Artificial Intelligence and Radiology: A Social Media Perspective

ObjectiveTo use Twitter to characterize public perspectives regarding artificial intelligence (AI) and radiology.Methods and materialsTwitter was searched for all tweets containing the terms “artificial intelligence” and “radiology” from November 2016 to October 2017. Users posting the tweets, tweet content, and linked websites were categorized.ResultsSix hundred and five tweets were identified. These were from 407 unique users (most commonly industry-related individuals [22.6%]; radiologists only 9.3%) and linked to 216 unique websites. 42.5% of users were from the United States. The tweets mentioned machine/deep learning in 17.2%, industry in 14.0%, a medical society/conference in 13.4%, and a university in 9.8%. 6.3% mentioned a specific clinical application, most commonly oncology and lung/tuberculosis. 24.6% of tweets had a favorable stance regarding the impact of AI on radiology, 75.4% neutral, and none were unfavorable. 88.0% of linked websites leaned toward AI being positive for the field of radiology; none leaned toward AI being negative for the field. 51.9% of linked websites specifically mentioned improved efficiency for radiology with AI. 35.2% of websites described challenges for implementing AI in radiology. Of the 47.2% of websites that mentioned the issue of AI replacing radiologists, 77.5% leaned against AI replacing radiologists, 13.7% had a neutral view, and 8.8% leaned toward AI replacing radiologists.ConclusionThese observations provide an overview of the social media discussions regarding AI in radiology. While noting challenges, the discussions were overwhelmingly positive toward the transformative impact of AI on radiology and leaned against AI replacing radiologists. Greater radiologist engagement in this online social media dialog is encouraged.     

Knowledge and attitudes towards artificial intelligence in imaging: a look at the quantitative survey literature

Rationale and objectivesThere exists many single sample perspectives on artificial intelligence (AI). The aim of this review was to collate the current data on attitudes/knowledge towards AI in three unique populations: medical students, clinicians and patients.Materials and methodsA literature search was performed on PubMed, Scopus and Web of Science pertaining to survey data on AI in radiology. Quality assessment was performed by an adapted version of the assessment tool from the National Heart, Lung and Blood Institute for Observational Studies.ResultsFourteen studies were found on attitudes/knowledge towards AI in radiology. Four studies examined medical students, seven on clinicians and three on patient populations. Deficiencies in the literature mainly related to sampling bias. Students had anxiety relating to future job prospects. Clinicians were optimistic and viewed AI as an aid to the diagnosis and wanted to further their knowledge. Patients were concerned about the lack of human interaction and accountability during error.ConclusionAttitudes and knowledge regarding AI in radiology remains a topic that needs to be researched further and education given pertaining to its use in a clinical setting.     

Hidden Curriculum and the Demographic Stoicism That Keeps Women and Minorities Away From Radiology: A Mixed-Methods Study of Medical Students

ObjectiveTo understand how women and historically underrepresented minority medical students perceive radiology as a potential career choice.MethodsMedical students representing a broad spectrum of radiology exposure from a single institution were invited to participate in a mixed-methods study. Participants completed a 16-item survey about demographics and perceptions of radiology. Ten focus groups were administered to probe decision making regarding career selection. The themes influencing women and historically underrepresented minority students are presented.ResultsForty-nine medical students, including 29 (59%) women and 17 (35%) underrepresented minorities, participated. Most participants (28 of 48, 58%) reported men outnumbered women in radiology. Female participants reported a lack of mentorship and role models as major concerns. Outreach efforts focused on the family-friendly nature of radiology were viewed as patronizing. Demographic improvements in the field were viewed as very slow. Forty-six percent (22 of 48) of participants indicated that radiology had a less underrepresented racial or ethnic workforce than other medical specialties. Minority participants especially noted a lack of radiology presence in mainstream media, so students have few preconceived biases. A failure to organically connect with the mostly White male radiologists because of a lack of shared background was a major barrier. Finally, participants described a hidden curriculum that pushes minority medical students away from specialty fields like radiology and toward primary care fields to address underserved communities and health care disparities.DiscussionWomen and historically underrepresented minority medical students perceive major barriers to choosing a career in radiology. Radiology departments must develop sophisticated multilevel approaches to improve diversity.     

