidis, Clare Rainey, João C Ribeiro, Nicolás Rozo Agudelo, Keina Sado, Julia M Saidman, Pedro J Saturno-Hernandez, Vidyani Suryadevara, Gerald B Schulz, Ena Soric, Javier Soto-Pérez-Olivares, Arnaldo Stanzione, Julian Peter Struck, Hiroyuki Takaoka, Satoru Tanioka, Tran T M Huyen, Daniel Truhn, Elon H C van Dijk, Peter van Wijngaarden, Yuan-Cheng Wang, Matthias Weidlich, Shuhang Zhang","doi":"10.1001/jamanetworkopen.2025.14452","DOIUrl":null,"url":null,"abstract":"<p><strong>Importance: </strong>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.</p><p><strong>Objectives: </strong>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.</p><p><strong>Design, setting, and participants: </strong>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.</p><p><strong>Exposure: </strong>Information sheets and paper surveys handed out by hospital staff and posted in conspicuous hospital locations.</p><p><strong>Main outcomes and measures: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions and relevance: </strong>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.</p>","PeriodicalId":14694,"journal":{"name":"JAMA Network Open","volume":"8 6","pages":"e2514452"},"PeriodicalIF":10.5000,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12152705/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"JAMA Network Open","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1001/jamanetworkopen.2025.14452","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍：
JAMA Network Open, a member of the esteemed JAMA Network, stands as an international, peer-reviewed, open-access general medical journal.The publication is dedicated to disseminating research across various health disciplines and countries, encompassing clinical care, innovation in health care, health policy, and global health.
JAMA Network Open caters to clinicians, investigators, and policymakers, providing a platform for valuable insights and advancements in the medical field. As part of the JAMA Network, a consortium of peer-reviewed general medical and specialty publications, JAMA Network Open contributes to the collective knowledge and understanding within the medical community.
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