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Qi W, Zhu X, He D, Wang B, Cao S, Dong C, Li Y, Chen Y, Wang B, Shi Y, Jiang G, Liu F, Boots LMM, Li J, Lou X, Yao J, Lu X, Kang J. Mapping Knowledge Landscapes and Emerging Trends in AI for Dementia Biomarkers: Bibliometric and Visualization Analysis. J Med Internet Res 2024; 26:e57830. [PMID: 39116438 DOI: 10.2196/57830] [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: 02/27/2024] [Revised: 05/04/2024] [Accepted: 06/25/2024] [Indexed: 08/10/2024] Open
Abstract
BACKGROUND With the rise of artificial intelligence (AI) in the field of dementia biomarker research, exploring its current developmental trends and research focuses has become increasingly important. This study, using literature data mining, analyzes and assesses the key contributions and development scale of AI in dementia biomarker research. OBJECTIVE The aim of this study was to comprehensively evaluate the current state, hot topics, and future trends of AI in dementia biomarker research globally. METHODS This study thoroughly analyzed the literature in the application of AI to dementia biomarkers across various dimensions, such as publication volume, authors, institutions, journals, and countries, based on the Web of Science Core Collection. In addition, scales, trends, and potential connections between AI and biomarkers were extracted and deeply analyzed through multiple expert panels. RESULTS To date, the field includes 1070 publications across 362 journals, involving 74 countries and 1793 major research institutions, with a total of 6455 researchers. Notably, 69.41% (994/1432) of the researchers ceased their studies before 2019. The most prevalent algorithms used are support vector machines, random forests, and neural networks. Current research frequently focuses on biomarkers such as imaging biomarkers, cerebrospinal fluid biomarkers, genetic biomarkers, and blood biomarkers. Recent advances have highlighted significant discoveries in biomarkers related to imaging, genetics, and blood, with growth in studies on digital and ophthalmic biomarkers. CONCLUSIONS The field is currently in a phase of stable development, receiving widespread attention from numerous countries, institutions, and researchers worldwide. Despite this, stable clusters of collaborative research have yet to be established, and there is a pressing need to enhance interdisciplinary collaboration. Algorithm development has shown prominence, especially the application of support vector machines and neural networks in imaging studies. Looking forward, newly discovered biomarkers are expected to undergo further validation, and new types, such as digital biomarkers, will garner increased research interest and attention.
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Affiliation(s)
- Wenhao Qi
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Xiaohong Zhu
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Danni He
- School of Nursing, Hangzhou Normal University, Hangzhou, China
- Nursing Department, Zhejiang Provincial People's Hospital, Hangzhou, China
| | - Bin Wang
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Shihua Cao
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Chaoqun Dong
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Yunhua Li
- College of Education, Chengdu College of Arts and Sciences, Sichuan, China
| | - Yanfei Chen
- School of Nursing, Hangzhou Normal University, Hangzhou, China
- Nursing Department, Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
| | - Bingsheng Wang
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Yankai Shi
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Guowei Jiang
- Department of Psychiatry and Neuropsychology and Alzheimer Center Limburg, School for Mental Health and Neuroscience (MHeNS), Maastricht University, Maastricht, Netherlands
| | - Fang Liu
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China
| | - Lizzy M M Boots
- Department of Psychiatry and Neuropsychology and Alzheimer Center Limburg, School for Mental Health and Neuroscience (MHeNS), Maastricht University, Maastricht, Netherlands
| | - Jiaqi Li
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Xiajing Lou
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Jiani Yao
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Xiaodong Lu
- Department of Neurology, Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
| | - Junling Kang
- Department of Neurology, The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
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Stogiannos N, Gillan C, Precht H, Reis CSD, Kumar A, O'Regan T, Ellis V, Barnes A, Meades R, Pogose M, Greggio J, Scurr E, Kumar S, King G, Rosewarne D, Jones C, van Leeuwen KG, Hyde E, Beardmore C, Alliende JG, El-Farra S, Papathanasiou S, Beger J, Nash J, van Ooijen P, Zelenyanszki C, Koch B, Langmack KA, Tucker R, Goh V, Turmezei T, Lip G, Reyes-Aldasoro CC, Alonso E, Dean G, Hirani SP, Torre S, Akudjedu TN, Ohene-Botwe B, Khine R, O'Sullivan C, Kyratsis Y, McEntee M, Wheatstone P, Thackray Y, Cairns J, Jerome D, Scarsbrook A, Malamateniou C. A multidisciplinary team and multiagency approach for AI implementation: A commentary for medical imaging and radiotherapy key stakeholders. J Med Imaging Radiat Sci 2024; 55:101717. [PMID: 39067309 DOI: 10.1016/j.jmir.2024.101717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2024] [Accepted: 06/27/2024] [Indexed: 07/30/2024]
Affiliation(s)
- Nikolaos Stogiannos
- Division of Midwifery & Radiography, City, University of London, United Kingdom; Magnitiki Tomografia Kerkiras, Corfu, Greece.
| | - Caitlin Gillan
- Joint Department of Medical Imaging, University Health Network, Canada; Departments of Radiation Oncology & Medical Imaging, University of Toronto, Toronto, Canada
| | - Helle Precht
- Health Sciences Research Centre, UCL University College, Radiology Department, Lillebelt Hospital, University Hospitals of Southern Denmark, Institute of Regional Health Research, University of Southern Denmark, Discipline of Medical Imaging and Radiation Therapy, University College Cork, Ireland
| | - Claudia Sa Dos Reis
- School of Health Sciences (HESAV), University of Applied Sciences and Arts Western Switzerland (HES-SO), Lausanne, Switzerland
| | - Amrita Kumar
- Frimley Health NHS Foundation Trust, British Institute of Radiology, United Kingdom
| | - Tracy O'Regan
- The Society and College of Radiographers, London, United Kingdom
| | | | - Anna Barnes
- King's Technology Evaluation Centre, School of biomedical engineering and imaging sciences, King's College London, United Kingdom
| | - Richard Meades
- Department of Nuclear Medicine, Royal Free London NHS Foundation, London, United Kingdom
| | | | - Julien Greggio
- Division of Midwifery & Radiography, City, University of London, United Kingdom; Italian Association of MR Radiographers, Cagliari, Italy
| | - Erica Scurr
- Department of Radiology, Royal Marsden Hospital, London, United Kingdom
| | | | - Graham King
- Annalise.ai Pty Ltd, Sydney, Australia; AI Special Focus Group, AXREM Association of Healthcare Technology Providers for Imaging Radiotherapy and Care, London, United Kingdom
| | | | - Catherine Jones
- Royal Brisbane and Womens' Hospital, Brisbane, Australia; I-MED Radiology, Brisbane, Australia; Faculty of Medicine and Health, University of Sydney, Sydney, Australia
| | - Kicky G van Leeuwen
- Romion Health, Utrecht, the Netherlands; Health AI Register, Utrecht, the Netherlands
| | - Emma Hyde
- University of Derby, Derby, United Kingdom
| | | | | | - Samar El-Farra
- Emirates Medical Society - The Radiographers Society of Emirates (RASE), United Arab Emirates
| | | | - Jan Beger
- Science and Technology Organisation, GE HealthCare, United States
| | - Jonathan Nash
- University Hospitals Sussex, United Kingdom; Kheiron Medical Technologies, London, United Kingdom; British Society of Breast Radiology, the Netherlands
| | - Peter van Ooijen
- Dept of Radiotherapy and Data Science Center in Health (DASH), University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Christiane Zelenyanszki
- Community Diagnostics, Barking, Havering and Redbridge University Hospitals NHS Trust, United Kingdom