Medical Student Perspectives on the Impact of Artificial Intelligence on the Practice of Medicine

IntroductionConcerns about radiologists being replaced by artificial intelligence (AI) from the lay media could have a negative impact on medical students’ perceptions of radiology as a viable specialty. The purpose of this study was to evaluate United States of America medical students’ perceptions about radiology and other medical specialties in relation to AI.MethodsAn anonymous, web-based survey was sent to 32 radiology interest groups at United States medical schools. The survey was comprised of 6 questions assessing medical student perceptions of AI and its potential impact on radiology and other medical specialties. Responses were voluntary and collected over a 6-month period from November 2017 to April 2018.ResultsA total of 156 students responded with representation from each year of medical school. Over 75% agreed that AI would have a significant role in the future of medicine. Most (66%) agreed that diagnostic radiology would be the specialty most greatly affected. Nearly half (44%) reported that AI made them less enthusiastic about radiology. The majority of students (57%) obtained their information about AI from online articles. Thematic analysis of free answer comments revealed mostly neutral comments towards AI, however, the negative responses were the strongest and most detailed.ConclusionsUS medical students believe that AI will play a significant role in medicine, particularly in radiology. However, nearly half are less enthusiastic about the field of radiology due to AI. As the majority receive information about AI from online articles, which may have negative sentiments towards AI's impact on radiology, formal AI education and medical student outreach may help combat misinformation and help prevent the dissuading of medical students who might otherwise consider the specialty.     

Knowledge,behavior and attitudes of dental practitioners towards photodynamic therapy use in dental practice

BackgroundThe aim of the present study was to assess knowledge, behavior and attitudes of dental practitioners (DPs) towards photodynamic therapy (PDT) in dental clinical practice.MethodsA cross-sectional study was performed and a 13-item survey questionnaire was given to DPs practicing in 13 different teaching hospitals in Karachi, Pakistan. Questions were aimed at exploring the knowledge of DPs regarding PDT and their attitude towards PDT and perceptions that may influence clinical practices. Chi-square and spearman coefficient were conducted to compare subgroups and correlate factors with the knowledge score of DPs.ResultsA total of 509 questionnaires were completed (response rate = 82%). Median age of participants was 34 years and 70% were females. Most DPs demonstrated good knowledge related to PDT, and nearly 77%, 69% and 62% were aware of the mechanism of action and the role of photosensitizers in PDT respectively. It was reported that 74% of the respondents expressed that they are comfortable to know about PDT in detail for their clinical practice. A cumulative 54% disagreed that discussing the option for PDT with their patients was peripheral to their role as clinicians. A striking 82% would like to attend seminars and workshops on PDT. Significant difference was found among senior lecturers and assistant professors for the knowledge items (p < 0.05). No statistical correlation was found between the knowledge items score of DPs and their behavior (r = 0.18; p = 0.762), attitude (r = 0.04; p = 0.594) and self-rated knowledge (r = 0.42; p = 0.854).ConclusionDental practitioners showed adequate knowledge regarding PDT and its use in dentistry. However, expertise with regards to handling and training is warranted so that DPs could use PDT in their dental practice.     

Development of lumbar spine MRI referrals vetting models using machine learning and deep learning algorithms: Comparison models vs healthcare professionals

IntroductionReferrals vetting is a necessary daily task to ensure the appropriateness of radiology referrals. Vetting requires extensive clinical knowledge and may challenge those responsible. This study aims to develop AI models to automate the vetting process and to compare their performance with healthcare professionals.Methods1020 lumbar spine MRI referrals were collected retrospectively from two Irish hospitals. Three expert MRI radiographers classified the referrals into indicated or not indicated for scanning based on iRefer guidelines. The reference label for each referral was assigned based on the majority voting. The corpus was divided into two datasets, one for the models' development with 920 referrals, and one included 100 referrals used as a held-out for the final comparison of the AI models versus national and international MRI radiographers. Three traditional models were developed: SVM, LR, RF, and two deep neural models, including CNN and Bi-LSTM. For the traditional models, four vectorisation techniques applied: BoW, bigrams, trigrams, and TF-IDF. A textual data augmentation technique was applied to investigate the influence of data augmentation on the models' performances.ResultsRF with BoW achieved the highest AUC reaching 0.99. CNN model outperformed Bi-LSTM with AUC = 0.98. With the augmented dataset, the performance significantly improved with an increase in F1 scores ranging from 1% to 7%. All models outperformed the national and international radiographers when compared on the hold-out dataset.ConclusionThe models assigned the referrals' appropriateness with higher accuracies than the national and international radiographers. Applying data augmentation significantly improved the models' performances.Implications for practiceThe outcomes suggest that the use of AI for checking referrals' eligibility could serve as a supporting tool to improve the referrals' management in radiology departments.     

