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Laupichler MC, Aster A, Meyerheim M, Raupach T, Mergen M. Medical students' AI literacy and attitudes towards AI: a cross-sectional two-center study using pre-validated assessment instruments. BMC MEDICAL EDUCATION 2024; 24:401. [PMID: 38600457 PMCID: PMC11007897 DOI: 10.1186/s12909-024-05400-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 04/08/2024] [Indexed: 04/12/2024]
Abstract
BACKGROUND Artificial intelligence (AI) is becoming increasingly important in healthcare. It is therefore crucial that today's medical students have certain basic AI skills that enable them to use AI applications successfully. These basic skills are often referred to as "AI literacy". Previous research projects that aimed to investigate medical students' AI literacy and attitudes towards AI have not used reliable and validated assessment instruments. METHODS We used two validated self-assessment scales to measure AI literacy (31 Likert-type items) and attitudes towards AI (5 Likert-type items) at two German medical schools. The scales were distributed to the medical students through an online questionnaire. The final sample consisted of a total of 377 medical students. We conducted a confirmatory factor analysis and calculated the internal consistency of the scales to check whether the scales were sufficiently reliable to be used in our sample. In addition, we calculated t-tests to determine group differences and Pearson's and Kendall's correlation coefficients to examine associations between individual variables. RESULTS The model fit and internal consistency of the scales were satisfactory. Within the concept of AI literacy, we found that medical students at both medical schools rated their technical understanding of AI significantly lower (MMS1 = 2.85 and MMS2 = 2.50) than their ability to critically appraise (MMS1 = 4.99 and MMS2 = 4.83) or practically use AI (MMS1 = 4.52 and MMS2 = 4.32), which reveals a discrepancy of skills. In addition, female medical students rated their overall AI literacy significantly lower than male medical students, t(217.96) = -3.65, p <.001. Students in both samples seemed to be more accepting of AI than fearful of the technology, t(745.42) = 11.72, p <.001. Furthermore, we discovered a strong positive correlation between AI literacy and positive attitudes towards AI and a weak negative correlation between AI literacy and negative attitudes. Finally, we found that prior AI education and interest in AI is positively correlated with medical students' AI literacy. CONCLUSIONS Courses to increase the AI literacy of medical students should focus more on technical aspects. There also appears to be a correlation between AI literacy and attitudes towards AI, which should be considered when planning AI courses.
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Affiliation(s)
- Matthias Carl Laupichler
- Institute of Medical Education, University Hospital Bonn, Venusberg Campus 1, 53127, Bonn, Germany.
| | - Alexandra Aster
- Institute of Medical Education, University Hospital Bonn, Venusberg Campus 1, 53127, Bonn, Germany
| | - Marcel Meyerheim
- Department of Pediatric Oncology and Hematology, Faculty of Medicine, Saarland University, Homburg, Germany
| | - Tobias Raupach
- Institute of Medical Education, University Hospital Bonn, Venusberg Campus 1, 53127, Bonn, Germany
| | - Marvin Mergen
- Department of Pediatric Oncology and Hematology, Faculty of Medicine, Saarland University, Homburg, Germany
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Eminoğlu A, Çelikkanat Ş. Assessment of the relationship between executive Nurses' leadership Self-Efficacy and medical artificial intelligence readiness. Int J Med Inform 2024; 184:105386. [PMID: 38387197 DOI: 10.1016/j.ijmedinf.2024.105386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 01/22/2024] [Accepted: 02/19/2024] [Indexed: 02/24/2024]
Abstract
AIMS This study aims to assess the relationship between management nurses' leadership self-efficacy and medical artificial intelligence readiness. METHODS The research was conducted using a descriptive-correlational design. The sample of the study consisted of 196 management nurses working in public, private, and educational research hospitals in Gaziantep, Turkey. The data collection tools included the Personal Information Form, the Leadership Self-Efficacy Scale, and the Medical Artificial Intelligence Readiness Scale. RESULTS The majority of the participants in the research were female (71.4 %), married (80.1 %) and graduates of a bachelor's or higher degree in nursing (74.5 %), had 16 years or more of work experience in the profession (39.3 %), and worked during the day shift (75.5 %). Among the participating management nurses, those who were single had a significantly higher mean score in the cognition subscale and the total score of medical artificial intelligence readiness (p < 0.05). The management nurses working in shifts had significantly higher mean scores in the cognition and ability subscales, as well as the total score of medical artificial intelligence readiness (p < 0.05). The management nurses who received leadership/management-related training after their undergraduate education had a significantly higher mean score in the cognition subscale (p < 0.05). Furthermore, there was a significant relationship (p < 0.05) between leadership self-efficacy, medical artificial intelligence readiness, and their subscales, concerning following and finding artificial intelligence applications useful, as well as informing team members about artificial intelligence applications. CONCLUSIONS In the research, it was determined that the leadership self-efficacy of the manager nurses was at a good level and that their artificial intelligence readiness was at a medium level in terms of cognition, skill, foresight and ethics while presenting their professional knowledge. A positive and significant relationship was found between leadership self-efficacy and medical artificial intelligence readiness.
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Affiliation(s)
- Ayşe Eminoğlu
- Gaziantep Islam Science and Technology University of Health Sciences Department of Nursing, Gaziantep, Turkey.
| | - Şirin Çelikkanat
- Gaziantep Islam Science and Technology University of Health Sciences Department of Nursing, Gaziantep, Turkey.
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Laupichler MC, Tavakoli AA, Raupach T, Paech D. [Future skills-AI competencies for radiologists : Fostering AI knowledge and skills in undergraduate medical education]. RADIOLOGIE (HEIDELBERG, GERMANY) 2024; 64:316-320. [PMID: 37994912 DOI: 10.1007/s00117-023-01237-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 10/23/2023] [Indexed: 11/24/2023]
Affiliation(s)
- Matthias Carl Laupichler
- Institut für Medizindidaktik, Universitätsklinikum Bonn, Venusberg-Campus 1, 53127, Bonn, Deutschland.
