Browsing by Author "Truhn, Daniel"
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- Global cross-sectional student survey on AI in medical, dental, and veterinary education and practice at 192 facultiesPublication . Busch, Felix; Hoffmann, Lena; Truhn, Daniel; Ortiz-Prado, Esteban; Makowski, Marcus R.; Bressem, Keno K.; Adams, Lisa C.; COMFORT consortiumBackground The successful integration of artifcial intelligence (AI) in healthcare depends on the global perspectives of all stakeholders. This study aims to answer the research question: What are the attitudes of medical, dental, and veterinary students towards AI in education and practice, and what are the regional diferences in these perceptions? Methods An anonymous online survey was developed based on a literature review and expert panel discussions. The survey assessed students’ AI knowledge, attitudes towards AI in healthcare, current state of AI education, and preferences for AI teaching. It consisted of 16 multiple-choice items, eight demographic queries, and one free-feld comment section. Medical, dental, and veterinary students from various countries were invited to participate via faculty newsletters and courses. The survey measured technological literacy, AI knowledge, current state of AI education, preferences for AI teaching, and attitudes towards AI in healthcare using Likert scales. Data were analyzed using descriptive statistics, Mann–Whitney U-test, Kruskal–Wallis test, and Dunn-Bonferroni post hoc test. Results The survey included 4313 medical, 205 dentistry, and 78 veterinary students from 192 faculties and 48 countries. Most participants were from Europe (51.1%), followed by North/South America (23.3%) and Asia (21.3%). Students reported positive attitudes towards AI in healthcare (median: 4, IQR: 3–4) and a desire for more AI teaching (median: 4, IQR: 4–5). However, they had limited AI knowledge (median: 2, IQR: 2–2), lack of AI courses (76.3%), and felt unprepared to use AI in their careers (median: 2, IQR: 1–3). Subgroup analyses revealed signifcant diferences between the Global North and South (r=0.025 to 0.185, all P<.001) and across continents (r=0.301 to 0.531, all P<.001), with generally small efect sizes. Conclusions This large-scale international survey highlights medical, dental, and veterinary students’ positive perceptions of AI in healthcare, their strong desire for AI education, and the current lack of AI teaching in medical curricula worldwide. The study identifes a need for integrating AI education into medical curricula, considering regional diferences in perceptions and educational needs. Trial registration Not applicable (no clinical trial)
- Multinational attitudes toward AI in Health Care and diagnostics among Hospital patients.Publication . Busch, Felix; Hoffmann, Lena; Xu, Lina; Zhang, Long Jiang; Hu, Bin; García-Juárez, Ignacio; Toapanta-Yanchapaxi, Liz; Gorelik, Natalia; Gorelik, Valérie; Rodriguez-Granillo, Gaston; Ferrarotti, Carlos; Cuong, Nguyen; Thi, Chau; Tuncel, Murat; Kaya, Gürsan; Solis-Barquero, Sergio; Mendez Avila, Maria; Ivanova, Nevena; Kitamura, Felipe; Hayama, Karina; Puntunet Bates, Monserrat; Torres, Pedro Iturralde; Ortiz-Prado, Esteban; Izquierdo-Condoy, Juan; Schwarz, Gilbert; Hofstaetter, Jochen; Hide, Michihiro; Takeda, Konagi; Peric, Barbara; Pilko, Gašper; Thulesius, Hans; Lindow, Thomas; Kolawole, Israel; Olatoke, Samuel Adegboyega; Grzybowski, Andrzej; Corlateanu, Alexandru; Iaconi, Oana-Simina; Li, Ting; Domitrz, Izabela; Kepczynska, Katarzyna; Mihalcin, Matúš; Fašaneková, Lenka; Zatonski, Tomasz; Fulek, Katarzyna; Molnár, András; Maihoub, Stefani; da Silva Gama, Zenewton; Saba, Luca; Sountoulides, Petros; Makowski, Marcus; Aerts, Hugo; Adams, Lisa; Bressem, Keno; 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; Billis, Antonis; Bitencourt, Almir; Bolejko, Anetta; Bollas Becerra, Antonio; Bwambale, Joe; Capela, Andreia; Cau, Riccardo; Chacon-Acevedo, Kelly; Chaunzwa, Tafadzwa; Chojniak, Rubens; Clements, Warren; Cuocolo, Renato; Dahlblom, Victor; Sousa, Kelienny de Meneses; Villarrubia, Jorge Esteban; Desai, Vijay; Dhakal, Ajaya; 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; Ishida, Fujimaro; Jha, Nisha; Jiang, Lili; Kader, Rawen; Kavnoudias, Helen; Klein, Clément; Kolostoumpis, George; Koshy, Abraham; Kruger, NicholaS; Löser, Alexander; Lucijanic, Marko; Mantziari, Despoina; Margue, Gaelle; McFadden, Sonyia; Miyake, Masahiro; Morakote, Wipawee; Ngabonziza, Issa; Nguyen, Thao; Niehues, Stefan; Nortje, Marc; Palaian, Subish; Pentara, Natalia; Poma, Gianluigi; Almeida, Rui; Purwoko, Mitayani; Pyrgidis, Nikolaos; Rafailidis, Vasileios; Rainey, Clare; Ribeiro, João; Agudelo, Nicolás Rozo; Sado, Keina; Saidman, Julia; Saturno-Hernandez, Pedro; Suryadevara, Vidyani; Schulz, Gerald; Soric, Ena; Soto-Pérez-Olivares, Javier; Stanzione, Arnaldo; Struck, Julian Peter; Takaoka, Hiroyuki; Tanioka, Satoru; Huyen, Tran; Truhn, Daniel; van Dijk, Elon; van Wijngaarden, Peter; Wang, Yuan-Cheng; Weidlich, Matthias; Zhang, ShuhangThe 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.