Effect of a communication lecture tutorial on breast imaging trainees' confidence with challenging breast imaging patient interactions

PurposeThe aim of the study was to evaluate the effect of a one-hour lecture based communication curriculum on breast imaging trainees' confidence in communicating with patients in a challenging communication setting such as delivering bad news or radiologic error disclosure.Methods12 breast imaging trainees from an academic fellowship program completed questionnaires before and after a communication tutorial. A four breast imaging specific scenario questionnaire assessed confidence by asking the trainees to rank agreement with statements related to their attitude in those specific settings. 12-month follow-up questionnaire was sent to the graduating fellows assessing their -overall confidence in patient communication, the contribution of the curriculum to their self-perceived communication skill and their likelihood in disclosing a radiologic error to a patient.ResultsAll trainees completed the pre and post lecture questionnaire. After the communication tutorial, all trainees reported increased confidence in communicating with patients in a variety of challenging settings with pre lecture survey mean confidence score of 38/98 and post lecture survey mean score of 85.3/98, P = 0.003. Three of eight trainees who completed the 12-month follow up questionnaire reported confidence in their communication skills and reported that the tutorial significantly contributed to their communication skill development. All three agreed that they would be likely to disclose a medical error should they encounter it in their future career.ConclusionsA limited resource one-hour lecture communication tutorial provides effective communication training for breast imaging fellows and is a promising part of a breast imaging curriculum.     

Percepciones de estudiantes de Medicina sobre el impacto de la inteligencia artificial en radiología

ObjectivesTo analyze medical students’ perceptions of the impact of artificial intelligence in radiology.Material and methodsA structured questionnaire comprising 28 items organized into six sections was distributed to students of medicine in Spain in December 2019.ResultsA total of 341 students responded. Of these, 27 (7.9%) included radiology among their three main choices for specialization, and 51.9% considered that they clearly understood what artificial intelligence is. The overall rate of correct answers to the objective true-or-false questions about artificial intelligence was 70.7%. Whereas 75.9% expressed their disagreement with the hypothesis that artificial intelligence would replace radiologists, only 41.9% disagreed with the hypothesis that the demand for radiologists would decrease in the future. Only 36.7% expressed concerns about the role of artificial intelligence related to choosing radiology as a specialty. A greater proportion of students in the early years of medical school agreed with statements that radiologists accept artificial-intelligence-related technological changes and work with the industry to apply them as well as with statements about the need to include basic training about artificial intelligence in the medical school curriculum.ConclusionsThe students surveyed are aware of the impact of artificial intelligence in daily life, but not of the current debate about its potential applications in radiology. In general, they think that artificial intelligence will revolutionize radiology without having an alarming effect on the employability of radiologists. The students surveyed think that it is necessary to provide basic training about artificial intelligence in undergraduate medical school programs.     

Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs

Objective:This study evaluated the use of a deep-learning approach for automated detection and numbering of deciduous teeth in children as depicted on panoramic radiographs.Methods and materials:An artificial intelligence (AI) algorithm (CranioCatch, Eskisehir-Turkey) using Faster R-CNN Inception v2 (COCO) models were developed to automatically detect and number deciduous teeth as seen on pediatric panoramic radiographs. The algorithm was trained and tested on a total of 421 panoramic images. System performance was assessed using a confusion matrix.Results:The AI system was successful in detecting and numbering the deciduous teeth of children as depicted on panoramic radiographs. The sensitivity and precision rates were high. The estimated sensitivity, precision, and F1 score were 0.9804, 0.9571, and 0.9686, respectively.Conclusion:Deep-learning-based AI models are a promising tool for the automated charting of panoramic dental radiographs from children. In addition to serving as a time-saving measure and an aid to clinicians, AI plays a valuable role in forensic identification.     

Deep Learning-Assisted Diagnosis of Pediatric Skull Fractures on Plain Radiographs