| | | | - Tobias Raupach
- Institut für Medizindidaktik, Universitätsklinikum Bonn, Venusberg-Campus 1, 53127, Bonn, Deutschland
| | - Daniel Paech
- Klinik für Neuroradiologie, Universitätsklinikum Bonn, Bonn, Deutschland
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Gordon M, Daniel M, Ajiboye A, Uraiby H, Xu NY, Bartlett R, Hanson J, Haas M, Spadafore M, Grafton-Clarke C, Gasiea RY, Michie C, Corral J, Kwan B, Dolmans D, Thammasitboon S. A scoping review of artificial intelligence in medical education: BEME Guide No. 84. MEDICAL TEACHER 2024; 46:446-470. [PMID: 38423127 DOI: 10.1080/0142159x.2024.2314198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 01/31/2024] [Indexed: 03/02/2024]
Abstract
BACKGROUND Artificial Intelligence (AI) is rapidly transforming healthcare, and there is a critical need for a nuanced understanding of how AI is reshaping teaching, learning, and educational practice in medical education. This review aimed to map the literature regarding AI applications in medical education, core areas of findings, potential candidates for formal systematic review and gaps for future research. METHODS This rapid scoping review, conducted over 16 weeks, employed Arksey and O'Malley's framework and adhered to STORIES and BEME guidelines. A systematic and comprehensive search across PubMed/MEDLINE, EMBASE, and MedEdPublish was conducted without date or language restrictions. Publications included in the review spanned undergraduate, graduate, and continuing medical education, encompassing both original studies and perspective pieces. Data were charted by multiple author pairs and synthesized into various thematic maps and charts, ensuring a broad and detailed representation of the current landscape. RESULTS The review synthesized 278 publications, with a majority (68%) from North American and European regions. The studies covered diverse AI applications in medical education, such as AI for admissions, teaching, assessment, and clinical reasoning. The review highlighted AI's varied roles, from augmenting traditional educational methods to introducing innovative practices, and underscores the urgent need for ethical guidelines in AI's application in medical education. CONCLUSION The current literature has been charted. The findings underscore the need for ongoing research to explore uncharted areas and address potential risks associated with AI use in medical education. This work serves as a foundational resource for educators, policymakers, and researchers in navigating AI's evolving role in medical education. A framework to support future high utility reporting is proposed, the FACETS framework.
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Affiliation(s)
- Morris Gordon
- School of Medicine and Dentistry, University of Central Lancashire, Preston, UK
- Blackpool Hospitals NHS Foundation Trust, Blackpool, UK
| | - Michelle Daniel
- School of Medicine, University of California, San Diego, SanDiego, CA, USA
| | - Aderonke Ajiboye
- School of Medicine and Dentistry, University of Central Lancashire, Preston, UK
| | - Hussein Uraiby
- Department of Cellular Pathology, University Hospitals of Leicester NHS Trust, Leicester, UK
| | - Nicole Y Xu
- School of Medicine, University of California, San Diego, SanDiego, CA, USA
| | - Rangana Bartlett
- Department of Cognitive Science, University of California, San Diego, CA, USA
| | - Janice Hanson
- Department of Medicine and Office of Education, School of Medicine, Washington University in Saint Louis, Saint Louis, MO, USA
| | - Mary Haas
- Department of Emergency Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Maxwell Spadafore
- Department of Emergency Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | | | | | - Colin Michie
- School of Medicine and Dentistry, University of Central Lancashire, Preston, UK
| | - Janet Corral
- Department of Medicine, University of Nevada Reno, School of Medicine, Reno, NV, USA
| | - Brian Kwan
- School of Medicine, University of California, San Diego, SanDiego, CA, USA
| | - Diana Dolmans
- School of Health Professions Education, Faculty of Health, Maastricht University, Maastricht, NL, USA
| | - Satid Thammasitboon
- Center for Research, Innovation and Scholarship in Health Professions Education, Baylor College of Medicine, Houston, TX, USA
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Özbek Güven G, Yilmaz Ş, Inceoğlu F. Determining medical students' anxiety and readiness levels about artificial intelligence. Heliyon 2024; 10:e25894. [PMID: 38384508 PMCID: PMC10878911 DOI: 10.1016/j.heliyon.2024.e25894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 01/23/2024] [Accepted: 02/05/2024] [Indexed: 02/23/2024] Open
Abstract
The aim of this study is to determine the levels of anxiety and readiness among medical students regarding artificial intelligence (AI) and examine the relationship between these factors. The research was conducted on medical students, and the data was collected through face-to-face and online surveys between April and June 2022. The study utilized a socio-demographic information form, an AI anxiety scale, and a medical AI readiness scale. The data collected from a total of 542 students were analyzed using the Statistical Program for Social Sciences (SPSS) version 25. Cronbach's α coefficient was used for reliability analysis. A path diagram was created using AMOS 24, and structural equation modelling (SEM) analysis was applied. The findings of the study indicate that medical students have a moderate level of readiness and a high level of anxiety regarding AI. Furthermore, an inverse relationship was found between AI readiness and AI anxiety. These results highlight the importance of increasing the preparedness of medical students for AI applications and reducing their anxieties. The study suggests the inclusion of AI in the medical curriculum and the development of a standardized curriculum to facilitate its teaching.
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Affiliation(s)
- Gamze Özbek Güven
- Department of Medical History and Ethics, School of Medicine, Yuksek Ihtisas University, Ankara, Türkiye
| | - Şerife Yilmaz
- Department of Medical History and Ethics, School of Medicine, Harran University, Şanlıurfa, Türkiye
| | - Feyza Inceoğlu
- Department of Biostatistics, School of Medicine, Malatya Turgut Ozal University, Malatya, Türkiye
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Weidener L, Fischer M. Proposing a Principle-Based Approach for Teaching AI Ethics in Medical Education. JMIR MEDICAL EDUCATION 2024; 10:e55368. [PMID: 38285931 PMCID: PMC10891487 DOI: 10.2196/55368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 01/02/2024] [Accepted: 01/29/2024] [Indexed: 01/31/2024]
Abstract
The use of artificial intelligence (AI) in medicine, potentially leading to substantial advancements such as improved diagnostics, has been of increasing scientific and societal interest in recent years. However, the use of AI raises new ethical challenges, such as an increased risk of bias and potential discrimination against patients, as well as misdiagnoses potentially leading to over- or underdiagnosis with substantial consequences for patients. Recognizing these challenges, current research underscores the importance of integrating AI ethics into medical education. This viewpoint paper aims to introduce a comprehensive set of ethical principles for teaching AI ethics in medical education. This dynamic and principle-based approach is designed to be adaptive and comprehensive, addressing not only the current but also emerging ethical challenges associated with the use of AI in medicine. This study conducts a theoretical analysis of the current academic discourse on AI ethics in medical education, identifying potential gaps and limitations. The inherent interconnectivity and interdisciplinary nature of these anticipated challenges are illustrated through a focused discussion on "informed consent" in the context of AI in medicine and medical education. This paper proposes a principle-based approach to AI ethics education, building on the 4 principles of medical ethics-autonomy, beneficence, nonmaleficence, and justice-and extending them by integrating 3 public health ethics principles-efficiency, common good orientation, and proportionality. The principle-based approach to teaching AI ethics in medical education proposed in this study offers a foundational framework for addressing the anticipated ethical challenges of using AI in medicine, recommended in the current academic discourse. By incorporating the 3 principles of public health ethics, this principle-based approach ensures that medical ethics education remains relevant and responsive to the dynamic landscape of AI integration in medicine. As the advancement of AI technologies in medicine is expected to increase, medical ethics education must adapt and evolve accordingly. The proposed principle-based approach for teaching AI ethics in medical education provides an important foundation to ensure that future medical professionals are not only aware of the ethical dimensions of AI in medicine but also equipped to make informed ethical decisions in their practice. Future research is required to develop problem-based and competency-oriented learning objectives and educational content for the proposed principle-based approach to teaching AI ethics in medical education.