| | - Barbara Koch
- Jheronimus Academy of Data Science, the Netherlands; Tilburg University, the Netherlands
| | | | | | - Vicky Goh
- School of Biomedical Engineering and Imaging Sciences, King's College London. Department of Radiology, Guy's & St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Tom Turmezei
- Norwich Medical School, University of East Anglia, United Kingdom; Department of Radiology, Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, United Kingdom
| | | | | | - Eduardo Alonso
- Artificial Intelligence Research Centre, City, University of London, United Kingdom
| | - Geraldine Dean
- ESTH NHS Trust, United Kingdom; NHS SW London Imaging Network, United Kingdom
| | - Shashivadan P Hirani
- Centre for Healthcare Innovation Research, City, University of London, London, United Kingdom
| | - Sofia Torre
- Frimley Health Foundation Trust, United Kingdom
| | - Theophilus N Akudjedu
- Institute of Medical Imaging & Visualisation, Department of Medical Science & Public Health, Faculty of Health & Social Sciences, Bournemouth University, United Kingdom
| | - Benard Ohene-Botwe
- Department of Midwifery & Radiography, City, University of London, United Kingdom
| | - Ricardo Khine
- Institute of Health Sciences Education, Faculty of Medicine and Dentistry, Queen Mary, University of London, United Kingdom
| | - Chris O'Sullivan
- Department of Midwifery & Radiography, School of Health & Psychological Sciences, City, University of London, United Kingdom
| | - Yiannis Kyratsis
- Erasmus School of Health Policy and Management, Erasmus University Rotterdam, the Netherlands
| | - Mark McEntee
- Discipline of Medical Imaging and Radiation Therapy, University College Cork, Ireland; Institute of Regional Health Research, University of Southern Denmark, Denmark; Faculty of Health Sciences, The University of Sydney, Australia
| | | | | | - James Cairns
- Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom; Faculty of Medicine & Health, University of Leeds, Leeds, United Kingdom
| | | | - Andrew Scarsbrook
- Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom; Faculty of Medicine & Health, University of Leeds, Leeds, United Kingdom
| | - Christina Malamateniou
- Department of Midwifery & Radiography, City, University of London, United Kingdom; European Society of Medical Imaging Informatics, Vienna, Austria
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Rasche A, Brader P, Borggrefe J, Seuss H, Carr Z, Hebecker A, Ten Cate G. Impact of intelligent virtual and AI-based automated collimation functionalities on the efficiency of radiographic acquisitions. Radiography (Lond) 2024; 30:1073-1079. [PMID: 38763093 DOI: 10.1016/j.radi.2024.05.002] [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: 03/16/2024] [Revised: 05/02/2024] [Accepted: 05/08/2024] [Indexed: 05/21/2024]
Abstract
INTRODUCTION Intelligent virtual and AI-based collimation functionalities have the potential to enable an efficient workflow for radiographers, but the specific impact on clinical routines is still unknown. This study analyzes primarily the influence of intelligent collimation functionalities on the examination time and the number of needed interactions with the radiography system. METHODS An observational study was conducted on the use of three camera-based intelligent features at five clinical sites in Europe and the USA: AI-based auto thorax collimation (ATC), smart virtual ortho (SVO) collimation for stitched long-leg and full-spine examinations, and virtual collimation (VC) at the radiography system workstation. Two people conducted semi-structured observations during routine examinations to collect data with the functionalities either activated or deactivated. RESULTS Median exam duration was 31 vs. 45 s (p < 0.0001) for 95 thorax examinations with ATC and 94 without ATC. For stitched orthopedic examinations, 34 were performed with SVO and 40 without SVO, and the median exam duration was 62 vs. 82 s (p < 0.0001). The median time for setting the ortho range - i.e., the time between setting the upper and the lower limits of the collimation field - was 7 vs. 16 s for 39 examinations with SVO and 43 without SVO (p < 0.0001). In 109 thorax examinations with ATC and 112 without ATC, the median number of system interactions was 1 vs. 2 (p < 0.0001). VC was used to collimate in 2.4% and to check the collimation field in 68.5% of 292 observed chest and other examinations. CONCLUSION ATC and SVO enable the radiographer to save time during chest or stitched examinations. Additionally, ATC reduces machine interactions during chest examinations. IMPLICATIONS FOR PRACTICE System and artificial intelligence can support the radiographer during the image acquisition by providing a more efficient workflow.
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Affiliation(s)
- A Rasche
- Siemens Healthineers AG, Siemensstrasse 3, 91301 Forchheim, Germany.
| | - P Brader
- Diagnostikum Linz GmbH, Saporoshjestrasse 3, 4030 Linz, Austria.
| | - J Borggrefe
- Department of Radiology, Johannes Wesling Universitätsklinikum/Mühlenkreiskliniken, Hans-Nolte-Strasse 1, 32429 Minden, Germany.
| | - H Seuss
- Department of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Maximiliansplatz 3, 91054 Erlangen, Germany; Department of Radiology, Klinikum Forchheim - Fränkische Schweiz, Krankenhausstrasse 10, 91301 Forchheim, Germany.
| | - Z Carr
- Department of Radiology, The Ohio State University Wexner Medical Center, 274V Doan Hall, 450 W. 10th Ave, Columbus, OH, 43210, United States.
| | - A Hebecker
- Siemens Healthineers AG, Siemensstrasse 3, 91301 Forchheim, Germany.
| | - G Ten Cate
- Siemens Healthineers AG, Siemensstrasse 3, 91301 Forchheim, Germany.
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Pedersen MRV, Kusk MW, Lysdahlgaard S, Mork-Knudsen H, Malamateniou C, Jensen J. A Nordic survey on artificial intelligence in the radiography profession - Is the profession ready for a culture change? Radiography (Lond) 2024; 30:1106-1115. [PMID: 38781794 DOI: 10.1016/j.radi.2024.04.020] [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: 01/18/2024] [Revised: 04/12/2024] [Accepted: 04/22/2024] [Indexed: 05/25/2024]
Abstract
INTRODUCTION The impact of artificial intelligence (AI) on the radiography profession remains uncertain. Although AI has been increasingly used in clinical radiography, the perspectives of the radiography professionals in Nordic countries have yet to be examined. The primary aim was to examine views of Nordic radiographers 'on AI, with focus on perspectives, engagement, and knowledge of AI. METHODS Radiographers from Denmark, Norway, Sweden, Iceland, Greenland, and the Faroe Island were invited through social media platforms to participate in an online survey from March to June 2023. The survey encompassed 29-items and included 4 sections a) demographics, b) barriers and enablers on AI, c) perspectives and experiences of AI and d) knowledge of AI in radiography. Edgars Schein's model of organizational culture was employed to analyse Nordic radiographers' perspectives on AI. RESULTS Overall, a total of 421 respondents participated in the survey. A majority were positive/somewhat positive towards AI in radiography e.g., 77.9 % (n = 342) thought that AI would have a positive effect on the profession, and 26% thought that AI would reduce the administrative workload. Most radiographers agreed or strongly agreed that clinicians may have access to AI generated reports (76.8 %, n = 297). Nevertheless, a total of 86 (20.1%) agree or somewhat agreed that AI a potential risk for radiography. CONCLUSION Nordic radiographers are generally positive towards AI, yet uncertainties regarding its implementation persist. The findings underscore the importance of understanding these challenges for the responsible integration of AI systems. Carefully weighing the expected influence of AI against key incentives will support a seamless integration of AI for the benefit not just of the patients, but also of the radiography profession. IMPLICATIONS FOR PRACTICE Understanding incentives factors and barriers can help address uncertainties during implementation of AI in clinical practice.
