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Daniyal M, Qureshi M, Marzo RR, Aljuaid M, Shahid D. Exploring clinical specialists' perspectives on the future role of AI: evaluating replacement perceptions, benefits, and drawbacks. BMC Health Serv Res 2024; 24:587. [PMID: 38725039 PMCID: PMC11080164 DOI: 10.1186/s12913-024-10928-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 03/29/2024] [Indexed: 05/13/2024] Open
Abstract
BACKGROUND OF STUDY Over the past few decades, the utilization of Artificial Intelligence (AI) has surged in popularity, and its application in the medical field is witnessing a global increase. Nevertheless, the implementation of AI-based healthcare solutions has been slow in developing nations like Pakistan. This unique study aims to assess the opinion of clinical specialists on the future replacement of AI, its associated benefits, and its drawbacks in form southern region of Pakistan. MATERIAL AND METHODS A cross-sectional selective study was conducted from 140 clinical specialists (Surgery = 24, Pathology = 31, Radiology = 35, Gynecology = 35, Pediatric = 17) from the neglected southern Punjab region of Pakistan. The study was analyzed using χ2 - the test of association and the nexus between different factors was examined by multinomial logistic regression. RESULTS Out of 140 respondents, 34 (24.3%) believed hospitals were ready for AI, while 81 (57.9%) disagreed. Additionally, 42(30.0%) were concerned about privacy violations, and 70(50%) feared AI could lead to unemployment. Specialists with less than 6 years of experience are more likely to embrace AI (p = 0.0327, OR = 3.184, 95% C.I; 0.262, 3.556) and those who firmly believe that AI knowledge will not replace their future tasks exhibit a lower likelihood of accepting AI (p = 0.015, OR = 0.235, 95% C.I: (0.073, 0.758). Clinical specialists who perceive AI as a technology that encompasses both drawbacks and benefits demonstrated a higher likelihood of accepting its adoption (p = 0.084, OR = 2.969, 95% C.I; 0.865, 5.187). CONCLUSION Clinical specialists have embraced AI as the future of the medical field while acknowledging concerns about privacy and unemployment.
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Affiliation(s)
- Muhammad Daniyal
- Department of Statistics, Faculty of Computing, Islamia University of Bahawalpur, Bahawalpur, Pakistan.
| | - Moiz Qureshi
- Government Degree College, TandoJam, Hyderabad, Sindh, Pakistan
| | - Roy Rillera Marzo
- Faculty of Humanities and Health Sciences, Curtin University, Malaysia, , Miri, Sarawak, Malaysia
- Jeffrey Cheah School of Medicine and Health Sciences, Global Public Health, Monash University Malaysia, Subang Jaya, Selangor, Malaysia
| | - Mohammed Aljuaid
- Department of Health Administration, College of Business Administration, King Saud University, Riyadh, Saudi Arabia
| | - Duaa Shahid
- Hult International Business School, 02141, Cambridge, MA, USA
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Amiri H, Peiravi S, Rezazadeh Shojaee SS, Rouhparvarzamin M, Nateghi MN, Etemadi MH, ShojaeiBaghini M, Musaie F, Anvari MH, Asadi Anar M. Medical, dental, and nursing students' attitudes and knowledge towards artificial intelligence: a systematic review and meta-analysis. BMC MEDICAL EDUCATION 2024; 24:412. [PMID: 38622577 PMCID: PMC11017500 DOI: 10.1186/s12909-024-05406-1] [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: 02/13/2024] [Accepted: 04/09/2024] [Indexed: 04/17/2024]
Abstract
BACKGROUND Nowadays, Artificial intelligence (AI) is one of the most popular topics that can be integrated into healthcare activities. Currently, AI is used in specialized fields such as radiology, pathology, and ophthalmology. Despite the advantages of AI, the fear of human labor being replaced by this technology makes some students reluctant to choose specific fields. This meta-analysis aims to investigate the knowledge and attitude of medical, dental, and nursing students and experts in this field about AI and its application. METHOD This study was designed based on PRISMA guidelines. PubMed, Scopus, and Google Scholar databases were searched with relevant keywords. After study selection according to inclusion criteria, data of knowledge and attitude were extracted for meta-analysis. RESULT Twenty-two studies included 8491 participants were included in this meta-analysis. The pooled analysis revealed a proportion of 0.44 (95%CI = [0.34, 0.54], P < 0.01, I2 = 98.95%) for knowledge. Moreover, the proportion of attitude was 0.65 (95%CI = [0.55, 0.75], P < 0.01, I2 = 99.47%). The studies did not show any publication bias with a symmetrical funnel plot. CONCLUSION Average levels of knowledge indicate the necessity of including relevant educational programs in the student's academic curriculum. The positive attitude of students promises the acceptance of AI technology. However, dealing with ethics education in AI and the aspects of human-AI cooperation are discussed. Future longitudinal studies could follow students to provide more data to guide how AI can be incorporated into education.