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Affiliation(s)
- Lukas Weidener
- UMIT TIROL - Private University for Health Sciences and Health Technology, Hall in Tirol, Austria
| | - Michael Fischer
- UMIT TIROL - Private University for Health Sciences and Health Technology, Hall in Tirol, Austria
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Daher OA, Dabbousi AA, Chamroukh R, Saab AY, Al Ayoubi AR, Salameh P. Artificial Intelligence: Knowledge and Attitude Among Lebanese Medical Students. Cureus 2024; 16:e51466. [PMID: 38298326 PMCID: PMC10829838 DOI: 10.7759/cureus.51466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/01/2024] [Indexed: 02/02/2024] Open
Abstract
Background Artificial intelligence (AI) has taken on a variety of functions in the medical field, and research has proven that it can address complicated issues in various applications. It is unknown whether Lebanese medical students and residents have a detailed understanding of this concept, and little is known about their attitudes toward AI. Aim This study fills a critical gap by revealing the knowledge and attitude of Lebanese medical students toward AI. Methods A multi-centric survey targeting 365 medical students from seven medical schools across Lebanon was conducted to assess their knowledge of and attitudes toward AI in medicine. The survey consists of five sections: the first part includes socio-demographic variables, while the second comprises the 'Medical Artificial Intelligence Readiness Scale' for medical students. The third part focuses on attitudes toward AI in medicine, the fourth assesses understanding of deep learning, and the fifth targets considerations of radiology as a specialization. Results There is a notable awareness of AI among students who are eager to learn about it. Despite this interest, there exists a gap in knowledge regarding deep learning, albeit alongside a positive attitude towards it. Students who are more open to embracing AI technology tend to have a better understanding of AI concepts (p=0.001). Additionally, a higher percentage of students from Mount Lebanon (71.6%) showed an inclination towards using AI compared to Beirut (63.2%) (p=0.03). Noteworthy are the Lebanese University and Saint Joseph University, where the highest proportions of students are willing to integrate AI into the medical field (79.4% and 76.7%, respectively; p=0.001). Conclusion It was concluded that most Lebanese medical students might not necessarily comprehend the core technological ideas of AI and deep learning. This lack of understanding was evident from the substantial amount of misinformation among the students. Consequently, there appears to be a significant demand for the inclusion of AI technologies in Lebanese medical school courses.
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Affiliation(s)
- Omar A Daher
- Faculty of Medicine, Beirut Arab University, Beirut, LBN
| | | | | | | | - Amir Rabih Al Ayoubi
- Department of General Medicine, Faculty of Medical Sciences, Lebanese University, Beirut, LBN
| | - Pascale Salameh
- Department of Primary Care and Population Health, University of Nicosia Medical School, Nicosia, CYP
- Department of Public Health, Institut National de Santé Publique, d'Épidémiologie Clinique et de Toxicologie (INSPECT-LB), Beirut, LBN
- Department of Pharmacy Practice, Lebanese University, Beirut, LBN
- School of Medicine, Lebanese American University, Beirut, LBN
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Truong NM, Vo TQ, Tran HTB, Nguyen HT, Pham VNH. Healthcare students' knowledge, attitudes, and perspectives toward artificial intelligence in the southern Vietnam. Heliyon 2023; 9:e22653. [PMID: 38107295 PMCID: PMC10724669 DOI: 10.1016/j.heliyon.2023.e22653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 11/15/2023] [Accepted: 11/16/2023] [Indexed: 12/19/2023] Open
Abstract
The application of new technologies in medical education still lags behind the extraordinary advances of AI. This study examined the understanding, attitudes, and perspectives of Vietnamese medical students toward AI and its consequences, as well as their knowledge of existing AI operations in Vietnam. A cross-sectional online survey was administered to 1142 students enrolled in undergraduate medicine and pharmacy programs. Most of the participants had no understanding of AI in healthcare (1053 or 92.2 %). The majority believed that AI would benefit their careers (890 or 77.9 %) and that such innovation will be used to oversee public health and epidemic prevention on their behalf (882 or 77.2 %). The proportion of students with satisfactory knowledge significantly differed depending on gender (P < 0.001), major (P = 0.003), experience (P < 0.001), and income (P = 0.011). The percentage of respondents with positive attitudes significantly differed by year level (P = 0.008) and income (P = 0.003), and the proportion with favorable perspectives regarding AI varied considerably by age (P = 0.046) and major (P < 0.001). Most of the participants wanted to integrate AI into radiology and digital imaging training (P = 0.283), while the fifth-year students wished to learn about AI in medical genetics and genomics (P < 0.001, 4.0 ± 0.8). The male students had 1.898 times more adequate knowledge of AI than their female counterparts, and those who had attended webinars/lectures/courses on AI in healthcare had 4.864 times more adequate knowledge than those having no such experiences. The majority believed that the barrier to implementing AI in healthcare is the lack of financial resources (83.54 %) and appropriate training (81.00 %). Participants saw AI as a "partner" rather than a "competitor", but the majority of low knowledge was recorded. Future research should take into account the way to integrate AI into medical training programs for healthcare students.