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Affiliation(s)
- M R V Pedersen
- Department of Radiology, Vejle Hospital - Part of Lillebaelt Hospital, Vejle, Denmark; Department of Regional Health Research, University of Southern Denmark, Odense, Denmark; Discipline of Medical Imaging & Radiation Therapy, School of Medicine, University College Cork, Ireland.
| | - M W Kusk
- Department of Regional Health Research, University of Southern Denmark, Odense, Denmark; Department of Radiology and Nuclear Medicine, University Hospital of Southern Denmark, Esbjerg, Denmark; IRIS - Imaging Research Initiative Southwest, University Hospital of Southern Denmark, Esbjerg, Denmark; Radiography and Diagnostic Imaging, School of Medicine, University College Dublin, Dublin, Ireland
| | - S Lysdahlgaard
- Department of Regional Health Research, University of Southern Denmark, Odense, Denmark; Department of Radiology and Nuclear Medicine, University Hospital of Southern Denmark, Esbjerg, Denmark; IRIS - Imaging Research Initiative Southwest, University Hospital of Southern Denmark, Esbjerg, Denmark
| | - H Mork-Knudsen
- Department of Radiology, Haukeland University Hospital, Norway
| | - C Malamateniou
- Department of Radiography, Division of Midwifery and Radiography, School of Health and Psychological Sciences, University of London, UK; European Federation of Radiographer Societies, Churchilllaan 11, 3527 GV, Utrecht, the Netherlands
| | - J Jensen
- Research and Innovation Unit of Radiology, University Hospital of Southern Denmark, Odense Denmark; Department of Radiology, Odense University Hospital, Odense, Denmark
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Stogiannos N, Litosseliti L, O'Regan T, Scurr E, Barnes A, Kumar A, Malik R, Pogose M, Harvey H, McEntee MF, Malamateniou C. Black box no more: A cross-sectional multi-disciplinary survey for exploring governance and guiding adoption of AI in medical imaging and radiotherapy in the UK. Int J Med Inform 2024; 186:105423. [PMID: 38531254 DOI: 10.1016/j.ijmedinf.2024.105423] [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: 08/15/2023] [Revised: 03/12/2024] [Accepted: 03/20/2024] [Indexed: 03/28/2024]
Abstract
BACKGROUND Medical Imaging and radiotherapy (MIRT) are at the forefront of artificial intelligence applications. The exponential increase of these applications has made governance frameworks necessary to uphold safe and effective clinical adoption. There is little information about how healthcare practitioners in MIRT in the UK use AI tools, their governance and associated challenges, opportunities and priorities for the future. METHODS This cross-sectional survey was open from November to December 2022 to MIRT professionals who had knowledge or made use of AI tools, as an attempt to map out current policy and practice and to identify future needs. The survey was electronically distributed to the participants. Statistical analysis included descriptive statistics and inferential statistics on the SPSS statistical software. Content analysis was employed for the open-ended questions. RESULTS Among the 245 responses, the following were emphasised as central to AI adoption: governance frameworks, practitioner training, leadership, and teamwork within the AI ecosystem. Prior training was strongly correlated with increased knowledge about AI tools and frameworks. However, knowledge of related frameworks remained low, with different professionals showing different affinity to certain frameworks related to their respective roles. Common challenges and opportunities of AI adoption were also highlighted, with recommendations for future practice.
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Affiliation(s)
- Nikolaos Stogiannos
- Department of Radiography, City, University of London, UK; Magnitiki Tomografia Kerkyras, Greece.
| | - Lia Litosseliti
- School of Health & Psychological Sciences, City, University of London, UK.
| | - Tracy O'Regan
- The Society and College of Radiographers, London, UK.
| | | | - Anna Barnes
- King's Technology Evaluation Centre (KiTEC), School of Biomedical Engineering & Imaging Science, King's College London, UK.
| | | | | | | | | | - Mark F McEntee
- Discipline of Medical Imaging and Radiation Therapy, University College Cork, Ireland.
| | - Christina Malamateniou
- Department of Radiography, City, University of London, UK; European Society of Medical Imaging Informatics, Vienna, Austria.
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Pedersen MRV, Kusk MW, Lysdahlgaard S, Mork-Knudsen H, Malamateniou C, Jensen J. Nordic radiographers' and students' perspectives on artificial intelligence - A cross-sectional online survey. Radiography (Lond) 2024; 30:776-783. [PMID: 38461583 DOI: 10.1016/j.radi.2024.02.020] [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: 11/07/2023] [Revised: 01/17/2024] [Accepted: 02/26/2024] [Indexed: 03/12/2024]
Abstract
INTRODUCTION The integration of artificial intelligence (AI) into the domain of radiography holds substantial potential in various aspects including workflow efficiency, image processing, patient positioning, and quality assurance. The successful implementation of AI within a Radiology department necessitates the participation of key stakeholders, particularly radiographers. The study aimed to provide a comprehensive investigation about Nordic radiographers' perspectives and attitudes towards AI in radiography. METHODS An online 29-item survey was distributed via social media platforms to Nordic students and radiographers working in Denmark, Norway, Sweden, Iceland, Greenland, and the Faroe Islands including items on demographics, specialization, educational background, place of work and perspectives and knowledge on AI. The items were a mix of closed-type and scaled questions, with the option for free-text responses when relevant. RESULTS The survey received responses from all Nordic countries with 586 respondents, 26.8% males, 72.1% females, and 1.1% non-binary/self-defined or preferred not to say. The mean age was 37.2 with a standard deviation (SD) of ±12.1 years, and the mean number of years since qualification was 14.2 SD ± 10.3 years. A total of 43% (n = 254) of the respondents had not received any AI training in clinical practice. Whereas 13% (n = 76) had received AI during radiography undergrad training. A total of 77.9% (n = 412) expressed interest in pursuing AI education. The majority of respondents were aware of the potential use of AI (n = 485, 82.8%) and 39.1% (n = 204) had no reservations about AI. CONCLUSION Overall, this study found that Nordic radiographers have a positive attitude toward AI. Very limited training or education has been provided to the radiographers. Especially since 82.8% reports on plans to implement AI in clinical practice. In general, awareness of AI applications is high, but the educational level is low for Nordic radiographers. IMPLICATION FOR PRACTICE This study emphasises the favourable view of AI held by students and Nordic radiographers. However, there is a need for continuous professional development to facilitate the implementation and effective utilization of AI tools within the field of radiography.
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Affiliation(s)
- M R V Pedersen
- Department of Radiology, Vejle Hospital - Part of Lillebaelt Hospital, Vejle, Denmark; Department of Radiology, Kolding Hospital- Part of Lillebaelt Hospital, Kolding, Denmark; Department of Regional Health Research, University of Southern Denmark, Odense, Denmark; Discipline of Medical Imaging & Radiation Therapy, School of Medicine, University College Cork, Ireland.