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Affiliation(s)
- Hamidreza Amiri
- Student Research Committee, Arak University of Medical Sciences, Arak, Iran
| | - Samira Peiravi
- Department of Emergency Medicine, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Seyedeh Sara Rezazadeh Shojaee
- Department of Nursing, Faculty of Nursing and Midwifery, Mashhad Medical Sciences, Islamic Azad University, Mashhad, Iran
| | - Motahareh Rouhparvarzamin
- Student Research Committee, School of Nursing and Midwifery, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
| | - Mohammad Naser Nateghi
- Student Research Committee, Faculty of Nursing and Midwifery, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mohammad Hossein Etemadi
- Students Research Committee, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mahdie ShojaeiBaghini
- Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
| | - Farhan Musaie
- Dentistry Student, Dental Branch, Islamic Azad University, Tehran, Iran
| | - Mohammad Hossein Anvari
- Master of Health Science, Faculty of Health Sciences, Universiti Kebangsaan Malaysia (UKM), Kuala Lumpur, Malaysia
| | - Mahsa Asadi Anar
- Student Research Committee, School of Medicine, Shahid Beheshti University of Medical Sciences, SBUMS, Arabi Ave, Daneshjoo Blvd, Velenjak, Tehran, 19839-63113, Iran.
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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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Schulz PJ, Lwin MO, Kee KM, Goh WWB, Lam TYT, Sung JJY. Modeling the influence of attitudes, trust, and beliefs on endoscopists' acceptance of artificial intelligence applications in medical practice. Front Public Health 2023; 11:1301563. [PMID: 38089040 PMCID: PMC10715310 DOI: 10.3389/fpubh.2023.1301563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 11/03/2023] [Indexed: 12/18/2023] Open
Abstract
Introduction The potential for deployment of Artificial Intelligence (AI) technologies in various fields of medicine is vast, yet acceptance of AI amongst clinicians has been patchy. This research therefore examines the role of antecedents, namely trust, attitude, and beliefs in driving AI acceptance in clinical practice. Methods We utilized online surveys to gather data from clinicians in the field of gastroenterology. Results A total of 164 participants responded to the survey. Participants had a mean age of 44.49 (SD = 9.65). Most participants were male (n = 116, 70.30%) and specialized in gastroenterology (n = 153, 92.73%). Based on the results collected, we proposed and tested a model of AI acceptance in medical practice. Our findings showed that while the proposed drivers had a positive impact on AI tools' acceptance, not all effects were direct. Trust and belief were found to fully mediate the effects of attitude on AI acceptance by clinicians. Discussion The role of trust and beliefs as primary mediators of the acceptance of AI in medical practice suggest that these should be areas of focus in AI education, engagement and training. This has implications for how AI systems can gain greater clinician acceptance to engender greater trust and adoption amongst public health systems and professional networks which in turn would impact how populations interface with AI. Implications for policy and practice, as well as future research in this nascent field, are discussed.