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Affiliation(s)
- Nguyen Minh Truong
- Faculty of Pharmacy, Pham Ngoc Thach University of Medicine, Ho Chi Minh City, 700000, Viet Nam
| | - Trung Quang Vo
- Faculty of Pharmacy, Pham Ngoc Thach University of Medicine, Ho Chi Minh City, 700000, Viet Nam
| | - Hien Thi Bich Tran
- Faculty of Pharmacy, Pham Ngoc Thach University of Medicine, Ho Chi Minh City, 700000, Viet Nam
| | - Hiep Thanh Nguyen
- Faculty of Medicine, Pham Ngoc Thach University of Medicine, Ho Chi Minh City, 700000, Viet Nam
| | - Van Nu Hanh Pham
- Faculty of Pharmaceutical Management and Economic, Hanoi University of Pharmacy, Hanoi, 100000, Viet Nam
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Pupic N, Ghaffari-Zadeh A, Hu R, Singla R, Darras K, Karwowska A, Forster BB. An evidence-based approach to artificial intelligence education for medical students: A systematic review. PLOS DIGITAL HEALTH 2023; 2:e0000255. [PMID: 38011214 PMCID: PMC10681314 DOI: 10.1371/journal.pdig.0000255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 09/14/2023] [Indexed: 11/29/2023]
Abstract
The exponential growth of artificial intelligence (AI) in the last two decades has been recognized by many as an opportunity to improve the quality of patient care. However, medical education systems have been slow to adapt to the age of AI, resulting in a paucity of AI-specific education in medical schools. The purpose of this systematic review is to evaluate the current evidence-based recommendations for the inclusion of an AI education curriculum in undergraduate medicine. Six databases were searched from inception to April 23, 2022 for cross sectional and cohort studies of fair quality or higher on the Newcastle-Ottawa scale, systematic, scoping, and integrative reviews, randomized controlled trials, and Delphi studies about AI education in undergraduate medical programs. The search yielded 991 results, of which 27 met all the criteria and seven more were included using reference mining. Despite the limitations of a high degree of heterogeneity among the study types and a lack of follow-up studies evaluating the impacts of current AI strategies, a thematic analysis of the key AI principles identified six themes needed for a successful implementation of AI in medical school curricula. These themes include ethics, theory and application, communication, collaboration, quality improvement, and perception and attitude. The themes of ethics, theory and application, and communication were further divided into subthemes, including patient-centric and data-centric ethics; knowledge for practice and knowledge for communication; and communication for clinical decision-making, communication for implementation, and communication for knowledge dissemination. Based on the survey studies, medical professionals and students, who generally have a low baseline knowledge of AI, have been strong supporters of adding formal AI education into medical curricula, suggesting more research needs to be done to push this agenda forward.
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Affiliation(s)
- Nikola Pupic
- Faculty of Medicine, University of British Columbia, British Columbia, Vancouver, Canada
| | - Aryan Ghaffari-Zadeh
- Faculty of Medicine, University of British Columbia, British Columbia, Vancouver, Canada
| | - Ricky Hu
- Faculty of Medicine, Queen's University, Ontario, Kingston, Canada
| | - Rohit Singla
- Faculty of Medicine, University of British Columbia, British Columbia, Vancouver, Canada
| | - Kathryn Darras
- Faculty of Medicine, Department of Radiology, University of British Columbia, British Columbia, Vancouver, Canada
| | - Anna Karwowska
- Association of Faculties of Medicine of Canada, Ontario, Ottawa, Canada
- Faculty of Medicine, Department of Pediatrics, University of Ottawa, Ontario, Ottawa, Canada
| | - Bruce B Forster
- Faculty of Medicine, Department of Radiology, University of British Columbia, British Columbia, Vancouver, Canada
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Taskiran N. Effect of Artificial Intelligence Course in Nursing on Students' Medical Artificial Intelligence Readiness: A Comparative Quasi-Experimental Study. Nurse Educ 2023; 48:E147-E152. [PMID: 37133231 DOI: 10.1097/nne.0000000000001446] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
BACKGROUND It is predicted that artificial intelligence (AI) will transform nursing across all domains of nursing practice, including administration, clinical care, education, policy, and research. PURPOSE This study examined the impact of an AI course in the nursing curriculum on students' medical AI readiness. DESIGN AND METHODS This comparative quasi-experimental study was conducted with a total of 300 3rd-year nursing students, 129 in the control group and 171 in the experimental group. Students in the experimental group received 28 hours of AI training. The students in the control group were not given any training. Data were collected by a socio-demographic form and the Medical Artificial Intelligence Readiness Scale. RESULTS An AI course should be included in the nursing curriculum, according to 67.8% of students in the experimental group and 57.4% of students in the control group. The mean score of the experimental group on medical AI readiness was higher ( P < .05) and the effect size of the course on readiness was -0.29. CONCLUSIONS An AI nursing course positively affects students' readiness for medical AI.
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Affiliation(s)
- Nihal Taskiran
- Assistant Professor, Department of Fundamentals of Nursing, Faculty of Nursing, Aydın Adnan Menderes University, Aydın, Turkey
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11
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Moodi Ghalibaf A, Moghadasin M, Emadzadeh A, Mastour H. Psychometric properties of the persian version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS). BMC MEDICAL EDUCATION 2023; 23:577. [PMID: 37582816 PMCID: PMC10428571 DOI: 10.1186/s12909-023-04553-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 07/30/2023] [Indexed: 08/17/2023]
Abstract
INTRODUCTION There are numerous cases where artificial intelligence (AI) can be applied to improve the outcomes of medical education. The extent to which medical practitioners and students are ready to work and leverage this paradigm is unclear in Iran. This study investigated the psychometric properties of a Persian version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) developed by Karaca, et al. in 2021. In future studies, the medical AI readiness for Iranian medical students could be investigated using this scale, and effective interventions might be planned and implemented according to the results. METHODS In this study, 502 medical students (mean age 22.66(± 2.767); 55% female) responded to the Persian questionnaire in an online survey. The original questionnaire was translated into Persian using a back translation procedure, and all participants completed the demographic component and the entire MAIRS-MS. Internal and external consistencies, factor analysis, construct validity, and confirmatory factor analysis were examined to analyze the collected data. A P ≤ 0.05 was considered as the level of statistical significance. RESULTS Four subscales emerged from the exploratory factor analysis (Cognition, Ability, Vision, and Ethics), and confirmatory factor analysis confirmed the four subscales. The Cronbach alpha value for internal consistency was 0.944 for the total scale and 0.886, 0.905, 0.865, and 0.856 for cognition, ability, vision, and ethics, respectively. CONCLUSIONS The Persian version of MAIRS-MS was fairly equivalent to the original one regarding the conceptual and linguistic aspects. This study also confirmed the validity and reliability of the Persian version of MAIRS-MS. Therefore, the Persian version can be a suitable and brief instrument to assess Iranian Medical Students' readiness for medical artificial intelligence.
