ROCOv2: Radiology Objects in COntext Version 2, an Updated Multimodal Image Dataset.

Automated medical image analysis systems often require large amounts of training data with high quality labels, which are difficult and time consuming to generate. This paper introduces Radiology Object in COntext version 2 (ROCOv2), a multimodal dataset consisting of radiological images and associated medical concepts and captions extracted from the PMC Open Access subset. It is an updated version of the ROCO dataset published in 2018, and adds 35,705 new images added to PMC since 2018. It further provides manually curated concepts for imaging modalities with additional anatomical and directional concepts for X-rays. The dataset consists of 79,789 images and has been used, with minor modifications, in the concept detection and caption prediction tasks of ImageCLEFmedical Caption 2023. The dataset is suitable for training image annotation models based on image-caption pairs, or for multi-label image classification using Unified Medical Language System (UMLS) concepts provided with each image. In addition, it can serve for pre-training of medical domain models, and evaluation of deep learning models for multi-task learning.

Visual Recalibration and Gating Enhancement Network for Radiology Report Generation.

Automatic radiology medical report generation is a necessary development of artificial intelligence technology in the health care. This technology serves to aid doctors in producing comprehensive diagnostic reports, alleviating the burdensome workloads of medical professionals. However, there are some challenges in generating radiological reports: (1) visual and textual data biases and (2) long-distance dependency problem. To tackle these issues, we design a visual recalibration and gating enhancement network (VRGE), which composes of the visual recalibration module and the gating enhancement module (gating enhancement module, GEM). Specifically, the visual recalibration module enhances the recognition of abnormal features in lesion areas of medical images. The GEM dynamically adjusts the contextual information in the report by introducing gating mechanisms, focusing on capturing professional medical terminology in medical text reports. We have conducted sufficient experiments on the public datasets of IU X-Ray to illustrate that the VRGE outperforms existing models.

Integrating and Adopting AI in the Radiology Workflow: A Primer for Standards and Integrating the Healthcare Enterprise (IHE) Profiles.

The deployment of artificial intelligence (AI) solutions in radiology practice creates new demands on existing imaging workflow. Accommodating custom integrations creates a substantial operational and maintenance burden. These custom integrations also increase the likelihood of unanticipated problems. Standards-based interoperability facilitates AI integration with systems from different vendors into a single environment by enabling seamless exchange between information systems in the radiology workflow. Integrating the Healthcare Enterprise (IHE) is an initiative to improve how computer systems share information across health care domains, including radiology. IHE integrates existing standards-such as Digital Imaging and Communications in Medicine, Health Level Seven, and health care lexicons and ontologies (ie, LOINC, RadLex, SNOMED Clinical Terms)-by mapping data elements from one standard to another. IHE Radiology manages profiles (standards-based implementation guides) for departmental workflow and information sharing across care sites, including profiles for scaling AI processing traffic and integrating AI results. This review focuses on the need for standards-based interoperability to scale AI integration in radiology, including a brief review of recent IHE profiles that provide a framework for AI integration. This review also discusses challenges and additional considerations for AI integration, including technical, clinical, and policy perspectives.

Spectrum of Cognitive Biases in Diagnostic Radiology.

Cognitive biases are systematic thought processes involving the use of a filter of personal experiences and preferences arising from the tendency of the human brain to simplify information processing, especially when taking in vast amounts of data such as from imaging studies. These biases encompass a wide spectrum of thought processes and frequently overlap in their concepts, with multiple biases usually in operation when interpretive and perceptual errors occur in radiology. The authors review the gamut of cognitive biases that occur in radiology. These biases are organized according to their expected stage of occurrence while the radiologist reads and interprets an imaging study. In addition, the authors propose several additional cognitive biases that have not yet, to their knowledge, been defined in the radiologic literature but are applicable to diagnostic radiology. Case examples are used to illustrate potential biases and their impact, with emergency radiology serving as the clinical paradigm, given the associated high imaging volumes, wide diversity of imaging examinations, and rapid pace, which can further increase a radiologist's reliance on biases and heuristics. Potential strategies to recognize and overcome one's personal biases at each stage of image interpretation are also discussed. Awareness of such biases and their unintended effects on imaging interpretations and patient outcomes may help make radiologists cognizant of their own biases that can result in diagnostic errors. Identification of cognitive bias in departmental and systematic quality improvement practices may represent another tool to prevent diagnostic errors in radiology. ©RSNA, 2024 See the invited commentary by Larson in this issue.

Medical students within the Radiology Departments: Drawback or opportunity?

University Radiology training has been carried out for years out of the Radiology Departments, where radiologists play their professional role. However, the educational needs and the leadership of the Scientific Societies make the Radiology Departments to be involved more and more in that training, though it has to be done in overloaded professional environments where medical students can be seen as a drawback. Nevertheless, radiologists must play an important role in the Radiology training of the future doctors for optimising the use of diagnostic imaging techniques and enhance the future of the specialty by bringing to our Departments those medical students who had demonstrated the most adequate personal profiles. The Radiology Department is that place to succeed by increasing the healthcare outcomes, the research results and the visibility of Radiology through a fruitful interaction between radiologists and medical students.

A problem-based learning experience in a radiology rotation for sixth-year medical students.