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Affiliation(s)
- Peter J. Schulz
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, Singapore
| | - May O. Lwin
- Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, Singapore
| | - Kalya M. Kee
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, Singapore
| | - Wilson W. B. Goh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
- Center for Biomedical Informatics, Nanyang Technological University, Singapore, Singapore
| | - Thomas Y. T Lam
- Faculty of Medicine, Institute of Digestive Diseases, The Chinese University of Hong Kong, Hong Kong, China
| | - Joseph J. Y. Sung
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
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Alanzi T, Alanazi F, Mashhour B, Altalhi R, Alghamdi A, Al Shubbar M, Alamro S, Alshammari M, Almusmili L, Alanazi L, Alzahrani S, Alalouni R, Alanzi N, Alsharifa A. Surveying Hematologists' Perceptions and Readiness to Embrace Artificial Intelligence in Diagnosis and Treatment Decision-Making. Cureus 2023; 15:e49462. [PMID: 38152821 PMCID: PMC10751460 DOI: 10.7759/cureus.49462] [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: 11/23/2023] [Indexed: 12/29/2023] Open
Abstract
AIM This study aims to explore the critical dimension of assessing the perceptions and readiness of hematologists to embrace artificial intelligence (AI) technologies in their diagnostic and treatment decision-making processes. METHODS This study used a cross-sectional design for collecting data related to the perceptions and readiness of hematologists using a validated online questionnaire-based survey. Both hematologists (MD) and postgraduate MD students in hematology were included in the study. A total of 188 participants, including 35 hematologists (MD) and 153 MD hematology students, completed the survey. RESULTS Major challenges include "AI's level of autonomy" and "the complexity in the field of medicine." Major barriers and risks identified include "lack of trust," "management's level of understanding," "dehumanization of healthcare," and "reduction in physicians' skills." Statistically significant differences in perceptions of benefits including resources (p=0.0326, p<0.05) and knowledge (p=0.0262, p<0.05) were observed between genders. Older physicians were observed to be more concerned about the use of AI compared to younger physicians (p<0.05). CONCLUSION While AI use in hematology diagnosis and treatment decision-making is positively perceived, issues such as lack of trust, transparency, regulations, and poor AI awareness can affect the adoption of AI.
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Affiliation(s)
- Turki Alanzi
- Department of Health Information Management and Technology, College of Public Health, Imam Abdulrahman Bin Faisal University, Dammam, SAU
| | - Fehaid Alanazi
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Jouf University, Sakakah, SAU
| | | | | | | | | | - Saud Alamro
- College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, SAU
| | | | | | - Lena Alanazi
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Jouf University, Sakakah, SAU
| | | | - Raneem Alalouni
- College of Public Health, Imam Abdulrahman Bin Faisal University, Dammam, SAU
| | - Nouf Alanzi
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Jouf University, Sakakah, SAU
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Ahmad MN, Abdallah SA, Abbasi SA, Abdallah AM. Student perspectives on the integration of artificial intelligence into healthcare services. Digit Health 2023; 9:20552076231174095. [PMID: 37312954 PMCID: PMC10259127 DOI: 10.1177/20552076231174095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 04/19/2023] [Indexed: 06/15/2023] Open
Abstract
Background Healthcare workers are often overworked, underfunded, and face many challenges. Integration of artificial intelligence into healthcare service provision can tackle these challenges by relieving burdens on healthcare workers. Since healthcare students are our future healthcare workers, we assessed the knowledge, attitudes, and perspectives of current healthcare students at Qatar University on the implementation of artificial intelligence into healthcare services. Methods This was a cross-sectional study of QU-Health Cluster students via an online survey over a three-week period in November 2021. Chi-squared tests and gamma coefficients were used to compare differences between categorical variables. Results One hundred and ninety-three QU-Health students responded. Most participants had a positive attitude towards artificial intelligence, finding it useful and reliable. The most popular perceived advantage of artificial intelligence was its ability to speed up work processes. Around 40% expressed concern about a threat to job security from artificial intelligence, and a majority believed that artificial intelligence cannot provide sympathetic care (57.9%). Participants who felt that artificial intelligence can better make diagnoses than humans also agreed that artificial intelligence could replace their job (p = 0.005). Male students had more knowledge (p = 0.005) and received more training (p = 0.005) about healthcare artificial intelligence. Participants cited a lack of expert mentorship as a barrier to obtaining knowledge about artificial intelligence, followed by lack of dedicated courses and funding. Conclusions More resources are required for students to develop a good understanding about artificial intelligence. Education needs to be supported by expert mentorship. Further work is needed on how best to integrate artificial intelligence teaching into university curricula.
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Affiliation(s)
- Muna N Ahmad
- Department of Biomedical Sciences, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
| | - Saja A Abdallah
- University of Birmingham Medical School, Edgbaston Campus, Birmingham, UK
| | - Saddam A Abbasi
- Department of Mathematics, Statistics, and Physics, Qatar University, Doha, Qatar
- Statistical Consulting Unit, College of Arts and Science, Qatar University, Doha, Qatar
| | - Atiyeh M Abdallah
- Department of Biomedical Sciences, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
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