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Affiliation(s)
- AmirAli Moodi Ghalibaf
- Student Research Committee, Faculty of Medicine, Birjand University of Medical Sciences, Birjand, Iran
| | - Maryam Moghadasin
- Department of Clinical Psychology, Faculty of Psychology and Education, Kharazmi University, Tehran, Iran
| | - Ali Emadzadeh
- Department of Medical Education, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Haniye Mastour
- Department of Medical Education, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
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Rezazadeh H, Ahmadipour H, Salajegheh M. Psychometric evaluation of Persian version of medical artificial intelligence readiness scale for medical students. BMC MEDICAL EDUCATION 2023; 23:527. [PMID: 37488522 PMCID: PMC10367280 DOI: 10.1186/s12909-023-04516-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Accepted: 07/18/2023] [Indexed: 07/26/2023]
Abstract
BACKGROUND Artificial intelligence's advancement in medicine and its worldwide implementation will be one of the main elements of medical education in the coming years. This study aimed to translate and psychometric evaluation of the Persian version of the medical artificial intelligence readiness scale for medical students. METHODS The questionnaire was translated according to a backward-forward translation procedure. Reliability was assessed by calculating Cronbach's alpha coefficient. Confirmatory Factor Analysis was conducted on 302 medical students. Content validity was evaluated using the Content Validity Index and Content Validity Ratio. RESULTS The Cronbach's alpha coefficient for the whole scale was found to be 0.94. The Content Validity Index was 0.92 and the Content Validity Ratio was 0.75. Confirmatory factor analysis revealed a fair fit for four factors: cognition, ability, vision, and ethics. CONCLUSION The Persian version of the medical artificial intelligence readiness scale for medical students consisting of four factors including cognition, ability, vision, and ethics appears to be an almost valid and reliable instrument for the evaluation of medical artificial intelligence readiness.
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Affiliation(s)
- Hossein Rezazadeh
- Student Committee of Medical Education Development, Education Development Center, Kerman University of Medical Sciences, Kerman, Iran
| | - Habibeh Ahmadipour
- Community Medicine Department, School of Medicine, Medical Education Leadership and Management Research Center, Kerman University of Medical Sciences, Kerman, Iran
| | - Mahla Salajegheh
- Department of Medical Education, Medical Education Development Center, Kerman University of Medical Sciences, Kerman, Iran.
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Kleine AK, Kokje E, Lermer E, Gaube S. Attitudes Toward the Adoption of 2 Artificial Intelligence-Enabled Mental Health Tools Among Prospective Psychotherapists: Cross-sectional Study. JMIR Hum Factors 2023; 10:e46859. [PMID: 37436801 PMCID: PMC10372564 DOI: 10.2196/46859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 05/08/2023] [Accepted: 05/14/2023] [Indexed: 07/13/2023] Open
Abstract
BACKGROUND Despite growing efforts to develop user-friendly artificial intelligence (AI) applications for clinical care, their adoption remains limited because of the barriers at individual, organizational, and system levels. There is limited research on the intention to use AI systems in mental health care. OBJECTIVE This study aimed to address this gap by examining the predictors of psychology students' and early practitioners' intention to use 2 specific AI-enabled mental health tools based on the Unified Theory of Acceptance and Use of Technology. METHODS This cross-sectional study included 206 psychology students and psychotherapists in training to examine the predictors of their intention to use 2 AI-enabled mental health care tools. The first tool provides feedback to the psychotherapist on their adherence to motivational interviewing techniques. The second tool uses patient voice samples to derive mood scores that the therapists may use for treatment decisions. Participants were presented with graphic depictions of the tools' functioning mechanisms before measuring the variables of the extended Unified Theory of Acceptance and Use of Technology. In total, 2 structural equation models (1 for each tool) were specified, which included direct and mediated paths for predicting tool use intentions. RESULTS Perceived usefulness and social influence had a positive effect on the intention to use the feedback tool (P<.001) and the treatment recommendation tool (perceived usefulness, P=.01 and social influence, P<.001). However, trust was unrelated to use intentions for both the tools. Moreover, perceived ease of use was unrelated (feedback tool) and even negatively related (treatment recommendation tool) to use intentions when considering all predictors (P=.004). In addition, a positive relationship between cognitive technology readiness (P=.02) and the intention to use the feedback tool and a negative relationship between AI anxiety and the intention to use the feedback tool (P=.001) and the treatment recommendation tool (P<.001) were observed. CONCLUSIONS The results shed light on the general and tool-dependent drivers of AI technology adoption in mental health care. Future research may explore the technological and user group characteristics that influence the adoption of AI-enabled tools in mental health care.
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Affiliation(s)
- Anne-Kathrin Kleine
- Department of Psychology, Ludwig Maximilian University of Munich, Munich, Germany
| | - Eesha Kokje
- Department of Psychology, Ludwig Maximilian University of Munich, Munich, Germany
| | - Eva Lermer
- Department of Psychology, Ludwig Maximilian University of Munich, Munich, Germany
- Technical University of Applied Sciences Augsburg, Augsburg, Germany
| | - Susanne Gaube
- Department of Psychology, Ludwig Maximilian University of Munich, Munich, Germany
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Buabbas AJ, Miskin B, Alnaqi AA, Ayed AK, Shehab AA, Syed-Abdul S, Uddin M. Investigating Students' Perceptions towards Artificial Intelligence in Medical Education. Healthcare (Basel) 2023; 11:healthcare11091298. [PMID: 37174840 PMCID: PMC10178742 DOI: 10.3390/healthcare11091298] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 04/26/2023] [Accepted: 04/29/2023] [Indexed: 05/15/2023] Open
Abstract
Implementing a reform in medical education requires students' awareness regarding the importance of artificial intelligence (AI) in modern medical practice. The objective of this study was to investigate students' perceptions of AI in medical education. A cross-sectional survey was conducted from June 2021 to November 2021 using an online questionnaire to collect data from medical students in the Faculty of Medicine at Kuwait University, Kuwait. The response rate for the survey was 51%, with a sample size of 352. Most students (349 (99.1%)) agreed that AI would play an important role in healthcare. More than half of the students (213 (60.5%)) understood the basic principles of AI, and (329 (93.4%)) students showed comfort with AI terminology. Many students (329 (83.5%)) believed that learning about AI would benefit their careers, and (289 (82.1%)) believed that medical students should receive AI teaching or training. The study revealed that most students had positive perceptions of AI. Undoubtedly, the role of AI in the future of medicine will be significant, and AI-based medical practice is required. There was a strong consensus that AI will not replace doctors but will drastically transform healthcare practices.
