Radiology Question Paper Download

Exams for both modules are held 3 times a year in March, June and September. Exact exam dates can be found on the Royal College of Radiologists website. ST1 radiology trainees are expected to attempt both modules in the March sitting of their first year of training. Visit the RCR website to view the specialty training curriculum for clinical radiology and get information on exam dates, fees and venues. Tips and links to useful resources for each examination are shown below.

There are 100 image based questions and marks are awarded for precision of anatomical description on each image. Each question is marked on a scale of 0, 1 or 2. The maximum mark for a single question is 2. As there are 100 questions, the examination is marked out of 200. The following mark scheme is used for each question:

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The exam consists of 200 true or false questions. There are 40 stems (question or statement) and five statements (answers) for each stem that must be marked true or false. The paper lasts 2 hours. The pass mark varies for each sitting, but is usually somewhere in the region of 70-75%.

Radiology reporting is a crucial part of the communication between radiologists and other medical professionals, but it can be time-consuming and error-prone. One approach to alleviate this is structured reporting, which saves time and enables a more accurate evaluation than free-text reports. However, there is limited research on automating structured reporting, and no public benchmark is available for evaluating and comparing different methods. To close this gap, we introduce Rad-ReStruct, a new benchmark dataset that provides fine-grained, hierarchically ordered annotations in the form of structured reports for X-Ray images. We model the structured reporting task as hierarchical visual question answering (VQA) and propose hi-VQA, a novel method that considers prior context in the form of previously asked questions and answers for populating a structured radiology report. Our experiments show that hi-VQA achieves competitive performance to the state-of-the-art on the medical VQA benchmark VQARad while performing best among methods without domain-specific vision-language pretraining and provides a strong baseline on Rad-ReStruct. Our work represents a significant step towards the automated population of structured radiology reports and provides a valuable first benchmark for future research in this area. We will make all annotations and our code for annotation generation, model evaluation, and training publicly available upon acceptance.

The paper introduces the first structured radiology reporting benchmark dataset Rad-ReStruct and a novel method called hi-VQA for automating structured reporting. The proposed method contributes to the development of automated structured radiology report population methods, while allowing an accurate and multi-level evaluation of clinical correctness and fostering fine-grained, in-depth radiological image understanding.

The authors propose a benchmark dateset RadStruct that provides fine-grained, hierarchically ordered annotations in the form structured report generation, which can benefit the further studies to develop more helpful reporting systems.This paper provides a hi-VQA architecture that considers prior context in the form of previously asked questions and answers for populating a structured radiology report.

The contributions of this paper can be summarized as two aspects: 1) A benchmark dataset of structured medical report generation; 2) A hierarchical VQA model with the memory of history questions. Howeverthere are also some drawbacks:

This study introduces Rad-ReStruct, a novel benchmark dataset featuring fine-grained, hierarchically ordered annotations for X-ray images in the form of structured reports. The authors propose a new approach, hi-VQA, which models the structured reporting task as hierarchical visual question answering (VQA) and takes into account prior context from previously asked questions and answers to generate structured radiology reports.

Introduction of a new benchmark dataset: The authors present Rad-ReStruct, a valuable benchmark dataset that provides fine-grained, hierarchically ordered annotations in the form of structured reports for X-ray images. This dataset addresses the current gap in the research area and facilitates the evaluation and comparison of different methods for automating structured radiology reporting.

Novel methodology: The proposed hi-VQA method models the structured reporting task as hierarchical visual question answering (VQA), considering prior context in the form of previously asked questions and answers. This innovative approach has the potential to improve the automated population of structured radiology reports.

Novelty and significance: The paper introduces a new benchmark dataset (Rad-ReStruct) and proposes a novel method (hi-VQA) for hierarchical visual question answering in radiology reporting. These contributions have the potential to advance the field and improve the automated population of structured radiology reports.

