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Hwang EJ, Park JE, Song KD, Yang DH, Kim KW, Lee JG, Yoon JH, Han K, Kim DH, Kim H, Park CM. 2023 Survey on User Experience of Artificial Intelligence Software in Radiology by the Korean Society of Radiology. Korean J Radiol 2024; 25:613-622. [PMID: 38942455 PMCID: PMC11214921 DOI: 10.3348/kjr.2023.1246] [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: 12/15/2023] [Revised: 04/24/2024] [Accepted: 04/26/2024] [Indexed: 06/30/2024] Open
Abstract
OBJECTIVE In Korea, radiology has been positioned towards the early adoption of artificial intelligence-based software as medical devices (AI-SaMDs); however, little is known about the current usage, implementation, and future needs of AI-SaMDs. We surveyed the current trends and expectations for AI-SaMDs among members of the Korean Society of Radiology (KSR). MATERIALS AND METHODS An anonymous and voluntary online survey was open to all KSR members between April 17 and May 15, 2023. The survey was focused on the experiences of using AI-SaMDs, patterns of usage, levels of satisfaction, and expectations regarding the use of AI-SaMDs, including the roles of the industry, government, and KSR regarding the clinical use of AI-SaMDs. RESULTS Among the 370 respondents (response rate: 7.7% [370/4792]; 340 board-certified radiologists; 210 from academic institutions), 60.3% (223/370) had experience using AI-SaMDs. The two most common use-case of AI-SaMDs among the respondents were lesion detection (82.1%, 183/223), lesion diagnosis/classification (55.2%, 123/223), with the target imaging modalities being plain radiography (62.3%, 139/223), CT (42.6%, 95/223), mammography (29.1%, 65/223), and MRI (28.7%, 64/223). Most users were satisfied with AI-SaMDs (67.6% [115/170, for improvement of patient management] to 85.1% [189/222, for performance]). Regarding the expansion of clinical applications, most respondents expressed a preference for AI-SaMDs to assist in detection/diagnosis (77.0%, 285/370) and to perform automated measurement/quantification (63.5%, 235/370). Most respondents indicated that future development of AI-SaMDs should focus on improving practice efficiency (81.9%, 303/370) and quality (71.4%, 264/370). Overall, 91.9% of the respondents (340/370) agreed that there is a need for education or guidelines driven by the KSR regarding the use of AI-SaMDs. CONCLUSION The penetration rate of AI-SaMDs in clinical practice and the corresponding satisfaction levels were high among members of the KSR. Most AI-SaMDs have been used for lesion detection, diagnosis, and classification. Most respondents requested KSR-driven education or guidelines on the use of AI-SaMDs.
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Affiliation(s)
- Eui Jin Hwang
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Ji Eun Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Kyoung Doo Song
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Dong Hyun Yang
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Kyung Won Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - June-Goo Lee
- Biomedical Engineering Research Center, Asan Institute for Life Sciences, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Jung Hyun Yoon
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Kyunghwa Han
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Dong Hyun Kim
- Department of Radiology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Hwiyoung Kim
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Chang Min Park
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea.
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Jarab AS, Al-Qerem W, Al-Hajjeh DM, Abu Heshmeh S, Mukattash TL, Naser AY, Alwafi H, Al Hamarneh YN. Artificial intelligence utilization in the healthcare setting: perceptions of the public in the UAE. INTERNATIONAL JOURNAL OF ENVIRONMENTAL HEALTH RESEARCH 2024:1-9. [PMID: 38832887 DOI: 10.1080/09603123.2024.2363472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 05/29/2024] [Indexed: 06/06/2024]
Abstract
Understanding the use of AI in healthcare is essential for the successful implementation of AI-driven healthcare solutions. The aim of this study was to evaluate public perception of AI utilization in healthcare settings. A validated questionnaire assessed general perceptions towards AI utilization, the use of AI by physician , and the use of AI by pharmacists . The study included 770 participants. The median perception score indicated an unfavorable attitude. Participants who had lower education level and those with no employment had a significantly lower perception scores than their counterpart. Participants who reported low income and those who visited the pharmacy five to ten times on average had a higher perception than their counterparts did. The reported negative perception necessitates the implementation of education campaigns to improve AI literacy and dispel any misconceptions and concerns, particularly among individuals with low education, high income, unemployment, and frequent pharmacy visits.