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Affiliation(s)
- Ali Jasem Buabbas
- Department of Community Medicine and Behavioral Sciences, Faculty of Medicine, Kuwait University, Jabriya 046300, Kuwait
| | - Brouj Miskin
- Ministry of Health, Jamal Abdel Nasser Street, Sulaibkhat, Kuwait City 13001, Kuwait
| | - Amar Ali Alnaqi
- Department of Surgery, Faculty of Medicine, Kuwait University, Jabriya 046300, Kuwait
| | - Adel K Ayed
- Department of Surgery, Faculty of Medicine, Kuwait University, Jabriya 046300, Kuwait
| | - Abrar Abdulmohsen Shehab
- Department of Immunology, Mubarak Alkabeer Hospital, Hawalli Health Region, Ministry of Health, Jabriya 047060, Kuwait
| | - Shabbir Syed-Abdul
- Graduate Institute of Bioinformatics, School of Gerontology and Long-Term Care, Taipei Medical University, Taipei 100-116, Taiwan
- International Center for Health Information Technology, Taipei Medical University, Taipei 100-116, Taiwan
| | - Mohy Uddin
- Research Quality Management Section, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences, Ministry of National Guard-Health Affairs, Riyadh 11481, Saudi Arabia
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15
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Zainal H, Tan JK, Xiaohui X, Thumboo J, Yong FK. Clinical informatics training in medical school education curricula: a scoping review. J Am Med Inform Assoc 2023; 30:604-616. [PMID: 36545751 PMCID: PMC9933074 DOI: 10.1093/jamia/ocac245] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 11/22/2022] [Accepted: 12/05/2022] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVES This scoping review evaluates the existing literature on clinical informatics (CI) training in medical schools. It aims to determine the essential components of a CI curriculum in medical schools, identify methods to evaluate the effectiveness of a CI-focused education, and understand its delivery modes. MATERIALS AND METHODS This review was informed by the methodological guidance of the Joanna Briggs Institute. Three electronic databases including PubMed, Scopus, and Web of Science were searched for articles discussing CI between January 2010 and December 2021. RESULTS Fifty-nine out of 3055 articles were included in our final analysis. Components of CI education include its utilization in clinical practice, ethical implications, key CI-related concepts, and digital health. Evaluation of educational effectiveness entails external evaluation by organizations external to the teaching institute, and internal evaluation from within the teaching institute. Finally, modes of delivery include various pedagogical strategies and teaching CI using a multidisciplinary approach. DISCUSSION Given the broad discussion on the required competencies, we propose 4 recommendations in CI delivery. These include situating CI curriculum within specific contexts, developing evidence-based guidelines for a robust CI education, developing validated assessment techniques to evaluate curriculum effectiveness, and equipping educators with relevant CI training. CONCLUSION The literature reveals that CI training in the core curricula will complement if not enhance clinical skills, reiterating the need to equip students with relevant CI competencies. Furthermore, future research needs to comprehensively address current gaps in CI training in different contexts, evaluation methodologies, and delivery modes to facilitate structured training.
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Affiliation(s)
- Humairah Zainal
- Health Services Research Unit, Singapore General Hospital, Singapore, Singapore
| | - Joshua Kuan Tan
- Health Services Research Unit, Singapore General Hospital, Singapore, Singapore
| | - Xin Xiaohui
- Health Services Research Unit, Singapore General Hospital, Singapore, Singapore
| | - Julian Thumboo
- Health Services Research Unit, Singapore General Hospital, Singapore, Singapore
- Department of Rheumatology and Immunology, Singapore General Hospital, Singapore, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Fong Kok Yong
- Department of Rheumatology and Immunology, Singapore General Hospital, Singapore, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
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AlZaabi A, AlMaskari S, AalAbdulsalam A. Are physicians and medical students ready for artificial intelligence applications in healthcare? Digit Health 2023; 9:20552076231152167. [PMID: 36762024 PMCID: PMC9903019 DOI: 10.1177/20552076231152167] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 01/03/2023] [Indexed: 01/28/2023] Open
Abstract
Background Artificial intelligence (AI) Healthcare applications are listed in the national visions of some Gulf Cooperation Council countries. A successful use of AI depends on the attitude and perception of medical experts of its applications. Objective To evaluate physicians and medical students' attitude and perception on AI applications in healthcare. Method A web-based survey was disseminated by email to physicians and medical students. Results A total of 293 (82 physicians and 211 medical students) individuals have participated (response rate is 27%). Seven participants (9%) reported knowing nothing about AI, while 208 (69%) were aware that it is an emerging field and would like to learn about it. Concerns about AI impact on physicians' employability were not prominent. Instead, the majority (n=159) agreed that new positions will be created and the job market for those who embrace AI will increase. They reported willingness to adapt AI in practice if it was incorporated in international guidelines (30.5%), published in respected scientific journals (17.1%), or included in formal training (12.2%). Almost two of the three participants agreed that dedicated courses will help them to implement AI. The most commonly reported problem of AI is its inability to provide opinions in unexpected scenarios. Half of the participants think that both the manufacturer and physicians should be legally liable for medical errors occur due to AI-based decision support tools while more than one-third (36.77%) think that physicians who make the final decision should be legally liable. Senior physicians were found to be less familiar with AI and more concerned about physicians' legal liability in case of a medical error. Conclusion Physicians and medical students showed positive attitudes and willingness to learn about AI applications in healthcare. Introducing AI learning objectives or short courses in medical curriculum would help to equip physicians with the needed skills for AI-augmented healthcare system.
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Affiliation(s)
- Adhari AlZaabi
- Human and Clinical Anatomy Department, College of Medicine and Health Science, Muscat, Sultanate of Oman,Adhari AlZaabi, Human and Clinical Anatomy Department, College of Medicine and Health Science, Alkhodh, P.O 123, Muscat, Sultanate of Oman.