The paper introduces Rad-ReStruct, a novel benchmark dataset with fine-grained, hierarchically ordered annotations for X-ray images in the form of structured reports. The proposed hi-VQA method models the structured reporting task as hierarchical visual question answering (VQA) and incorporates prior context from previously asked questions and answers. The strengths of the paper include the introduction of the benchmark dataset and the comprehensive evaluation showing competitive performance. However, weaknesses include the lack of clarity in algorithm details, particularly in inference and hierarchy handling, training and evaluation processes, feature encoding, as well as the potentially poor generalization to open-ended settings that reflect detailed pathological content. Overall, the paper contributes to the field and is relevant to the clinical session, but clarifications are needed in addressing the weaknesses.

Our model uses a transformer with input-specific token-type IDs facilitating an informed, attention-based feature fusion (R2). Also, it must rely on visual details for precise answers (R2) as prerequisite questions offer only high-level information.

If your doctor provided you with a paper prescription, please have it available when you call. Please also have your insurance card available. If you are scheduling an MRI and you have an implant, have your implant card available as well.

You can use the NYU Langone Health app to complete questionnaires before your visit, and to view arrival and preparation instructions. After your appointment, you can use the app to read your results and view and share the exam images.

The latest version of ChatGPT passed a radiology board-style exam, highlighting the potential of large language models but also revealing limitations that hinder reliability, according to two new research studies published in Radiology.

To assess its performance on radiology board exam questions and explore strengths and limitations, Dr. Bhayana and colleagues first tested ChatGPT based on GPT-3.5, currently the most commonly used version. The researchers used 150 multiple-choice questions designed to match the style, content and difficulty of the Canadian Royal College and American Board of Radiology exams.

The questions did not include images and were grouped by question type to gain insight into performance: lower-order (knowledge recall, basic understanding) and higher-order (apply, analyze, synthesize) thinking. The higher-order thinking questions were further subclassified by type (description of imaging findings, clinical management, calculation and classification, disease associations).

The researchers found that ChatGPT based on GPT-3.5 answered 69% of questions correctly (104 of 150), near the passing grade of 70% used by the Royal College in Canada. The model performed relatively well on questions requiring lower-order thinking (84%, 51 of 61), but struggled with questions involving higher-order thinking (60%, 53 of 89).

More specifically, it struggled with higher-order questions involving description of imaging findings (61%, 28 of 46), calculation and classification (25%, 2 of 8), and application of concepts (30%, 3 of 10). Its poor performance on higher-order thinking questions was not surprising given its lack of radiology-specific pretraining.

ChatGPT response to a classification question involving the Thyroid Imaging Reporting and Data System (TI-RADS). The model selected the incorrect answer (option B, TI-RADS 3). Since the lesion is solid (2 points), hypoechoic (2 points), and has macrocalcifications (1 point), this corresponds to a TI-RADS 4 lesion (correct answer is option C).

In a follow-up study, GPT-4 answered 81% (121 of 150) of the same questions correctly, outperforming GPT-3.5 and exceeding the passing threshold of 70%. GPT-4 performed much better than GPT-3.5 on higher-order thinking questions (81%), more specifically those involving description of imaging findings (85%) and application of concepts (90%).

GPT-4 showed no improvement on lower-order thinking questions (80% vs 84%) and answered 12 questions incorrectly that GPT-3.5 answered correctly, raising questions related to its reliability for information gathering.

At the time of the examination you must be employed as an accredited radiology trainee and you must have completed all required training program assessments at the time of applying to sit the examination. If you commence in an accredited radiology training position after the closing date for applications, in addition to submitting the examination application form, the completed and required attachments of the Approval of Course in Training Form must be submitted with your examination application prior to the closing date.

There has been a lot of discussion about the artificial intelligence chatbot ChatGPT and how it might be used in healthcare. In radiology, one of the most interesting developments is the latest version ChatGPT (GPT-4) passing the written portion of a radiology-style board exam. The research was published today in the Radiological Society of North America's flagship journal Radiology.[1,2] 17dc91bb1f