| | - M W Kusk
- Department of Regional Health Research, University of Southern Denmark, Odense, Denmark; Department of Radiology and Nuclear Medicine, University Hospital of Southern Denmark, Esbjerg, Denmark; IRIS - Imaging Research Initiative Southwest, University Hospital of Southern Denmark, Esbjerg, Denmark; Radiography and Diagnostic Imaging, School of Medicine, University College Dublin, Dublin, Ireland
| | - S Lysdahlgaard
- Department of Regional Health Research, University of Southern Denmark, Odense, Denmark; Department of Radiology and Nuclear Medicine, University Hospital of Southern Denmark, Esbjerg, Denmark; IRIS - Imaging Research Initiative Southwest, University Hospital of Southern Denmark, Esbjerg, Denmark
| | - H Mork-Knudsen
- Department of Radiology, Haukeland University Hospital, Norway
| | - C Malamateniou
- Department of Radiography, Division of Midwifery and Radiography, School of Health and Psychological Sciences, City, University of London, UK; European Federation of Radiographer Societies, Churchilllaan 11, 3527 GV, Utrecht, the Netherlands
| | - J Jensen
- Research and Innovation Unit of Radiology, University of Southern Denmark, Odense, Denmark; Department of Radiology, Odense University Hospital, Odense, Denmark
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Toh ZA, Berg B, Han QYC, Hey HWD, Pikkarainen M, Grotle M, He HG. Clinical Decision Support System Used in Spinal Disorders: Scoping Review. J Med Internet Res 2024; 26:e53951. [PMID: 38502157 PMCID: PMC10988379 DOI: 10.2196/53951] [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: 10/28/2023] [Revised: 01/29/2024] [Accepted: 02/10/2024] [Indexed: 03/20/2024] Open
Abstract
BACKGROUND Spinal disorders are highly prevalent worldwide with high socioeconomic costs. This cost is associated with the demand for treatment and productivity loss, prompting the exploration of technologies to improve patient outcomes. Clinical decision support systems (CDSSs) are computerized systems that are increasingly used to facilitate safe and efficient health care. Their applications range in depth and can be found across health care specialties. OBJECTIVE This scoping review aims to explore the use of CDSSs in patients with spinal disorders. METHODS We used the Joanna Briggs Institute methodological guidance for this scoping review and reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) statement. Databases, including PubMed, Embase, Cochrane, CINAHL, Web of Science, Scopus, ProQuest, and PsycINFO, were searched from inception until October 11, 2022. The included studies examined the use of digitalized CDSSs in patients with spinal disorders. RESULTS A total of 4 major CDSS functions were identified from 31 studies: preventing unnecessary imaging (n=8, 26%), aiding diagnosis (n=6, 19%), aiding prognosis (n=11, 35%), and recommending treatment options (n=6, 20%). Most studies used the knowledge-based system. Logistic regression was the most commonly used method, followed by decision tree algorithms. The use of CDSSs to aid in the management of spinal disorders was generally accepted over the threat to physicians' clinical decision-making autonomy. CONCLUSIONS Although the effectiveness was frequently evaluated by examining the agreement between the decisions made by the CDSSs and the health care providers, comparing the CDSS recommendations with actual clinical outcomes would be preferable. In addition, future studies on CDSS development should focus on system integration, considering end user's needs and preferences, and external validation and impact studies to assess effectiveness and generalizability. TRIAL REGISTRATION OSF Registries osf.io/dyz3f; https://osf.io/dyz3f.
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Affiliation(s)
- Zheng An Toh
- National University Hospital, National University Health System, Singapore, Singapore
| | - Bjørnar Berg
- Centre for Intelligent Musculoskeletal Health, Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway
| | | | - Hwee Weng Dennis Hey
- Division of Orthopaedic Surgery, National University Hospital, National University Health System, Singapore, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Minna Pikkarainen
- Department of Rehabilitation and Health Technology, Oslo Metropolitan University, Oslo, Norway
- Martti Ahtisaari Institute, Oulu Business School, Oulu University, Oulu, Finland
- Department of Product Design, Oslo Metropolitan University, Oslo, Norway
| | - Margreth Grotle
- Centre for Intelligent Musculoskeletal Health, Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway
- Department of Research and Innovation, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
| | - Hong-Gu He
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
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Lokaj B, Pugliese MT, Kinkel K, Lovis C, Schmid J. Barriers and facilitators of artificial intelligence conception and implementation for breast imaging diagnosis in clinical practice: a scoping review. Eur Radiol 2024; 34:2096-2109. [PMID: 37658895 PMCID: PMC10873444 DOI: 10.1007/s00330-023-10181-6] [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: 03/02/2023] [Revised: 06/07/2023] [Accepted: 07/10/2023] [Indexed: 09/05/2023]
Abstract
OBJECTIVE Although artificial intelligence (AI) has demonstrated promise in enhancing breast cancer diagnosis, the implementation of AI algorithms in clinical practice encounters various barriers. This scoping review aims to identify these barriers and facilitators to highlight key considerations for developing and implementing AI solutions in breast cancer imaging. METHOD A literature search was conducted from 2012 to 2022 in six databases (PubMed, Web of Science, CINHAL, Embase, IEEE, and ArXiv). The articles were included if some barriers and/or facilitators in the conception or implementation of AI in breast clinical imaging were described. We excluded research only focusing on performance, or with data not acquired in a clinical radiology setup and not involving real patients. RESULTS A total of 107 articles were included. We identified six major barriers related to data (B1), black box and trust (B2), algorithms and conception (B3), evaluation and validation (B4), legal, ethical, and economic issues (B5), and education (B6), and five major facilitators covering data (F1), clinical impact (F2), algorithms and conception (F3), evaluation and validation (F4), and education (F5). CONCLUSION This scoping review highlighted the need to carefully design, deploy, and evaluate AI solutions in clinical practice, involving all stakeholders to yield improvement in healthcare. CLINICAL RELEVANCE STATEMENT The identification of barriers and facilitators with suggested solutions can guide and inform future research, and stakeholders to improve the design and implementation of AI for breast cancer detection in clinical practice. KEY POINTS • Six major identified barriers were related to data; black-box and trust; algorithms and conception; evaluation and validation; legal, ethical, and economic issues; and education. • Five major identified facilitators were related to data, clinical impact, algorithms and conception, evaluation and validation, and education. • Coordinated implication of all stakeholders is required to improve breast cancer diagnosis with AI.
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Affiliation(s)
- Belinda Lokaj
- Geneva School of Health Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland, Delémont, Switzerland.
- Faculty of Medicine, University of Geneva, Geneva, Switzerland.
- Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland.
| | - Marie-Thérèse Pugliese
- Geneva School of Health Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland, Delémont, Switzerland
| | - Karen Kinkel
- Réseau Hospitalier Neuchâtelois, Neuchâtel, Switzerland
| | - Christian Lovis
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland
| | - Jérôme Schmid
- Geneva School of Health Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland, Delémont, Switzerland
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9
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Stogiannos N, O'Regan T, Scurr E, Litosseliti L, Pogose M, Harvey H, Kumar A, Malik R, Barnes A, McEntee MF, Malamateniou C. AI implementation in the UK landscape: Knowledge of AI governance, perceived challenges and opportunities, and ways forward for radiographers. Radiography (Lond) 2024; 30:612-621. [PMID: 38325103 DOI: 10.1016/j.radi.2024.01.019] [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: 01/04/2024] [Accepted: 01/26/2024] [Indexed: 02/09/2024]
Abstract
INTRODUCTION Despite the rapid increase of AI-enabled applications deployed in clinical practice, many challenges exist around AI implementation, including the clarity of governance frameworks, usability of validation of AI models, and customisation of training for radiographers. This study aimed to explore the perceptions of diagnostic and therapeutic radiographers, with existing theoretical and/or practical knowledge of AI, on issues of relevance to the field, such as AI implementation, including knowledge of AI governance and procurement, perceptions about enablers and challenges and future priorities for AI adoption. METHODS An online survey was designed and distributed to UK-based qualified radiographers who work in medical imaging and/or radiotherapy and have some previous theoretical and/or practical knowledge of working with AI. Participants were recruited through the researchers' professional networks on social media with support from the AI advisory group of the Society and College of Radiographers. Survey questions related to AI training/education, knowledge of AI governance frameworks, data privacy procedures, AI implementation considerations, and priorities for AI adoption. Descriptive statistics were employed to analyse the data, and chi-square tests were used to explore significant relationships between variables. RESULTS In total, 88 valid responses were received. Most radiographers (56.6 %) had not received any AI-related training. Also, although approximately 63 % of them used an evaluation framework to assess AI models' performance before implementation, many (36.9 %) were still unsure about suitable evaluation methods. Radiographers requested clearer guidance on AI governance, ample time to implement AI in their practice safely, adequate funding, effective leadership, and targeted support from AI champions. AI training, robust governance frameworks, and patient and public involvement were seen as priorities for the successful implementation of AI by radiographers. CONCLUSION AI implementation is progressing within radiography, but without customised training, clearer governance, key stakeholder engagement and suitable new roles created, it will be hard to harness its benefits and minimise related risks. IMPLICATIONS FOR PRACTICE The results of this study highlight some of the priorities and challenges for radiographers in relation to AI adoption, namely the need for developing robust AI governance frameworks and providing optimal AI training.