OBJECTIVE: To analyse a problem-based learning experience (PBL) in the sixth year of medicine, within a course organised in successive rotations of 12 school days for 7 annual groups. MATERIAL AND METHODS: Each group was divided into subgroups of 6-8 students. Each subgroup was assigned two cases with radiographic images that they had to prepare and present in a joint session in which the students discussed each case and the teacher acted as moderator, without providing solutions. Finally, they had 15 days to complete the debate in an online forum and prepare a written report on each case. RESULTS: During 6 consecutive years, 1001 students participated, whose annual grades ranged between 7.7 ±â€¯1.6 and 9.0 ±â€¯0.7 (mean ±â€¯standard deviation). No correlation was found between the degree of difficulty assigned to the cases and the mean score obtained by each group (R2 = 0.0115). Sixty-six point two percent completed a questionnaire rating various aspects of this experience above 4 out of 5 points and providing overall scores above 8.3 out of 10 points in the different years. The students found this experience appropriate to the objectives of the subject and useful for their educational needs. CONCLUSIONS: PBL allows students to acquire skills of understanding, reasoning and deepening in radiological diagnosis. This study demonstrates that an experience based on PBL can be included in a radiology course organised in a traditional way, allowing students to be graded regardless of the difficulty of the cases.

End-of-degree projects in radiology in Spanish universities.

The End-of-Degree Project (TFG) is a supervised research project that medical students must carry out before graduating. This study aims to make radiology teaching staff aware of the importance of getting involved in tutoring radiology TFGs. We provide recommendations to help encourage students choose our area and carry it out. We describe the TFG regulations for the subject of medicine as well as data on TFGs carried out both in medicine in general, and more specifically in radiology between 2018 and 2022. The total number of radiology TFGs was 181, accounting for 3.3% of the 5349 TFGs carried out in medicine. There was a discrepancy between the results found on the websites, those expected according to the number of graduates and those provided by the teachers contacted. We would consider reasonable a percentage of TFGs in radiology proportional to the number of credits of this subject during the degree course and the number of lecturers in this subject.

Green radiology: How to develop sustainable radiology.

The phenomenon of global warming due to the increased emission of greenhouse gases makes it necessary to raise public awareness about the importance of promoting sustainable practices. The field of radiology is not an exception, as it consumes a large amount of energy and resources to operate equipment and generate images. Green radiology is a sustainable, innovative, and responsible approach in radiology practice that focuses on minimizing the negative environmental effects of the technologies and procedures used in radiology. Its primary goal is to reduce the carbon, water and ecological footprint in our services based on four strategic pillars: decreasing energy, water, and helium usage; properly recycling and/or disposing of waste and residues (including contrast media); minimizing the environmental impact of ionizing radiation; and promoting eco-friendly radiology practices.

Patient-centered radiology reports with generative artificial intelligence: adding value to radiology reporting.

The purposes were to assess the efficacy of AI-generated radiology reports in terms of report summary, patient-friendliness, and recommendations and to evaluate the consistent performance of report quality and accuracy, contributing to the advancement of radiology workflow. Total 685 spine MRI reports were retrieved from our hospital database. AI-generated radiology reports were generated in three formats: (1) summary reports, (2) patient-friendly reports, and (3) recommendations. The occurrence of artificial hallucinations was evaluated in the AI-generated reports. Two radiologists conducted qualitative and quantitative assessments considering the original report as a standard reference. Two non-physician raters assessed their understanding of the content of original and patient-friendly reports using a 5-point Likert scale. The scoring of the AI-generated radiology reports were overall high average scores across all three formats. The average comprehension score for the original report was 2.71 ± 0.73, while the score for the patient-friendly reports significantly increased to 4.69 ± 0.48 (p < 0.001). There were 1.12% artificial hallucinations and 7.40% potentially harmful translations. In conclusion, the potential benefits of using generative AI assistants to generate these reports include improved report quality, greater efficiency in radiology workflow for producing summaries, patient-centered reports, and recommendations, and a move toward patient-centered radiology.

Application of the process-based teaching based on SPARK case database in the practice teaching of radiology in the musculoskeletal system for undergraduate medical students.

BACKGROUND: Process-based teaching is a new education model. SPARK case database is a free medical imaging case database. This manuscript aimed to explore the application of the process-based teaching based on SPARK case database in the practice teaching of radiology in the musculoskeletal system. METHODS: 117 third year medical students were included. They were divided into Group A, B, C and D according to the curriculum arrangement. Group A and B attended the experimental class at the same time, A was the experimental group, B was the control group. Group C and D attended experimental classes at the same time, C was the experimental group, D was the control group. The experimental group used SPARK case database, while the control group used traditional teaching model for learning. The four groups of students were respectively tested after the theoretical class, before the experimental class, after the experimental class, and one week after the experimental class to compare the results. Finally, all students used SPARK case database to study, and were tested one month after the experimental class to compare their differences. RESULTS: The scores after the theoretical class of Group A and B were (100.0 ± 25.4), (101.0 ± 23.8)(t=-0.160, P > 0.05), Group C and D were (94.7 ± 23.7), (92.1 ± 18.6)(t = 0.467, P > 0.05). The scores of Group A and B before and after the experimental class and one week after the experimental class were respectively (84.1 ± 17.4), (72.1 ± 21.3)(t = 2.363, P < 0.05), (107.6 ± 14.3), (102.1 ± 18.0)(t = 1.292, P > 0.05), (89.7 ± 24.3), (66.6 ± 23.2)(t = 3.706, P < 0.05). The scores of Group C and D were (94.0 ± 17.3), (72.8 ± 25.5)(t = 3.755, P < 0.05), (107.3 ± 20.3), (93.1 ± 20.9)(t = 2.652, P < 0.05), (100.3 ± 19.7), (77.2 ± 24.0)(t = 4.039, P < 0.05). The scores of Group A and B for one month after the experimental class were (86.6 ± 28.8), (84.5 ± 24.0)(t = 0.297, P > 0.05), and Group C and D were (95.7 ± 20.3), (91.7 ± 23.0)(t = 0.699, P > 0.05). CONCLUSIONS: The process-based teaching based on SPARK case database could improve the radiology practice ability of the musculoskeletal system of students.