Artificial intelligence: radiologists’ expectations and opinions gleaned from a nationwide online survey

Coppola, Francesca; Faggioni, Lorenzo; Regge, Daniele; Giovagnoni, Andrea; Golfieri, Rita; Bibbolino, Corrado; Miele, Vittorio; Neri, Emanuele; Grassi, Roberto

doi:10.1007/s11547-020-01205-y

Cited by 103 publications

(89 citation statements)

References 30 publications

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“…There were similarities between the attitudes expressed by the participants in this study and those described by Dos Santos et al, where 83% of the respondents disagreed with the statement that AI would replace human radiologists [ 23 ]. Another recently published study found that radiologists have a positive attitude toward the implementation of AI and are not concerned that AI will replace them [ 25 ]. At the same time, AI may replace human medical expertise in specific and repetitive tasks, such as detecting disease indicators in images [ 26 ].…”

Section: Discussionmentioning

confidence: 99%

Radiology Community Attitude in Saudi Arabia about the Applications of Artificial Intelligence in Radiology

et al. 2021

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Artificial intelligence (AI) is a broad, umbrella term that encompasses the theory and development of computer systems able to perform tasks normally requiring human intelligence. The aim of this study is to assess the radiology community’s attitude in Saudi Arabia toward the applications of AI. Methods: Data for this study were collected using electronic questionnaires in 2019 and 2020. The study included a total of 714 participants. Data analysis was performed using SPSS Statistics (version 25). Results: The majority of the participants (61.2%) had read or heard about the role of AI in radiology. We also found that radiologists had statistically different responses and tended to read more about AI compared to all other specialists. In addition, 82% of the participants thought that AI must be included in the curriculum of medical and allied health colleges, and 86% of the participants agreed that AI would be essential in the future. Even though human–machine interaction was considered to be one of the most important skills in the future, 89% of the participants thought that it would never replace radiologists. Conclusion: Because AI plays a vital role in radiology, it is important to ensure that radiologists and radiographers have at least a minimum understanding of the technology. Our finding shows an acceptable level of knowledge regarding AI technology and that AI applications should be included in the curriculum of the medical and health sciences colleges.

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Section: Discussionmentioning

confidence: 99%

Radiology Community Attitude in Saudi Arabia about the Applications of Artificial Intelligence in Radiology

et al. 2021

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“…This analysis can use multiple mathematical models that are aimed to provide quantitative parameters within a selected image, which are called texture features. TA is performed employing a computer quantification of both the gray-level intensity and position of the pixels, and its use is being investigated in several fields [ 10 , 11 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ]. More recently, a different approach to TA was developed, taking into consideration the variations in TA parameters at different acquisition times.…”

Section: Introductionmentioning

confidence: 99%

Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment

Nardone

Boldrini

Grassi

et al. 2021

Cancers

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Introduction: Neoadjuvant radiotherapy is currently used mainly in locally advanced rectal cancer and sarcoma and in a subset of non-small cell lung cancer and esophageal cancer, whereas in other diseases it is under investigation. The evaluation of the efficacy of the induction strategy is made possible by performing imaging investigations before and after the neoadjuvant therapy and is usually challenging. In the last decade, texture analysis (TA) has been developed to help the radiologist to quantify and identify the parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye. The aim of this narrative is to review the impact of TA on the prediction of response to neoadjuvant radiotherapy and or chemoradiotherapy. Materials and Methods: Key references were derived from a PubMed query. Hand searching and ClinicalTrials.gov were also used. Results: This paper contains a narrative report and a critical discussion of radiomics approaches in different fields of neoadjuvant radiotherapy, including esophageal cancer, lung cancer, sarcoma, and rectal cancer. Conclusions: Radiomics can shed a light on the setting of neoadjuvant therapies that can be used to tailor subsequent approaches or even to avoid surgery in the future. At the same, these results need to be validated in prospective and multicenter trials.

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“…Fully automated segmentation methods have the potential of overcoming the aforementioned limitations; however, the intrinsic complexity of rectal MR images, together with differences in signal intensity among different patients and different image acquisition protocols and MR scanners, can form substantial hurdles, which have long prevented developing radiomics as a tool for supporting the diagnosis. Newer deep learning (DL) systems can outperform conventional pattern recognition algorithms in segmenting RC lesions from clinical MR images [ 39 , 40 , 41 , 42 , 43 ].…”

Section: Radiomics Workflowmentioning

confidence: 99%

Radiomics and Magnetic Resonance Imaging of Rectal Cancer: From Engineering to Clinical Practice

et al. 2021

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While cross-sectional imaging has seen continuous progress and plays an undiscussed pivotal role in the diagnostic management and treatment planning of patients with rectal cancer, a largely unmet need remains for improved staging accuracy, assessment of treatment response and prediction of individual patient outcome. Moreover, the increasing availability of target therapies has called for developing reliable diagnostic tools for identifying potential responders and optimizing overall treatment strategy on a personalized basis. Radiomics has emerged as a promising, still fully evolving research topic, which could harness the power of modern computer technology to generate quantitative information from imaging datasets based on advanced data-driven biomathematical models, potentially providing an added value to conventional imaging for improved patient management. The present study aimed to illustrate the contribution that current radiomics methods applied to magnetic resonance imaging can offer to managing patients with rectal cancer.

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Artificial intelligence: radiologists’ expectations and opinions gleaned from a nationwide online survey

Cited by 103 publications

References 30 publications

Radiology Community Attitude in Saudi Arabia about the Applications of Artificial Intelligence in Radiology

Radiology Community Attitude in Saudi Arabia about the Applications of Artificial Intelligence in Radiology

Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment

Radiomics and Magnetic Resonance Imaging of Rectal Cancer: From Engineering to Clinical Practice

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