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Affiliation(s)
- N Stogiannos
- Division of Midwifery & Radiography, City, University of London, UK; Medical Imaging Department, Corfu General Hospital, Greece.
| | - T O'Regan
- The Society and College of Radiographers, London, UK.
| | - E Scurr
- The Royal Marsden NHS Foundation Trust, UK.
| | - L Litosseliti
- School of Health & Psychological Sciences, City, University of London, UK.
| | - M Pogose
- Quality Assurance and Regulatory Affairs, Hardian Health, UK.
| | | | - A Kumar
- Frimley Health NHS Foundation Trust, UK.
| | - R Malik
- Bolton NHS Foundation Trust, UK.
| | - A Barnes
- King's Technology Evaluation Centre (KiTEC), School of Biomedical Engineering & Imaging Science, King's College London, UK.
| | - M F McEntee
- Discipline of Medical Imaging and Radiation Therapy, University College Cork, Ireland.
| | - C Malamateniou
- Division of Midwifery & Radiography, City, University of London, UK; Society and College of Radiographers AI Advisory Group, London, UK; European Society of Medical Imaging Informatics, Vienna, Austria; European Federation of Radiographer Societies, Cumieira, Portugal.
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10
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Hussain A, Lee C, Hu E, Amirouche F. Deep learning automation of radiographic patterns for hallux valgus diagnosis. World J Orthop 2024; 15:105-109. [PMID: 38464350 PMCID: PMC10921175 DOI: 10.5312/wjo.v15.i2.105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 12/22/2023] [Accepted: 01/04/2024] [Indexed: 02/07/2024] Open
Abstract
Artificial intelligence (AI) and deep learning are becoming increasingly powerful tools in diagnostic and radiographic medicine. Deep learning has already been utilized for automated detection of pneumonia from chest radiographs, diabetic retinopathy, breast cancer, skin carcinoma classification, and metastatic lymphadenopathy detection, with diagnostic reliability akin to medical experts. In the World Journal of Orthopedics article, the authors apply an automated and AI-assisted technique to determine the hallux valgus angle (HVA) for assessing HV foot deformity. With the U-net neural network, the authors constructed an algorithm for pattern recognition of HV foot deformity from anteroposterior high-resolution radiographs. The performance of the deep learning algorithm was compared to expert clinician manual performance and assessed alongside clinician-clinician variability. The authors found that the AI tool was sufficient in assessing HVA and proposed the system as an instrument to augment clinical efficiency. Though further sophistication is needed to establish automated algorithms for more complicated foot pathologies, this work adds to the growing evidence supporting AI as a powerful diagnostic tool.
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Affiliation(s)
- Angela Hussain
- Department of Orthopaedic Surgery, University of Illinois College of Medicine, Chicago, IL 60612, United States
| | - Cadence Lee
- Department of Orthopaedic Surgery, University of Illinois College of Medicine, Chicago, IL 60612, United States
| | - Eric Hu
- Department of Orthopaedic Surgery, University of Illinois College of Medicine, Chicago, IL 60612, United States
| | - Farid Amirouche
- Department of Orthopaedics Surgery, University of Illinois at Chicago, Chicago, IL 60612, United States
- Department of Orthopaedic Surgery, Northshore University Health System, Skokie, IL 6007, United States
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11
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Mohamed A, Mat Sanusi NSA, Azman NS, Zailani NS, Jasmin NH, Che Isa IN. Factors influencing final year radiography students' intention to pursue postgraduate education in medical imaging. Radiography (Lond) 2024; 30:388-393. [PMID: 38159357 DOI: 10.1016/j.radi.2023.12.006] [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: 04/12/2023] [Revised: 10/27/2023] [Accepted: 12/13/2023] [Indexed: 01/03/2024]
Abstract
INTRODUCTION Postgraduate education in medical imaging is an important platform that can support in preparing radiographers for the role extension and advancement in radiography. Thus, this study aims to identify the factors influencing final year radiography students' intention to pursue postgraduate education in medical imaging. METHODS An online cross-sectional survey was conducted among final year students in medical imaging programs from six institutions in Malaysia. Purposive convenience sampling has been employed. Data collection was related to students' interest in postgraduate study and possible factors that may affect students' intention to pursue postgraduate education after study degree completion. The questionnaire was a combination of a Likert Scale and open-ended question. RESULTS A total of 148 (female, n = 132 and male, n = 16) responses were included in the analysis. Among the participants, n = 93 (62.8 %) of students intended to pursue study. The highest choice of study was mixed mode (41.9 %) and cardiac imaging was the field of choice by the students (22.3 %). Five factors have been found to significantly correlate with the students' intention to pursue postgraduate study in medical imaging which were student attributes, being an academician, remuneration, finance, and social influences (p < 0.05). Two factors that did not significantly correlate with the students' intention were being an expert in the field and Covid-19 (p > 0.05). CONCLUSION Five out of seven factors tested were found to significantly influence students' decision to pursue postgraduate education in medical imaging. Effective strategies based on the influencing factors should be strategized to encourage more students to pursue postgraduate education in medical imaging. IMPLICATIONS FOR PRACTICE Implementation of effective strategies based on the influencing factors will improve access to education among radiography students, ultimately enhancing future radiographers' capability and competency.
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Affiliation(s)
- A Mohamed
- Centre for Diagnostic, Therapeutic and Investigative Studies, Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia.
| | - N S A Mat Sanusi
- Centre for Diagnostic, Therapeutic and Investigative Studies, Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - N S Azman
- Centre for Diagnostic, Therapeutic and Investigative Studies, Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - N S Zailani
- Faculty of Science and Marine Environment, Universiti Malaysia Terengganu, Terengganu, Malaysia
| | - N H Jasmin
- Faculty of Health Sciences, Universiti Sultan Zainal Abidin, Terengganu, Malaysia
| | - I N Che Isa
- Centre for Diagnostic, Therapeutic and Investigative Studies, Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
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12
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Mdletshe S. Driving professional development and growth through research: a commentary. J Med Radiat Sci 2023; 70:338-344. [PMID: 37015837 PMCID: PMC10500103 DOI: 10.1002/jmrs.678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 03/17/2023] [Indexed: 04/06/2023] Open
Abstract
Research remains one of the essential elements that drive the growth of a profession since it contributes to the development of knowledge. Research also creates value for public service and leads to better health outcomes. Research output from the Medical Imaging and Radiation Sciences (MIRS) profession is currently lacking in New Zealand while the output from Australia can also be improved. For research to be improved, there needs to be intentionality. This commentary presents proposed considerations that could contribute toward increasing research impact and research output in the MIRS profession in Australia and New Zealand.
