Fix formatting mistake in Busc24

diag.bib

@@ -5155,19 +5155,18 @@ @article{Busc23
 }
 
 @article{Busc24,
-  author = {Busch, Felix and Hoffmann, Lena and Truhn, Daniel and Ortiz-Prado, Esteban and Makowski, Marcus R. and Bressem, Keno K. and Adams, Lisa C. and COMFORT Consortium and Abdala, Nitamar and Navarro, \'{A},
-   title = {Global cross-sectional student survey on AI in medical, dental, and veterinary education and practice at 192 faculties},
-   doi = {10.1186/s12909-024-06035-4},
-   year = {2024},
-   abstract = {Abstract                 Background                 The successful integration of artificial 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 differences 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-field 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 significant differences 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 effect 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 identifies a need for integrating AI education into medical curricula, considering regional differences in perceptions and educational needs.                                Trial registration                 Not applicable (no clinical trial).},
-   url = {http://dx.doi.org/10.1186/s12909-024-06035-4},
-   file = {Busc24.pdf:pdf\\Busc24.pdf:PDF},
-   optnote = {DIAG, RADIOLOGY},
-   journal = {BMC Medical Education},
-   automatic = {yes},
-   all_ss_ids = {['c2d2d0b4b568df7336a0691ce89cd5c1a3585833']},
-   volume = {24},
- ,},
+  author = {Busch, Felix and Hoffmann, Lena and Truhn, Daniel and Ortiz-Prado, Esteban and Makowski, Marcus R. and Bressem, Keno K. and Adams, Lisa C. and COMFORT Consortium and Abdala, Nitamar and Navarro, A.},
+  title = {Global cross-sectional student survey on AI in medical, dental, and veterinary education and practice at 192 faculties},
+  doi = {10.1186/s12909-024-06035-4},
+  year = {2024},
+  abstract = {Abstract                 Background                 The successful integration of artificial 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 differences 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-field 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 significant differences 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 effect 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 identifies a need for integrating AI education into medical curricula, considering regional differences in perceptions and educational needs.                                Trial registration                 Not applicable (no clinical trial).},
+  url = {http://dx.doi.org/10.1186/s12909-024-06035-4},
+  file = {Busc24.pdf:pdf\\Busc24.pdf:PDF},
+  optnote = {DIAG, RADIOLOGY},
+  journal = {BMC Medical Education},
+  automatic = {yes},
+  all_ss_ids = {['c2d2d0b4b568df7336a0691ce89cd5c1a3585833']},
+  volume = {24},
   pmid = {39342231},
 }