ObjectiveTo develop and evaluate a deep learning-based artificial intelligence (AI) model for detecting skull fractures on plain radiographs in children.Materials and MethodsThis retrospective multi-center study consisted of a development dataset acquired from two hospitals (n = 149 and 264) and an external test set (n = 95) from a third hospital. Datasets included children with head trauma who underwent both skull radiography and cranial computed tomography (CT). The development dataset was split into training, tuning, and internal test sets in a ratio of 7:1:2. The reference standard for skull fracture was cranial CT. Two radiology residents, a pediatric radiologist, and two emergency physicians participated in a two-session observer study on an external test set with and without AI assistance. We obtained the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity along with their 95% confidence intervals (CIs).ResultsThe AI model showed an AUROC of 0.922 (95% CI, 0.842–0.969) in the internal test set and 0.870 (95% CI, 0.785–0.930) in the external test set. The model had a sensitivity of 81.1% (95% CI, 64.8%–92.0%) and specificity of 91.3% (95% CI, 79.2%–97.6%) for the internal test set and 78.9% (95% CI, 54.4%–93.9%) and 88.2% (95% CI, 78.7%–94.4%), respectively, for the external test set. With the model’s assistance, significant AUROC improvement was observed in radiology residents (pooled results) and emergency physicians (pooled results) with the difference from reading without AI assistance of 0.094 (95% CI, 0.020–0.168; p = 0.012) and 0.069 (95% CI, 0.002–0.136; p = 0.043), respectively, but not in the pediatric radiologist with the difference of 0.008 (95% CI, -0.074–0.090; p = 0.850).ConclusionA deep learning-based AI model improved the performance of inexperienced radiologists and emergency physicians in diagnosing pediatric skull fractures on plain radiographs.     

Prevention of dental trauma in a high-risk military population: the discrepancy between knowledge and willingness to comply

Military fighters are at high risk for oral/tooth injuries. Our aim was to evaluate the knowledge and willingness to use preventive measures among this population to reduce oral trauma. A total of 336 fighters were randomly assigned to two groups. The control group answered a structured questionnaire, which included questions regarding: knowledge of the benefits of mouthguard use, past/current use, and willingness to use a mouthguard. The intervention group received a 60-minute dental trauma lecture, and responded to the same questionnaire. Significantly more subjects in the intervention group were familiar with the benefits of mouthguards compared to the control group, but there was no difference between the groups in their willingness to use mouthguards routinely. Discomfort and potential interference to sport performance were the most common reasons for rejection. It seems that a structured lecture is not sufficient for ensuring usage of mouthguards in a military population. Emphasis on motivation or mandating use may be required.     

Current Clinical Applications of Artificial Intelligence in Radiology and Their Best Supporting Evidence

PurposeDespite tremendous gains from deep learning and the promise of artificial intelligence (AI) in medicine to improve diagnosis and save costs, there exists a large translational gap to implement and use AI products in real-world clinical situations. Adoption of standards such as Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis, Consolidated Standards of Reporting Trials, and the Checklist for Artificial Intelligence in Medical Imaging is increasing to improve the peer-review process and reporting of AI tools. However, no such standards exist for product-level review.MethodsA review of clinical trials showed a paucity of evidence for radiology AI products; thus, the authors developed a 10-question assessment tool for reviewing AI products with an emphasis on their validation and result dissemination. The assessment tool was applied to commercial and open-source algorithms used for diagnosis to extract evidence on the clinical utility of the tools.ResultsThere is limited technical information on methodologies for FDA-approved algorithms compared with open-source products, likely because of intellectual property concerns. Furthermore, FDA-approved products use much smaller data sets compared with open-source AI tools, because the terms of use of public data sets are limited to academic and noncommercial entities, which precludes their use in commercial products.ConclusionsOverall, this study reveals a broad spectrum of maturity and clinical use of AI products, but a large gap exists in exploring actual performance of AI tools in clinical practice.     

Radiology in Medical Education: A Pediatric Radiology Elective as a Template for Other Radiology Courses

PurposeTraditionally, the pediatric radiology elective for medical students and pediatric residents constituted a morning teaching session focused mainly on radiography and fluoroscopy. A more structured elective was desired to broaden the exposure to more imaging modalities, create a more uniform educational experience, and include assessment tools.MethodsIn 2012, an introductory e-mail and formal syllabus, including required reading assignments, were sent to participants before the start date. A rotating weekly schedule was expanded to include cross-sectional imaging (ultrasound, CT, MR) and nuclear medicine. The schedule could accommodate specific goals of the pediatric resident or medical student, as requested. Starting in 2013, an online pre-test and post-test were developed, as well as an online end-of-rotation survey specific to the pediatric radiology elective. Taking the Image Gently pledge was required. A scavenger hunt tool, cue cards, and electronic modules were added.ResultsPre-test and post-test scores, averaged over 2 years, showed improvement in radiology knowledge, with scores increasing by 27% for medical students and 21% for pediatric residents. Surveys at the end of the elective were overwhelmingly positive, with constructive criticism and complimentary comments.ConclusionsWe have successfully created an elective experience in radiology that dedicates time to education while preserving the workflow of radiologists. We have developed tools to provide a customized experience with many self-directed learning opportunities. Our tools and techniques are easily translatable to a general or adult radiology elective.     