Abdulrahman AalAbdulsalam, College of Science, Sultan Qaboos University, Muscat, Sultanate of Oman.
| | - Saleh AlMaskari
- Human and Clinical Anatomy Department, College of Medicine and Health Science, Muscat, Sultanate of Oman
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Tung AYZ, Dong LW. Malaysian Medical Students' Attitudes and Readiness Toward AI (Artificial Intelligence): A Cross-Sectional Study. JOURNAL OF MEDICAL EDUCATION AND CURRICULAR DEVELOPMENT 2023; 10:23821205231201164. [PMID: 37719325 PMCID: PMC10501060 DOI: 10.1177/23821205231201164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 06/12/2023] [Indexed: 09/19/2023]
Abstract
OBJECTIVES The Malaysian health ministry has started introducing artificial intelligence (AI) technology to aid local healthcare delivery. This study aims to survey Malaysian medical students' attitudes toward AI and evaluate their readiness to work with medical AI technology. METHODS An online questionnaire on Google Forms was distributed to all 31 medical schools in Malaysia. The questionnaire consists of 3 sections: the first part surveyed the participants' demographics, the second assessed the participants' attitudes toward AI, and the final part utilizes the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) scale to evaluate their AI readiness. RESULTS Three hundred and one students from 17 universities in Malaysia responded to the questionnaire. 87.36% of students agreed that AI will play an essential role in healthcare; 32.55% of students were less likely to consider a career in radiology due to the advancement of AI. The majority of students (71%) felt that teaching in AI will benefit their careers, while 69.44% agreed that all students should receive teaching in AI. Around 44.5% of students felt that they will possess the knowledge required to work with AI upon graduation. On the MAIRS-MS scale, students had a mean score of 21 of 40 for the cognitive factor, 25 of 40 for the ability factor, 10 of 15 for the vision factor, and 11 of 15 for the ethics factor. Overall, Malaysian students had a mean total score of 67±14.8 out of 110. CONCLUSION Malaysian medical students have demonstrated awareness of AI and a willingness to learn more about it. More work needs to be done to improve students' AI readiness, particularly their knowledge and application of AI technology. Malaysian universities should start to work on incorporating AI teaching into their curricula.
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Affiliation(s)
- Alvin Yong Zong Tung
- Faculty of Medical Sciences, Newcastle University Medicine Malaysia, Johor Bahru, Malaysia
- Wrexham Maelor Hospital, Wrexham, UK
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18
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Laupichler MC, Hadizadeh DR, Wintergerst MWM, von der Emde L, Paech D, Dick EA, Raupach T. Effect of a flipped classroom course to foster medical students' AI literacy with a focus on medical imaging: a single group pre-and post-test study. BMC MEDICAL EDUCATION 2022; 22:803. [PMID: 36397110 PMCID: PMC9672614 DOI: 10.1186/s12909-022-03866-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 11/04/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND The use of artificial intelligence applications in medicine is becoming increasingly common. At the same time, however, there are few initiatives to teach this important and timely topic to medical students. One reason for this is the predetermined medical curriculum, which leaves very little room for new topics that were not included before. We present a flipped classroom course designed to give undergraduate medical students an elaborated first impression of AI and to increase their "AI readiness". METHODS The course was tested and evaluated at Bonn Medical School in Germany with medical students in semester three or higher and consisted of a mixture of online self-study units and online classroom lessons. While the online content provided the theoretical underpinnings and demonstrated different perspectives on AI in medical imaging, the classroom sessions offered deeper insight into how "human" diagnostic decision-making differs from AI diagnoses. This was achieved through interactive exercises in which students first diagnosed medical image data themselves and then compared their results with the AI diagnoses. We adapted the "Medical Artificial Intelligence Scale for Medical Students" to evaluate differences in "AI readiness" before and after taking part in the course. These differences were measured by calculating the so called "comparative self-assessment gain" (CSA gain) which enables a valid and reliable representation of changes in behaviour, attitudes, or knowledge. RESULTS We found a statistically significant increase in perceived AI readiness. While values of CSA gain were different across items and factors, the overall CSA gain regarding AI readiness was satisfactory. CONCLUSION Attending a course developed to increase knowledge about AI in medical imaging can increase self-perceived AI readiness in medical students.
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Affiliation(s)
- Matthias C Laupichler
- Institute of Medical Education, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany.
| | - Dariusch R Hadizadeh
- Clinic for Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
| | | | - Leon von der Emde
- Department of Ophthalmology, University Hospital Bonn, Bonn, Germany
| | - Daniel Paech
- Clinic for Neuroradiology, University Hospital Bonn, Bonn, Germany
| | - Elizabeth A Dick
- Imperial College NHS Trust and Imperial College London, St. Marys Hospital London, London, UK
| | - Tobias Raupach
- Institute of Medical Education, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany
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19
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Civaner MM, Uncu Y, Bulut F, Chalil EG, Tatli A. Artificial intelligence in medical education: a cross-sectional needs assessment. BMC MEDICAL EDUCATION 2022; 22:772. [PMID: 36352431 PMCID: PMC9646274 DOI: 10.1186/s12909-022-03852-3] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 11/01/2022] [Indexed: 05/09/2023]
Abstract
BACKGROUND As the information age wanes, enabling the prevalence of the artificial intelligence age; expectations, responsibilities, and job definitions need to be redefined for those who provide services in healthcare. This study examined the perceptions of future physicians on the possible influences of artificial intelligence on medicine, and to determine the needs that might be helpful for curriculum restructuring. METHODS A cross-sectional multi-centre study was conducted among medical students country-wide, where 3018 medical students participated. The instrument of the study was an online survey that was designed and distributed via a web-based service. RESULTS Most of the medical students perceived artificial intelligence as an assistive technology that could facilitate physicians' access to information (85.8%) and patients to healthcare (76.7%), and reduce errors (70.5%). However, half of the participants were worried about the possible reduction in the services of physicians, which could lead to unemployment (44.9%). Furthermore, it was agreed that using artificial intelligence in medicine could devalue the medical profession (58.6%), damage trust (45.5%), and negatively affect patient-physician relationships (42.7%). Moreover, nearly half of the participants affirmed that they could protect their professional confidentiality when using artificial intelligence applications (44.7%); whereas, 16.1% argued that artificial intelligence in medicine might cause violations of professional confidentiality. Of all the participants, only 6.0% stated that they were competent enough to inform patients about the features and risks of artificial intelligence. They further expressed that their educational gaps regarding their need for "knowledge and skills related to artificial intelligence applications" (96.2%), "applications for reducing medical errors" (95.8%), and "training to prevent and solve ethical problems that might arise as a result of using artificial intelligence applications" (93.8%). CONCLUSIONS The participants expressed a need for an update on the medical curriculum, according to necessities in transforming healthcare driven by artificial intelligence. The update should revolve around equipping future physicians with the knowledge and skills to effectively use artificial intelligence applications and ensure that professional values and rights are protected.
