Assessment of MRI technologists in acceptance and willingness to integrate artificial intelligence into practice

Abuzaid, Mohamed M.; Tekın, H.O.; Reza, Md. Mahfuz; Elhag, I.R.; Elshami, Wiam

doi:10.1016/j.radi.2021.07.007

Cited by 25 publications

(46 citation statements)

References 10 publications

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“…After this quick first screening, the eligibility process led to the choice of 15 papers [ 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ].…”

Section: Resultsmentioning

confidence: 99%

“…that the selected papers showed that the tools were essentially based on questionnaires [ 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ];…”

Section: Resultsmentioning

confidence: 99%

“…The selected papers [ 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ] showed that the studies focused on some of the actors in this area: radiologists, radiographers, primary care providers, developers, students, and patients, that is, on both service providers and users, and on the subjects in training.…”

Section: Resultsmentioning

confidence: 99%

“…After this quick first screening, the eligibility process led to the choice of 15 papers [31][32][33][34][35][36][37][38][39][40][41][42][43][44].…”

Section: The Tools Used To Assess the Acceptancementioning

confidence: 99%

“…The search highlighted: (a) that the selected papers showed that the tools were essentially based on questionnaires [31][32][33][34][35][36][37][38][39][40][41][42][43][44]; (b) that the interest into this theme is recent, since publications of studies have started very recently, as the first ones are from 2019, and this reinforces the need for this study.…”

Section: General Considerations On the Tools For The Acceptancementioning

confidence: 99%

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The Artificial Intelligence in Digital Radiology: Part 1: The Challenges, Acceptance and Consensus

Giansanti

Basilio

2022

Healthcare

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Artificial intelligence is having important developments in the world of digital radiology also thanks to the boost given to the research sector by the COVID-19 pandemic. In the last two years, there was an important development of studies focused on both challenges and acceptance and consensus in the field of Artificial Intelligence. The challenges and acceptance and consensus are two strategic aspects in the development and integration of technologies in the health domain. The study conducted two narrative reviews by means of two parallel points of view to take stock both on the ongoing challenges and on initiatives conducted to face the acceptance and consensus in this area. The methodology of the review was based on: (I) search of PubMed and Scopus and (II) an eligibility assessment, using parameters with 5 levels of score. The results have: (a) highlighted and categorized the important challenges in place. (b) Illustrated the different types of studies conducted through original questionnaires. The study suggests for future research based on questionnaires a better calibration and inclusion of the challenges in place together with validation and administration paths at an international level.

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“…After this quick first screening, the eligibility process led to the choice of 15 papers [ 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ].…”

Section: Resultsmentioning

confidence: 99%

“…that the selected papers showed that the tools were essentially based on questionnaires [ 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ];…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“…After this quick first screening, the eligibility process led to the choice of 15 papers [31][32][33][34][35][36][37][38][39][40][41][42][43][44].…”

Section: The Tools Used To Assess the Acceptancementioning

confidence: 99%

Section: General Considerations On the Tools For The Acceptancementioning

confidence: 99%

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The Artificial Intelligence in Digital Radiology: Part 1: The Challenges, Acceptance and Consensus

Giansanti

Basilio

2022

Healthcare

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Australian perspectives on artificial intelligence in medical imaging

Currie

Nelson

Hewis

et al. 2022

J of Medical Radiation Sci

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Introduction While artificial intelligence (AI) and recent developments in deep learning (DL) have sparked interest in medical imaging, there has been little commentary on the impact of AI on imaging technologists. The aim of this survey was to understand the attitudes, applications and concerns among nuclear medicine and radiography professionals in Australia with regard to the rapidly emerging applications of AI. Methods An anonymous online survey with invitation to participate was circulated to nuclear medicine and radiography members of the Rural Alliance in Nuclear Scintigraphy and the Australian Society of Medical Imaging and Radiation Therapy. The survey invitations were sent to members via email and as a push via social media with the survey open for 10 weeks. All information collected was anonymised and there is no disclosure of personal information as it was de‐identified from commencement. Results Among the 102 respondents, there was a high level of acceptance of lower order tasks (e.g. patient registration, triaging and dispensing) and less acceptance of high order task automation (e.g. surgery and interpretation). There was a low priority perception for the role of AI in higher order tasks (e.g. diagnosis, interpretation and decision making) and high priority for those applications that automate complex tasks (e.g. quantitation, segmentation, reconstruction) or improve image quality (e.g. dose / noise reduction and pseudo CT for attenuation correction). Medico‐legal, ethical, diversity and privacy issues posed moderate or high concern while there appeared to be no concern regarding AI being clinically useful and improving efficiency. Mild concerns included redundancy, training bias, transparency and validity. Conclusion Australian nuclear medicine technologists and radiographers recognise important applications of AI for assisting with repetitive tasks, performing less complex tasks and enhancing the quality of outputs in medical imaging. There are concerns relating to ethical aspects of algorithm development and implementation.

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Artificial Intelligence in Diagnostic Medical Image Processing for Advanced Healthcare Applications

Kalita,

Boruah,

Das

et al. 2024

Biological and Medical Physics, Biomedical Engineering

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Assessment of MRI technologists in acceptance and willingness to integrate artificial intelligence into practice

Cited by 25 publications

References 10 publications

The Artificial Intelligence in Digital Radiology: Part 1: The Challenges, Acceptance and Consensus

The Artificial Intelligence in Digital Radiology: Part 1: The Challenges, Acceptance and Consensus

Australian perspectives on artificial intelligence in medical imaging

Artificial Intelligence in Diagnostic Medical Image Processing for Advanced Healthcare Applications

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