Artificial intelligence in dentomaxillofacial radiology

Artificial intelligence (AI) has the potential to revolutionize healthcare and dentistry. Recently, there has been much interest in the development of AI applications. Dentomaxillofacial radiology (DMFR) is within the scope of these applications due to its compatibility with image processing methods. Classification and segmentation of teeth, automatic marking of anatomical structures and cephalometric analysis, determination of early dental diseases, gingival, periodontal diseases and evaluation of risk groups, diagnosis of certain diseases, such as; osteoporosis that can be detected in jaw radiographs are among studies conducted by using radiological images. Further research in the field of AI will make great contributions to DMFR. We aim to discuss most recent AI-based studies in the field of DMFR.     

Impact of COVID-19 pandemic on radiographers in the Republic of Cyprus. A questionnaire survey

IntroductionImaging is essential for the initial diagnosis and monitoring of the novel coronavirus, which emerged in Wuhan, China. This study aims to assess the insight of radiographers on how the COVID-19 pandemic has affected their work routine and if protective measures are applied.MethodA prospective observational study was conducted among radiographers registered in the Cyprus Society of Registered Radiologic Technologists & Radiation Therapy Technologists. A questionnaire composed of 28 multiple choice questions was utilised, and the data analysis was performed using SPSS software with the statistical significance assumed as p-value < 0.05.ResultsOut of 350 registered radiographers, 101 responses were received. The results showed that there are statistically significant differences regarding the working hours, the feeling of stress, the work effectiveness, the average examination time, the presence of a protocol used among the different workplaces of the participants; a private radiology centre, a private hospital or a public hospital, with a p-value 0.0022, 0.015, 0.027, 0.001, 0.0001 respectively. Also, statistically significant differences were observed in the decontamination methods used for equipment (p-value 0.007), for air (p-value 0.04) and when decontamination takes place (p-value 0.00032) among the different workplaces of the participants. Nonetheless, the majority of radiographers believe that their workplace is sufficiently provided with PPE, cleaning supplies, equipment, and with cleaning personnel and are optimistic regarding the adequacy of these provisions in the next three months.ConclusionThis study showed that in the Republic of Cyprus, there are protocols regarding protective measures against COVID-19, and the radiographers are adequately trained on how to face an infectious disease outbreak. However, work is needed in order to develop protocols that reassure the safety of patients and medical personnel while managing the excess workload effectively.Implications for practiceThis study indicates the importance of applying protective measures and protocols in the radiology departments in order to minimise the spread of the virus.     

Examining Gender Disparity in Academic Abdominal Radiology in North America

BackgroundGender disparity exists in nearly every medical specialty, particularly in leadership roles and academia. Radiology is not exempt from this phenomenon, with women making up less than a third of radiology residents in the United States (US). This can have long-lasting effects on the career progression of female radiologists. Our search did not reveal any study on gender composition in academic abdominal radiology.PurposeTo evaluate the academic productivity and career advancement of female academic abdominal radiology faculty in the United States and Canada.Materials and methodsParameters of academic achievement were measured, including the number of citations and publications, years of research, as well as H-index. Information regarding academic and leadership ranking among academic abdominal radiologists in the United States and Canada was also analyzed.ResultsIn academic abdominal radiology, there were fewer females than males (34.9% vs 65.1%; p-value 0.256). Among the female radiologists, the greatest proportion held the rank of assistant professor (40%). Female representation decreased with increasing rank. Females had a lower H-index than males (P-value = 0.0066) and significantly fewer years of research than males (P-value = 0.0243).ConclusionMale predominance in academic abdominal radiology is similar to many other medical specialties, and encompasses senior faculty rank, leadership roles and research productivity.     

Workflow Applications of Artificial Intelligence in Radiology and an Overview of Available Tools

In the past decade, there has been tremendous interest in applying artificial intelligence (AI) to improve the field of radiology. Currently, numerous AI applications are in development, with potential benefits spanning all steps of the imaging chain from test ordering to report communication. AI has been proposed as a means to optimize patient scheduling, improve worklist management, enhance image acquisition, and help radiologists interpret diagnostic studies. Although the potential for AI in radiology appears almost endless, the field is still in the early stages, with many uses still theoretical, in development, or limited to single institutions. Moreover, although the current use of AI in radiology has emphasized its clinical applications, some of which are in the distant future, it is increasingly clear that AI algorithms could also be used in the more immediate future for a variety of noninterpretive and quality improvement uses. Such uses include the integration of AI into electronic health record systems to reduce unwarranted variation in radiologists’ follow-up recommendations and to improve other dimensions of radiology report quality. In the end, the potential of AI in radiology must be balanced with acknowledgment of its current limitations regarding generalizability and data privacy.     