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Affiliation(s)
- M Murat Civaner
- Department of Medical Ethics, Bursa Uludag University School of Medicine, Bursa, Turkey.
| | - Yeşim Uncu
- Department of Family Medicine, Bursa Uludag University School of Medicine, Bursa, Turkey
| | - Filiz Bulut
- Institute of Health Sciences, Bursa Uludag University, Bursa, Turkey
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20
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Understanding Medical Students’ Perceptions of and Behavioral Intentions toward Learning Artificial Intelligence: A Survey Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19148733. [PMID: 35886587 PMCID: PMC9315694 DOI: 10.3390/ijerph19148733] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 07/11/2022] [Accepted: 07/14/2022] [Indexed: 01/27/2023]
Abstract
Medical students learning to use artificial intelligence for medical practices is likely to enhance medical services. However, studies in this area have been lacking. The present study investigated medical students’ perceptions of and behavioral intentions toward learning artificial intelligence (AI) in clinical practice based on the theory of planned behavior (TPB). A sum of 274 Year-5 undergraduates and master’s and doctoral postgraduates participated in the online survey. Six constructs were measured, including (1) personal relevance (PR) of medical AI, (2) subjective norm (SN) related to learning medical AI, (3) perceived self-efficacy (PSE) of learning medical AI, (4) basic knowledge (BKn) of medical AI, (5) behavioral intention (BI) toward learning medical AI and (6) actual learning (AL) of medical AI. Confirmatory factor analysis and structural equation modelling were employed to analyze the data. The results showed that the proposed model had a good model fit and the theoretical hypotheses in relation to the TPB were mostly confirmed. Specifically, (a) BI had a significantly strong and positive impact on AL; (b) BI was significantly predicted by PR, SN and PSE, whilst BKn did not have a direct effect on BI; (c) PR was significantly and positively predicted by SN and PSE, but BKn failed to predict PR; (d) both SN and BKn had significant and positive impact on PSE, and BKn had a significantly positive effect on SN. Discussion was conducted regarding the proposed model, and new insights were provided for researchers and practitioners in medical education.
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21
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Gray K, Slavotinek J, Dimaguila GL, Choo D. Artificial Intelligence Education for the Health Workforce: Expert Survey of Approaches and Needs. JMIR MEDICAL EDUCATION 2022; 8:e35223. [PMID: 35249885 PMCID: PMC9016514 DOI: 10.2196/35223] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 02/04/2022] [Accepted: 03/05/2022] [Indexed: 06/02/2023]
Abstract
BACKGROUND The preparation of the current and future health workforce for the possibility of using artificial intelligence (AI) in health care is a growing concern as AI applications emerge in various care settings and specializations. At present, there is no obvious consensus among educators about what needs to be learned or how this learning may be supported or assessed. OBJECTIVE Our study aims to explore health care education experts' ideas and plans for preparing the health workforce to work with AI and identify critical gaps in curriculum and educational resources across a national health care system. METHODS A survey canvassed expert views on AI education for the health workforce in terms of educational strategies, subject matter priorities, meaningful learning activities, desired attitudes, and skills. A total of 39 senior people from different health workforce subgroups across Australia provided ratings and free-text responses in late 2020. RESULTS The responses highlighted the importance of education on ethical implications, suitability of large data sets for use in AI clinical applications, principles of machine learning, and specific diagnosis and treatment applications of AI as well as alterations to cognitive load during clinical work and the interaction between humans and machines in clinical settings. Respondents also outlined barriers to implementation, such as lack of governance structures and processes, resource constraints, and cultural adjustment. CONCLUSIONS Further work around the world of the kind reported in this survey can assist educators and education authorities who are responsible for preparing the health workforce to minimize the risks and realize the benefits of implementing AI in health care.
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Affiliation(s)
- Kathleen Gray
- Centre for Digital Transformation of Health, The University of Melbourne, Parkville, Australia
| | - John Slavotinek
- South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, Australia
- College of Medicine and Public Health, Flinders University, Adelaide, Australia
| | | | - Dawn Choo
- Centre for Digital Transformation of Health, The University of Melbourne, Parkville, Australia
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Aboalshamat K, Alhuzali R, Alalyani A, Alsharif S, Qadhi H, Almatrafi R, Ammash D, Alotaibi S. Medical and Dental Professionals Readiness for Artificial Intelligence for Saudi Arabia Vision 2030. INTERNATIONAL JOURNAL OF PHARMACEUTICAL RESEARCH AND ALLIED SCIENCES 2022. [DOI: 10.51847/nu8y6y6q1m] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Cox CB, Laborda T, Kynes JM, Hiremath G. Evolution in the Practice of Pediatric Endoscopy and Sedation. Front Pediatr 2021; 9:687635. [PMID: 34336742 PMCID: PMC8317208 DOI: 10.3389/fped.2021.687635] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 06/18/2021] [Indexed: 12/15/2022] Open
Abstract
The fields of pediatric gastrointestinal endoscopy and sedation are critically important to the diagnosis and treatment of gastrointestinal (GI) disease in children. Since its inception in the 1970s, pediatric endoscopy has benefitted from tremendous technological innovation related to the design of the endoscope and its associated equipment. Not only that, but expertise among pediatric gastroenterologists has moved the field forward to include a full complement of diagnostic and therapeutic endoscopic procedures in children. In this review, we discuss the remarkable history of pediatric endoscopy and highlight current limitations and future advances in the practice and technology of pediatric endoscopy and sedation.
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Affiliation(s)
- Conrad B Cox
- Division of Pediatric Gastroenterology Hepatology, and Nutrition, Monroe Carell Jr. Children's Hospital at Vanderbilt, Nashville, TN, United States
| | - Trevor Laborda
- Division of Pediatric Gastroenterology, Hepatology, and Nutrition, University of Utah Primary Children's Hospital, Salt Lake City, UT, United States.,Division of Pediatric Gastroenterology, Hepatology, and Nutrition, Baylor College of Medicine, Children's Hospital of San Antonio, San Antonio, TX, United States
| | - J Matthew Kynes
- Department of Anesthesiology, Monroe Carell Jr. Children's Hospital at Vanderbilt, Nashville, TN, United States
| | - Girish Hiremath
- Division of Pediatric Gastroenterology Hepatology, and Nutrition, Monroe Carell Jr. Children's Hospital at Vanderbilt, Nashville, TN, United States
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