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Affiliation(s)
- Sibusiso Mdletshe
- Department of Anatomy and Medical Imaging, Faculty of Medical and Health SciencesUniversity of AucklandAucklandNew Zealand
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13
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Stogiannos N, Psimitis A, Bougias H, Georgiadou E, Leandrou S, Papavasileiou P, Polycarpou I, Malamateniou C, McEntee MF. Exploring radiographers' perceptions and knowledge about patient lead shielding: a cross-sectional study in Greece and Cyprus. RADIATION PROTECTION DOSIMETRY 2023; 199:1401-1409. [PMID: 37415570 DOI: 10.1093/rpd/ncad194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 06/02/2023] [Accepted: 06/19/2023] [Indexed: 07/08/2023]
Abstract
The present study aimed to explore radiographers' knowledge, clinical practice and perceptions regarding the use of patient lead shielding in Greece and Cyprus. Qualitative data were analyzed using conceptual content analysis and through the classification of findings into themes and categories. A total of 216 valid responses were received. Most respondents reported not being aware of the patient shielding recommendations issued by the American Association of Physicists in Medicine (67%) or the guidance issued by the British Institute of Radiology (69%). Shielding-related training was generally not provided by radiography departments (74%). Most of them (85%) reported that they need specific guidance on lead shielding practices. Also, 82% of the respondents said that lead shielding should continue to be used outside the pelvic area when imaging pregnant patients. Pediatric patients are the most common patient category to which lead shielding was applied. Significant gaps in relevant training have been identified among radiographers in Greece and Cyprus, highlighting the need for new protocols and provision of adequate training on lead shielding practices. Radiography departments should invest in appropriate shielding equipment and adequately train their staff.
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Affiliation(s)
- Nikolaos Stogiannos
- Discipline of Medical Imaging and Radiation Therapy, University College Cork, Cork, T12 R229, Ireland
- Division of Midwifery and Radiography, City University of London, London, EC1V OHB, United Kingdom
- Medical Imaging Department, Corfu General Hospital, Corfu 49100, Greece
| | | | - Haralabos Bougias
- Department of Clinical Radiology, Ioannina University Hospital, Ioannina 45110, Greece
| | | | - Stephanos Leandrou
- School of Science, European University Cyprus, Nicosia 1516, Cyprus
- School of Mathematical Sciences, Computer Science and Engineering, City University of London, London, EC1V 0HB, United Kingdom
| | - Periklis Papavasileiou
- Section of Radiography and Radiotherapy, Department of Biomedical Sciences, School of Health Sciences, University of West Attica, Athens 12243, Greece
| | - Irene Polycarpou
- Department of Health Sciences, European University Cyprus, Nicosia 1516, Cyprus
| | - Christina Malamateniou
- Division of Midwifery and Radiography, City University of London, London, EC1V OHB, United Kingdom
- School of Health Sciences (HESAV), University of Applied Sciences and Arts Western Switzerland (HES-SO), Lausanne 1007, Switzerland
| | - Mark F McEntee
- Discipline of Medical Imaging and Radiation Therapy, University College Cork, Cork, T12 R229, Ireland
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14
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Walsh G, Stogiannos N, van de Venter R, Rainey C, Tam W, McFadden S, McNulty JP, Mekis N, Lewis S, O'Regan T, Kumar A, Huisman M, Bisdas S, Kotter E, Pinto dos Santos D, Sá dos Reis C, van Ooijen P, Brady AP, Malamateniou C. Responsible AI practice and AI education are central to AI implementation: a rapid review for all medical imaging professionals in Europe. BJR Open 2023; 5:20230033. [PMID: 37953871 PMCID: PMC10636340 DOI: 10.1259/bjro.20230033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 05/27/2023] [Accepted: 05/30/2023] [Indexed: 11/14/2023] Open
Abstract
Artificial intelligence (AI) has transitioned from the lab to the bedside, and it is increasingly being used in healthcare. Radiology and Radiography are on the frontline of AI implementation, because of the use of big data for medical imaging and diagnosis for different patient groups. Safe and effective AI implementation requires that responsible and ethical practices are upheld by all key stakeholders, that there is harmonious collaboration between different professional groups, and customised educational provisions for all involved. This paper outlines key principles of ethical and responsible AI, highlights recent educational initiatives for clinical practitioners and discusses the synergies between all medical imaging professionals as they prepare for the digital future in Europe. Responsible and ethical AI is vital to enhance a culture of safety and trust for healthcare professionals and patients alike. Educational and training provisions for medical imaging professionals on AI is central to the understanding of basic AI principles and applications and there are many offerings currently in Europe. Education can facilitate the transparency of AI tools, but more formalised, university-led training is needed to ensure the academic scrutiny, appropriate pedagogy, multidisciplinarity and customisation to the learners' unique needs are being adhered to. As radiographers and radiologists work together and with other professionals to understand and harness the benefits of AI in medical imaging, it becomes clear that they are faced with the same challenges and that they have the same needs. The digital future belongs to multidisciplinary teams that work seamlessly together, learn together, manage risk collectively and collaborate for the benefit of the patients they serve.
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Affiliation(s)
- Gemma Walsh
- Division of Midwifery & Radiography, City University of London, London, United Kingdom
| | | | | | - Clare Rainey
- School of Health Sciences, Ulster University, Derry~Londonderry, Northern Ireland
| | - Winnie Tam
- Division of Midwifery & Radiography, City University of London, London, United Kingdom
| | - Sonyia McFadden
- School of Health Sciences, Ulster University, Coleraine, United Kingdom
| | | | - Nejc Mekis
- Medical Imaging and Radiotherapy Department, University of Ljubljana, Faculty of Health Sciences, Ljubljana, Slovenia
| | - Sarah Lewis
- Discipline of Medical Imaging Science, Sydney School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, Australia
| | - Tracy O'Regan
- The Society and College of Radiographers, London, United Kingdom
| | - Amrita Kumar
- Frimley Health NHS Foundation Trust, Frimley, United Kingdom
| | - Merel Huisman
- Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
| | | | | | | | - Cláudia Sá dos Reis
- School of Health Sciences (HESAV), University of Applied Sciences and Arts Western Switzerland (HES-SO), Lausanne, Switzerland
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15
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Kelly BS, Kirwan A, Quinn MS, Kelly AM, Mathur P, Lawlor A, Killeen RP. The ethical matrix as a method for involving people living with disease and the wider public (PPI) in near-term artificial intelligence research. Radiography (Lond) 2023; 29 Suppl 1:S103-S111. [PMID: 37062673 DOI: 10.1016/j.radi.2023.03.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 03/10/2023] [Accepted: 03/12/2023] [Indexed: 04/18/2023]
Abstract
INTRODUCTION The rapid pace of research in the field of Artificial Intelligence in medicine has associated risks for near-term AI. Ethical considerations of the use of AI in medicine remain a subject of much debate. Concurrently, the Involvement of People living with disease and the Public (PPI) in research is becoming mandatory in the EU and UK. The goal of this research was to elucidate the important values for our relevant stakeholders: People with MS, Radiologists, neurologists, Registered Healthcare Practitioners and Computer Scientists concerning AI in radiology and synthesize these in an ethical matrix. METHODS An ethical matrix workshop co-designed with a patient expert. The workshop yielded a survey which was disseminated to the professional societies of the relevant stakeholders. Quantitative data were analysed using the Pingouin 0.53 python package. Qualitative data were examined with word frequency analysis and analysed for themes with grounded theory with a patient expert. RESULTS 184 participants were recruited, (54, 60, 17, 12, 41 respectively). There were significant (p < 0.00001) differences in age, gender and ethnicity between groups. Key themes emerging from our results were the importance fast and accurate results, explanations over model performance and the significance of maintaining personal connections and choice. These themes were used to construct the ethical matrix. CONCLUSION The ethical matrix is a useful tool for PPI and stakeholder engagement with particular advantages for near-term AI in the pandemic era. IMPLICATIONS FOR PRACTICE We have produced an ethical matrix that allows for the inclusion of stakeholder opinion in medical AI research design.