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Affiliation(s)
- Anan S Jarab
- College of Pharmacy, Al Ain University, Abu Dhabi, United Arab Emirates
- Department of Clinical Pharmacy, Faculty of Pharmacy, Jordan University of Science and Technology, Irbid, Jordan
| | - Walid Al-Qerem
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, Amman, Jordan
| | - Dua'a M Al-Hajjeh
- Department of Clinical Pharmacy, Faculty of Pharmacy, Jordan University of Science and Technology, Irbid, Jordan
| | - Shrouq Abu Heshmeh
- Department of Clinical Pharmacy, Faculty of Pharmacy, Jordan University of Science and Technology, Irbid, Jordan
| | - Tareq L Mukattash
- Department of Clinical Pharmacy, Faculty of Pharmacy, Jordan University of Science and Technology, Irbid, Jordan
| | - Abdallah Y Naser
- Department of Applied Pharmaceutical Sciences and Clinical Pharmacy, Faculty of Pharmacy, Isra University, Amman, Jordan
| | - Hassan Alwafi
- Department of Pharmacology and Toxicology, Faculty of Medicine, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Yazid N Al Hamarneh
- Department of Pharmacology, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Canada
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Gebauer S, Eckert C. Survey of US physicians' attitudes and knowledge of AI. BMJ Evid Based Med 2024:bmjebm-2023-112726. [PMID: 38355284 DOI: 10.1136/bmjebm-2023-112726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/25/2024] [Indexed: 02/16/2024]
Affiliation(s)
- Sarah Gebauer
- Anesthesiology, Elk River Anesthesia Associates, Steamboat Springs, Colorado, USA
| | - Carly Eckert
- University of Washington, Seattle, Washington, USA
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Miró Catalina Q, Femenia J, Fuster-Casanovas A, Marin-Gomez FX, Escalé-Besa A, Solé-Casals J, Vidal-Alaball J. Knowledge and Perception of the Use of AI and its Implementation in the Field of Radiology: Cross-Sectional Study. J Med Internet Res 2023; 25:e50728. [PMID: 37831495 PMCID: PMC10612005 DOI: 10.2196/50728] [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: 07/11/2023] [Revised: 08/31/2023] [Accepted: 09/25/2023] [Indexed: 10/14/2023] Open
Abstract
BACKGROUND Artificial Intelligence (AI) has been developing for decades, but in recent years its use in the field of health care has experienced an exponential increase. Currently, there is little doubt that these tools have transformed clinical practice. Therefore, it is important to know how the population perceives its implementation to be able to propose strategies for acceptance and implementation and to improve or prevent problems arising from future applications. OBJECTIVE This study aims to describe the population's perception and knowledge of the use of AI as a health support tool and its application to radiology through a validated questionnaire, in order to develop strategies aimed at increasing acceptance of AI use, reducing possible resistance to change and identifying possible sociodemographic factors related to perception and knowledge. METHODS A cross-sectional observational study was conducted using an anonymous and voluntarily validated questionnaire aimed at the entire population of Catalonia aged 18 years or older. The survey addresses 4 dimensions defined to describe users' perception of the use of AI in radiology, (1) "distrust and accountability," (2) "personal interaction," (3) "efficiency," and (4) "being informed," all with questions in a Likert scale format. Results closer to 5 refer to a negative perception of the use of AI, while results closer to 1 express a positive perception. Univariate and bivariate analyses were performed to assess possible associations between the 4 dimensions and sociodemographic characteristics. RESULTS A total of 379 users responded to the survey, with an average age of 43.9 (SD 17.52) years and 59.8% (n=226) of them identified as female. In addition, 89.8% (n=335) of respondents indicated that they understood the concept of AI. Of the 4 dimensions analyzed, "distrust and accountability" obtained a mean score of 3.37 (SD 0.53), "personal interaction" obtained a mean score of 4.37 (SD 0.60), "efficiency" obtained a mean score of 3.06 (SD 0.73) and "being informed" obtained a mean score of 3.67 (SD 0.57). In relation to the "distrust and accountability" dimension, women, people older than 65 years, the group with university studies, and the population that indicated not understanding the AI concept had significantly more distrust in the use of AI. On the dimension of "being informed," it was observed that the group with university studies rated access to information more positively and those who indicated not understanding the concept of AI rated it more negatively. CONCLUSIONS The majority of the sample investigated reported being familiar with the concept of AI, with varying degrees of acceptance of its implementation in radiology. It is clear that the most conflictive dimension is "personal interaction," whereas "efficiency" is where there is the greatest acceptance, being the dimension in which there are the best expectations for the implementation of AI in radiology.
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Affiliation(s)
- Queralt Miró Catalina
- Unitat de Suport a la Recerca de la Catalunya Central, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Sant Fruitós de Bages, Spain
- Health Promotion in Rural Areas Research Group, Gerència Territorial de la Catalunya Central, Institut Català de la Salut, Sant Fruitós de Bages, Spain
| | - Joaquim Femenia
- Faculty of Medicine, University of Vic-Central University of Catalonia, Vic, Spain
| | - Aïna Fuster-Casanovas
- Unitat de Suport a la Recerca de la Catalunya Central, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Sant Fruitós de Bages, Spain
| | - Francesc X Marin-Gomez
- Unitat de Suport a la Recerca de la Catalunya Central, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Sant Fruitós de Bages, Spain
- Health Promotion in Rural Areas Research Group, Gerència Territorial de la Catalunya Central, Institut Català de la Salut, Sant Fruitós de Bages, Spain
| | - Anna Escalé-Besa
- Unitat de Suport a la Recerca de la Catalunya Central, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Sant Fruitós de Bages, Spain
- Health Promotion in Rural Areas Research Group, Gerència Territorial de la Catalunya Central, Institut Català de la Salut, Sant Fruitós de Bages, Spain
- Faculty of Medicine, University of Vic-Central University of Catalonia, Vic, Spain
| | - Jordi Solé-Casals
- Data and Signal Processing group, Faculty of Science, Technology and Engineering, University of Vic-Central University of Catalonia, Vic, Spain
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Josep Vidal-Alaball
- Unitat de Suport a la Recerca de la Catalunya Central, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Sant Fruitós de Bages, Spain
- Health Promotion in Rural Areas Research Group, Gerència Territorial de la Catalunya Central, Institut Català de la Salut, Sant Fruitós de Bages, Spain
- Faculty of Medicine, University of Vic-Central University of Catalonia, Vic, Spain
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