Questionnaire surveys of dentists on radiology

Objectives

Survey by questionnaire is a widely used research method in dental radiology. A major concern in reviews of questionnaires is non-response. The objectives of this study were to review questionnaire studies in dental radiology with regard to potential survey errors and to develop recommendations to assist future researchers.

Methods

A literature search with the software search package PubMed was used to obtain internet-based access to Medline through the website www.ncbi.nlm.nih.gov/pubmed. A search of the English language peer-reviewed literature was conducted of all published studies, with no restriction on date. The search strategy found articles with dates from 1983 to 2010. The medical subject heading terms used were “questionnaire”, “dental radiology” and “dental radiography”. The reference sections of articles retrieved by this method were hand-searched in order to identify further relevant papers. Reviews, commentaries and relevant studies from the wider literature were also included.

Results

53 questionnaire studies were identified in the dental literature that concerned dental radiography and included a report of response rate. These were all published between 1983 and 2010. In total, 87 articles are referred to in this review, including the 53 dental radiology studies. Other cited articles include reviews, commentaries and examples of studies outside dental radiology where they are germane to the arguments presented.

Conclusions

Non-response is only one of four broad areas of error to which questionnaire surveys are subject. This review considers coverage, sampling and measurement, as well as non-response. Recommendations are made to assist future research that uses questionnaire surveys.     

Evaluation of web-based instruction for anatomical interpretation in maxillofacial cone beam computed tomography

OBJECTIVES: To evaluate the effectiveness of a web-based instruction in the interpretation of anatomy in images acquired with maxillofacial cone beam CT (CBCT). METHODS: An interactive web-based education course for the interpretation of craniofacial CBCT images was recently developed at our institution. Self-evaluation modules on correlative anatomical features were also included to support the learning process. Three e-learner groups were selected to evaluate the effectiveness of the educational modules. The three groups were (1) oral health specialists (OHSs) (comprising periodontologists, prosthodontists, orthodontists and maxillofacial surgeons); (2) third grade (DS3) and (3) first grade (DS1) undergraduate dental students. The assessment modules that were part of the interactive web-course content were administered after delivery of the course material. In addition, each group received a computer affinity questionnaire to quantify the extent of knowledge about computers and a perception questionnaire to assess their attitudes toward the web-course. RESULTS: The OHS group yielded significantly better scoring results in the post-course test than the pre-course test. However, no statistically significant differences in test scores were found for both undergraduate student groups (DS1 and DS3). All groups presented a highly positive attitude towards the web-course, as was demonstrated by the post-course perception questionnaire. CONCLUSIONS: The present CBCT educational course is an effective didactic method for teaching OHSs the anatomical interpretation of CBCT multiplanar reformatted images and, for undergraduate students, it was found to be as effective as conventional educational methods in dentistry. The efficacy of a web-based educational course requires further evaluation.     

The quality and impact of computer supported collaborative learning (CSCL) in radiology case-based learning

Objective

The aim of this research was to explore (1) clinical years students’ perceptions about radiology case-based learning within a computer supported collaborative learning (CSCL) setting, (2) an analysis of the collaborative learning process, and (3) the learning impact of collaborative work on the radiology cases.

Methods

The first part of this study focuses on a more detailed analysis of a survey study about CSCL based case-based learning, set up in the context of a broader radiology curriculum innovation. The second part centers on a qualitative and quantitative analysis of 52 online collaborative learning discussions from 5th year and nearly graduating medical students. The collaborative work was based on 26 radiology cases regarding musculoskeletal radiology.

Results

The analysis of perceptions about collaborative learning on radiology cases reflects a rather neutral attitude that also does not differ significantly in students of different grade levels. Less advanced students are more positive about CSCL as compared to last year students. Outcome evaluation shows a significantly higher level of accuracy in identification of radiology key structures and in radiology diagnosis as well as in linking the radiological signs with available clinical information in nearly graduated students. No significant differences between different grade levels were found in accuracy of using medical terminology.

Conclusion

Students appreciate computer supported collaborative learning settings when tackling radiology case-based learning. Scripted computer supported collaborative learning groups proved to be useful for both 5th and 7th year students in view of developing components of their radiology diagnostic approaches.