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Affiliation(s)
- B S Kelly
- School of Medicine, UCD, Belfield, Dublin 4, Ireland; Department of Radiology, St Vincent's University Hospital, Dublin 4, Ireland; School of Computer Science and Insight Centre, UCD Belfield, Dublin 4, Ireland.
| | - A Kirwan
- Multiple Sclerosis Ireland National Office, 80 Northumberland Road, Dublin 4, Ireland
| | - M S Quinn
- School of Computer Science and Insight Centre, UCD Belfield, Dublin 4, Ireland
| | - A M Kelly
- School of Education, Trinity College Dublin, Dublin 2, Ireland
| | - P Mathur
- Department of Radiology, St Vincent's University Hospital, Dublin 4, Ireland
| | - A Lawlor
- Department of Radiology, St Vincent's University Hospital, Dublin 4, Ireland
| | - R P Killeen
- School of Medicine, UCD, Belfield, Dublin 4, Ireland
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Alsultan K. Awareness of Artificial Intelligence in Medical Imaging Among Radiologists and Radiologic Technologists. Cureus 2023; 15:e38325. [PMID: 37261164 PMCID: PMC10228162 DOI: 10.7759/cureus.38325] [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: 04/30/2023] [Indexed: 06/02/2023] Open
Abstract
BACKGROUND Current technological developments in medical imaging are primarily focused on increasing the integration of artificial intelligence (AI) into all medical imaging modalities. They are already considered capable of handling tasks such as image reconstruction, processing (denoising, segmentation), analysis, and predictive modeling. The purpose of this study is to assess the awareness (knowledge, attitudes, and practices) of radiologists and radiologic technologists regarding AI in medical imaging. MATERIALS AND METHODS This cross-sectional, qualitative study focuses on radiologists and radiologic technologists in Saudi Arabia, Sudan, and Yemen. A self-administered questionnaire based on published studies was used to collect primary data. Version 25.0 of IBM SPSS Statistics (IBM Corp., Armonk, NY) was used for the statistical analysis. The demographics were summarized as frequency and percentage. Independent samples t-tests and ANOVA tests were used to evaluate and compare the degree of AI awareness among the study groups. RESULTS A total of 210 individuals completed the survey. According to demographic information, there were 134 (63.8%) radiologic technologists and 76 radiologists (36.2%). Of the participants, 131 (62%) were male, while 79 (37.6%) were female. A total of 130 (61.9%) of the targeted respondents had a positive attitude, 105 (50%) had appropriate practice, and 122 (58.1%) of them were informed (knowledgeable) about AI in medical imaging. There was a significant difference in knowledge awareness between radiologists and radiologic technologists (p-value: <0.05). Radiologists were more knowledgeable than radiologic technologists, and females were more knowledgeable than males (p-value: 0.049). For attitude awareness, there were no significant differences regarding specialization, gender, age, academic qualification, and experience (p-value > 0.05). Regarding practice awareness, it turned out that females are more knowledgeable than males (p-value: 0.007). Additionally, it was discovered that significant differences indicated that bachelor's degree holders have a higher level of practice awareness than diploma holders (p-value: <0.05). CONCLUSION Significant differences between the respondent's knowledge awareness regarding specialization, gender, and experience are linked with relatively sufficient AI-basic knowledge and positive attitude awareness among radiologists and radiologic technologists. Only half of the study participants had appropriate practical awareness; therefore, additional training could enhance practical awareness.
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Affiliation(s)
- Kamal Alsultan
- Department of Diagnostic Radiology Technology, College of Applied Medical Sciences, Taibah University, Medina, SAU
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17
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Akudjedu TN, Torre S, Khine R, Katsifarakis D, Newman D, Malamateniou C. Knowledge, perceptions, and expectations of Artificial intelligence in radiography practice: A global radiography workforce survey. J Med Imaging Radiat Sci 2023; 54:104-116. [PMID: 36535859 DOI: 10.1016/j.jmir.2022.11.016] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 10/19/2022] [Accepted: 11/29/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND Artificial Intelligence (AI) technologies have already started impacting clinical practice across various settings worldwide, including the radiography profession. This study is aimed at exploring a world-wide view on AI technologies in relation to knowledge, perceptions, and expectations of radiography professionals. METHODS An online survey (hosted on Qualtrics) on key AI concepts was open to radiography professionals worldwide (August 1st to December 31st 2020). The survey sought both quantitative and qualitative data on topical issues relating to knowledge, perceptions, and expectations in relation to AI implementation in radiography practice. Data obtained was analysed using the Statistical Package for Social Sciences (SPSS) (v.26) and the six-phase thematic analysis approach. RESULTS A total of 314 valid responses were obtained with a fair geographical distribution. Of the respondents, 54.1% (157/290) were from North America and were predominantly clinical practicing radiographers (60.5%, 190/314). Our findings broadly relate to different perceived benefits and misgivings/shortcomings of AI implementation in radiography practice. The benefits relate to enhanced workflows and optimised workstreams while the misgivings/shortcomings revolve around de-skilling and impact on patient-centred care due to over-reliance on advanced technology following AI implementation. DISCUSSION Artificial intelligence is a tool but to operate optimally it requires human input and validation. Radiographers working at the interface between technology and the patient are key stakeholders in AI implementation. Lack of training and of transparency of AI tools create a mixed response of radiographers when they discuss their perceived benefits and challenges. It is also possible that their responses are nuanced by different regional and geographical contexts when it comes to AI deployment. Irrespective of geography, there is still a lot to be done about formalised AI training for radiographers worldwide. This is a vital step to ensure safe and effective AI implementation, adoption, and faster integration into clinical practice by healthcare workers including radiographers. CONCLUSION Advancement of AI technologies and implementation should be accompanied by proportional training of end-users in radiography and beyond. There are many benefits of AI-enabled radiography workflows and improvement on efficiencies but equally there will be widespread disruption of traditional roles and patient-centred care, which can be managed by a well-educated and well-informed workforce.
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Affiliation(s)
- Theophilus N Akudjedu
- Department of Medical Science and Public Health, Faculty of Health and Social Sciences, Institute of Medical Imaging and Visualisation, Bournemouth University, Bournemouth, Dorset, UK.
