Artificial intelligence in radiology: who’s afraid of the big bad wolf?

Gallix, B.; Chong, Jaron

doi:10.1007/s00330-018-5995-9

Cited by 36 publications

(22 citation statements)

References 8 publications

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“…The introduction of this paper indicated possible applications of AI in radiology, which has caused some disquiet among some radiographers. [17]. Across the respondents, a wide variation of educational background represented the 'generational' range from Diploma to Doctoral levels.…”

Section: Discussionmentioning

confidence: 99%

An extensive survey of radiographers from the Middle East and India on artificial intelligence integration in radiology practice

et al. 2021

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Assessing the current Artificial intelligence (AI) situation is a crucial step towards its implementation into radiology practice. The study aimed to assess radiographer willingness to accept AI in radiology work practice and the impact of AI in work performance. An exploratory cross-sectional online survey conducted for radiographers working within the Middle East and India was conducted from May-August 2020. A previously validated survey used to obtain radiographer's demographics, knowledge, perceptions, organization readiness, and challenges of integrating AI into radiology. The survey was accessible for radiographers and distributed through the societies page. The survey was completed by 549 radiographers distributed as (77.6%, n = 426) from the Middle East while (22.4%, n = 123) from India. A majority (86%, n = 773) agreed that AI currently plays an important role in radiology and (88.0%, n = 483) expected that AI would play a role in radiology practice and image production. The challenges for AI implementation in practice were developing AI skills (42.8%, n = 235) and AI knowledge development (37.0%, n = 203). Participants showed high interest to integrate AI in under and postgraduate curriculum. There is excitement about what AI could offer, but education input is a requirement. Fears are expressed about job security and how radiology may work across all ages and educational backgrounds. Radiographers become aware of AI role and challenges, which can be improved by education and training.

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Section: Discussionmentioning

confidence: 99%

An extensive survey of radiographers from the Middle East and India on artificial intelligence integration in radiology practice

et al. 2021

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“…Participants emphasized that the basic working principles of AI should be taught in dentistry, as indicated in other studies in the literature. 35,45 Participants also noted that their information was provided more by social media than by academics. However, most educated students were also the least afraid of this new technology.…”

Section: Discussionmentioning

confidence: 99%

Attitudes and perceptions of dental students towards artificial intelligence

Yüzbaşıoğlu

2020

Journal of Dental Education

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IntroductionArtificial Intelligence (AI) is a burning topic and use of AI in our day‐to‐day life has increased exponentially. The purpose of this study was to evaluate the attitudes and perceptions of Turkish dental students towards AI and to provide information about their opinions to use of AI in dentistry.MethodsA 22‐question survey was distributed through Google Forms to all dental students at 9 Turkish dental schools in March 2019. It consisted of various sections aiming to evaluate the dental students’ knowledge and attitudes toward AI and possible applications in dentistry. Respondents’ anonymity was ensured.ResultsA total of 1103 students (650 female, 453 male; mean age was 21.36 ± 1.93 years) responded to the questionnaire for a 21.69% response rate. Of these, about 48.40% had basic knowledge about AI technologies and 10.6% stated that they had no information sources about AI. 85.70% agreed that dentistry would be revolutionized by AI but 28.60% did not agree that AI could replace themselves in the near future. In addition, 74.60% and 79.80% of agreed on topics about AI to be included in undergraduate and postgraduate dental education, respectively.ConclusionsAlthough participants have insufficient knowledge of AI, they are willing to improve their knowledge in this field. Participants shared an optimistic views and thought that AI will have a positive impact on dental future practice. Follow‐up surveys and multinational studies should be conducted to further explore these issues.

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“…Different diagnostic or monitoring devices such as wearable health monitoring systems [243, 244], liquid biopsy or associated approaches [245, 246], wireless ultrasound transducer [247], or other point-of-care testing (POCT) methods [248, 249] are introduced and need proper analysis and validation. Furthermore, the availability of big data opens a new pathway in analyzing medical data, and artificial intelligence approaches will probably change the way of imaging diagnostic and monitoring [250, 251]. The ethical aspects must be considered [252, 253] along with valid and reliable methods for the assessment of old and new diagnostic approaches that are required.…”

Section: Limitations and Perspectivesmentioning

confidence: 99%

Medical Diagnostic Tests: A Review of Test Anatomy, Phases, and Statistical Treatment of Data

Bolboacă

2019

Computational and Mathematical Methods in Medicine

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Diagnostic tests are approaches used in clinical practice to identify with high accuracy the disease of a particular patient and thus to provide early and proper treatment. Reporting high-quality results of diagnostic tests, for both basic and advanced methods, is solely the responsibility of the authors. Despite the existence of recommendation and standards regarding the content or format of statistical aspects, the quality of what and how the statistic is reported when a diagnostic test is assessed varied from excellent to very poor. This article briefly reviews the steps in the evaluation of a diagnostic test from the anatomy, to the role in clinical practice, and to the statistical methods used to show their performances. The statistical approaches are linked with the phase, clinical question, and objective and are accompanied by examples. More details are provided for phase I and II studies while the statistical treatment of phase III and IV is just briefly presented. Several free online resources useful in the calculation of some statistics are also given.

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Artificial intelligence in radiology: who’s afraid of the big bad wolf?

Cited by 36 publications

References 8 publications

An extensive survey of radiographers from the Middle East and India on artificial intelligence integration in radiology practice

An extensive survey of radiographers from the Middle East and India on artificial intelligence integration in radiology practice

Attitudes and perceptions of dental students towards artificial intelligence

Medical Diagnostic Tests: A Review of Test Anatomy, Phases, and Statistical Treatment of Data

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