| | - Sofia Torre
- Department of Radiography, School of Health Sciences, City, University of London, Northampton Square, London, UK
| | - Ricardo Khine
- School of Health and Care Professions, Buckinghamshire New University, UK
| | | | - Donna Newman
- International Society of Radiographers and Radiological Technologists, UK
| | - Christina Malamateniou
- Department of Radiography, School of Health Sciences, City, University of London, Northampton Square, London, UK
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18
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Stogiannos N, Bougias H, Georgiadou E, Leandrou S, Papavasileiou P. Analysis of radiomic features derived from post-contrast T1-weighted images and apparent diffusion coefficient (ADC) maps for breast lesion evaluation: A retrospective study. Radiography (Lond) 2023; 29:355-361. [PMID: 36758380 DOI: 10.1016/j.radi.2023.01.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 01/17/2023] [Accepted: 01/25/2023] [Indexed: 02/10/2023]
Abstract
INTRODUCTION Breast cancer is the most common malignancy among women, and its diagnosis relies on medical imaging and the invasive, uncomforted biopsy. Recent advances in quantitative imaging and specifically the application of radiomics has proved to be a very promising technique, facilitating both diagnosis and therapy. The purpose of this study is to assess radiomic features derived from post-contrast T1w Magnetic Resonance Imaging (MRI) sequences and Apparent Diffusion Coefficient (ADC) maps for the evaluation of breast pathologies. METHODS MRI data from 52 women were retrospectively reviewed, involving 54 breast lesions, both malignant and benign. Diffusion Weighted Imaging (DWI) was applied as a standard MRΙ protocol, including dynamic contrast-enhanced (DCE) MRΙ in all cases. All patients were examined on a 1.5T MRI scanner, and 216 features were initially extracted from DCE-MRI images. Histological analysis of the breast lesions was performed, and a comparative analysis of the results was carried out to assess the accuracy of the method. RESULTS Following surgery and histological analysis, 30 lesions were found to be malignant and 24 benign. Implementation of a Machine Learning (ML) classification algorithm with 5-fold cross-validation resulted in a sensitivity of 70%, specificity of 66%, Negative Predictive Value of 82% and overall accuracy of 67% in differentiating malignancy from benevolence. CONCLUSION Texture analysis and ML methodology based on the first post-contrast dynamic sequences and ADC maps may be employed to differentiate between malignant and benign breast lesions, offering a promising new tool for diagnostic analysis. IMPLICATIONS FOR PRACTICE The results of this study will enhance knowledge around application and performance of radiomics in breast MRI, thus helping MRI radiographers who use AI-enabled technologies to better delineate the pros and cons of these procedures.
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Affiliation(s)
- N Stogiannos
- Discipline of Medical Imaging & Radiation Therapy, University College Cork, Ireland; Division of Midwifery & Radiography, City, University of London, UK; Medical Imaging Department, Corfu General Hospital, Greece, Felix Lames 6A, 1st Parodos, Corfu, Greece.
| | - H Bougias
- Department of Clinical Radiology, Ioannina University Hospital, Ioannina, Greece.
| | | | - S Leandrou
- School of Science, European University Cyprus, Nicosia, Cyprus; School of Mathematical Sciences, Computer Science and Engineering, City, University of London, UK.
| | - P Papavasileiou
- Section of Radiography and Radiotherapy, Dept of Biomedical Sciences, School of Health Sciences, University of West Attica, Athens, Greece.
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Artificial intelligence education for radiographers, an evaluation of a UK postgraduate educational intervention using participatory action research: a pilot study. Insights Imaging 2023; 14:25. [PMID: 36735172 PMCID: PMC9897152 DOI: 10.1186/s13244-023-01372-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 01/15/2023] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI)-enabled applications are increasingly being used in providing healthcare services, such as medical imaging support. Sufficient and appropriate education for medical imaging professionals is required for successful AI adoption. Although, currently, there are AI training programmes for radiologists, formal AI education for radiographers is lacking. Therefore, this study aimed to evaluate and discuss a postgraduate-level module on AI developed in the UK for radiographers. METHODOLOGY A participatory action research methodology was applied, with participants recruited from the first cohort of students enrolled in this module and faculty members. Data were collected using online, semi-structured, individual interviews and focus group discussions. Textual data were processed using data-driven thematic analysis. RESULTS Seven students and six faculty members participated in this evaluation. Results can be summarised in the following four themes: a. participants' professional and educational backgrounds influenced their experiences, b. participants found the learning experience meaningful concerning module design, organisation, and pedagogical approaches, c. some module design and delivery aspects were identified as barriers to learning, and d. participants suggested how the ideal AI course could look like based on their experiences. CONCLUSIONS The findings of our work show that an AI module can assist educators/academics in developing similar AI education provisions for radiographers and other medical imaging and radiation sciences professionals. A blended learning delivery format, combined with customisable and contextualised content, using an interprofessional faculty approach is recommended for future similar courses.
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Leading radiography research to deliver clinical outcomes. J Med Imaging Radiat Sci 2022; 53:S35-S37. [PMID: 36123291 DOI: 10.1016/j.jmir.2022.08.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 08/17/2022] [Accepted: 08/23/2022] [Indexed: 12/24/2022]
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Singapore radiographers' perceptions and expectations of artificial intelligence - A qualitative study. J Med Imaging Radiat Sci 2022; 53:554-563. [PMID: 36115823 DOI: 10.1016/j.jmir.2022.08.005] [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: 05/23/2022] [Revised: 08/03/2022] [Accepted: 08/11/2022] [Indexed: 12/24/2022]
Abstract
INTRODUCTION With the emergence of artificial intelligence (AI) in medical imaging, radiographers are likely to be at the forefront of this technological advancement. Studies have therefore been conducted recently to understand radiographers' opinions on AI adoption. This study extends that work by using a qualitative approach to further explore radiographers' knowledge, perceptions, and expectations of AI. METHOD Six online focus groups were conducted with 22 radiographers from the three public healthcare clusters in Singapore. They were purposively sampled, and participants were recruited from a broad demographic background with varying years of working experience and designations. The focus group sessions were transcribed verbatim and thematic analysis was performed on their responses. RESULTS Participants demonstrated limited knowledge of AI. Their perceptions of AI were mixed, recognising its benefits in increasing efficiency and improving patient care, but also aware of its limitations in accuracy and bias. On how patients may perceive AI, participants felt that patients would accept AI if they felt it improves their care but may reject it once they lose trust in it. Expectations wise, participants envisioned several applications in pre-, peri‑, and post-procedural workflows including order vetting, patient positioning, language translation, and artefact removal. On radiographers' role and career opportunities, some participants see an opportunity for radiographers to specialise in AI, becoming involved in algorithm development and its clinical implementation. DISCUSSION Our findings suggest that widespread implementation of AI would require limited knowledge amongst radiographers and current AI limitations to be addressed. While radiographers are positively anticipating the integration of AI into their practices, they should also become actively involved in the development of AI tools such that those they envisioned. This would help align optimal use of AI tools and radiographer role changes. Patients' acceptance and reactions to AI also warrant further research.
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Hizzett K, Snaith B. Career intentions, their influences and motivational factors in diagnostic radiography: A survey of undergraduate students. Radiography (Lond) 2021; 28:162-167. [PMID: 34642119 DOI: 10.1016/j.radi.2021.09.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 08/30/2021] [Accepted: 09/26/2021] [Indexed: 11/16/2022]
Abstract
INTRODUCTION The choice of career and speciality can be a complex process. It is unclear what influences career decisions within undergraduate radiographers and whether the curriculum provides sufficient support. METHODS An online cross-sectional survey was undertaken. All undergraduate diagnostic radiography students within one UK higher education institution were invited to participate. Questions were related to anticipated career choice, motivational factors and influences. RESULTS The response rate was 67.6% (90/133). The most popular specialist area for career preference was general radiography. The top three motivational factors were work life balance, career development and job satisfaction. The most influential factors on career choice were clinical placements, radiographers on placements and formal teaching. Career guidance was predominantly sourced from the personal academic tutor and was informed by role models. CONCLUSION Radiography academic teams and clinical placement providers must work together to ensure that students have access to high quality placements across specialities. The experiences received during undergraduate training are important in specialty choice. IMPLICATIONS FOR PRACTICE Career guidance is essential and should begin as early as possible. Clinical and academic radiographers need to appreciate the influence they have on students future career plans.
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Affiliation(s)
- K Hizzett
- Faculty of Health Studies, University of Bradford, Richmond Road, Bradford, BD7 1DP, UK.
| | - B Snaith
- Faculty of Health Studies, University of Bradford, Richmond Road, Bradford, BD7 1DP, UK; Radiology, Mid Yorkshire Hospitals NHS Trust, Aberford Road, Wakefield, WF1 4DG, UK
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