Subspecialty Of Radiology Research Articles

Perilesional dominance: radiomics of multiparametric MRI enhances differentiation of IgG4-Related ophthalmic disease and orbital MALT lymphoma

BackgroundTo develop and validate a diagnostic framework integrating intralesional (ILN) and perilesional (PLN) radiomics derived from multiparametric MRI (mpMRI) for distinguishing IgG4-related ophthalmic disease (IgG4-ROD) from orbital mucosa-associated lymphoid tissue (MALT) lymphoma.MethodsThis multicenter retrospective study analyzed 214 histopathologically confirmed cases (68 IgG4-ROD, 146 MALT lymphoma) from two institutions (2019–2024). A LASSO-SVM classifier was optimized through comparative evaluation of seven machine learning models, incorporating fused radiomic features (1,197 features) from ILN/PLN regions. Diagnostic performance was benchmarked against two subspecialty radiologists (10–20 years’ experience) using receiver operating characteristics - area under the curve (AUC), precision-recall AUC (PR-AUC), and decision curve analysis (DCA), adhering to CLEAR/METRICS guidelines.ResultsThe fusion model (FR_RAD) achieved state-of-the-art performance, with an AUC of 0.927 (95% CI 0.902–0.958) and a PR-AUC of 0.901 (95% CI 0.862–0.940) in the training set, and an AUC of 0.907 (95% CI 0.857–0.965) and a PR-AUC of 0.872 (95% CI 0.820–0.924) on external testing. In contrast, subspecialty radiologists achieved lower AUCs of 0.671–0.740 (95% CI 0.630–0.780) and PR-AUCs of 0.553–0.632 (95% CI 0.521–0.664) (all p < 0.001). FR_RAD also outperformed radiologists in accuracy (88.6% vs. 66.2% and 71.3%; p < 0.01). DCA demonstrated a net benefit of 0.18 at a high-risk threshold of 30%, equivalent to avoiding 18 unnecessary biopsies per 100 cases.ConclusionsThe fusion model integrating multi-regional radiomics from mpMRI achieves precise differentiation between IgG4-ROD and orbital MALT lymphoma, outperforming subspecialty radiologists. This approach highlights the transformative potential of spatial radiomics analysis in resolving diagnostic uncertainties and reducing reliance on invasive procedures for orbital lesion characterization.

RadioRAG: Online Retrieval-augmented Generation for Radiology Question Answering.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To evaluate diagnostic accuracy of various large language models (LLMs) when answering radiology-specific questions with and without access to additional online, up-to-date information via retrieval-augmented generation (RAG). Materials and Methods The authors developed Radiology RAG (RadioRAG), an end-to-end framework that retrieves data from authoritative radiologic online sources in real-time. RAG incorporates information retrieval from external sources to supplement the initial prompt, grounding the model's response in relevant information. Using 80 questions from the RSNA Case Collection across radiologic subspecialties and 24 additional expert-curated questions with reference standard answers, LLMs (GPT-3.5-turbo, GPT-4, Mistral-7B, Mixtral-8 × 7B, and Llama3 [8B and 70B]) were prompted with and without RadioRAG in a zero-shot inference scenario (temperature ≤ 0.1, top- P = 1). RadioRAG retrieved context-specific information from www.radiopaedia.org. Accuracy of LLMs with and without RadioRAG in answering questions from each dataset was assessed. Statistical analyses were performed using bootstrapping while preserving pairing. Additional assessments included comparison of model with human performance and comparison of time required for conventional versus RadioRAG-powered question answering. Results RadioRAG improved accuracy for some LLMs, including GPT-3.5-turbo [74% (59/80) versus 66% (53/80), FDR = 0.03] and Mixtral-8 × 7B [76% (61/80) versus 65% (52/80), FDR = 0.02] on the RSNA-RadioQA dataset, with similar trends in the ExtendedQA dataset. Accuracy exceeded (FDR ≤ 0.007) that of a human expert (63%, (50/80)) for these LLMs, while not for Mistral-7B-instruct-v0.2, Llama3-8B, and Llama3-70B (FDR ≥ 0.21). RadioRAG reduced hallucinations for all LLMs (rates from 6-25%). RadioRAG increased estimated response time fourfold. Conclusion RadioRAG shows potential to improve LLM accuracy and factuality in radiology question answering by integrating real-time domain-specific data. ©RSNA, 2025.

Evaluating ChatGPT's performance across radiology subspecialties: A meta-analysis of board-style examination accuracy and variability.

Evaluating ChatGPT's performance across radiology subspecialties: A meta-analysis of board-style examination accuracy and variability.

Age is more than just a number - Tailoring radiologic practice for the geriatric population.

Age is more than just a number - Tailoring radiologic practice for the geriatric population.

Evaluating the reference accuracy of large language models in radiology: a comparative study across subspecialties.

This study aimed to compare six large language models (LLMs) [Chat Generative Pre-trained Transformer (ChatGPT)o1-preview, ChatGPT-4o, ChatGPT-4o with canvas, Google Gemini 1.5 Pro, Claude 3.5 Sonnet, and Claude 3 Opus] in generating radiology references, assessing accuracy, fabrication, and bibliographic completeness. In this cross-sectional observational study, 120 open-ended questions were administered across eight radiology subspecialties (neuroradiology, abdominal, musculoskeletal, thoracic, pediatric, cardiac, head and neck, and interventional radiology), with 15 questions per subspecialty. Each question prompted the LLMs to provide responses containing four references with in-text citations and complete bibliographic details (authors, title, journal, publication year/month, volume, issue, page numbers, and PubMed Identifier). References were verified using Medline, Google Scholar, the Directory of Open Access Journals, and web searches. Each bibliographic element was scored for correctness, and a composite final score [(FS): 0-36] was calculated by summing the correct elements and multiplying this by a 5-point verification score for content relevance. The FS values were then categorized into a 5-point Likert scale reference accuracy score (RAS: 0 = fabricated; 4 = fully accurate). Non-parametric tests (Kruskal-Wallis, Tamhane's T2, Wilcoxon signed-rank test with Bonferroni correction) were used for statistical comparisons. Claude 3.5 Sonnet demonstrated the highest reference accuracy, with 80.8% fully accurate references (RAS 4) and a fabrication rate of 3.1%, significantly outperforming all other models (P < 0.001). Claude 3 Opus ranked second, achieving 59.6% fully accurate references and a fabrication rate of 18.3% (P < 0.001). ChatGPT-based models (ChatGPT-4o, ChatGPT-4o with canvas, and ChatGPT o1-preview) exhibited moderate accuracy, with fabrication rates ranging from 27.7% to 52.9% and <8% fully accurate references. Google Gemini 1.5 Pro had the lowest performance, achieving only 2.7% fully accurate references and the highest fabrication rate of 60.6% (P < 0.001). Reference accuracy also varied by subspecialty, with neuroradiology and cardiac radiology outperforming pediatric and head and neck radiology. Claude 3.5 Sonnet significantly outperformed all other models in generating verifiable radiology references, and Claude 3 Opus showed moderate performance. In contrast, ChatGPT models and Google Gemini 1.5 Pro delivered substantially lower accuracy with higher rates of fabricated references, highlighting current limitations in automated academic citation generation. The high accuracy of Claude 3.5 Sonnet can improve radiology literature reviews, research, and education with dependable references. The poor performance of other models, with high fabrication rates, risks misinformation in clinical and academic settings and highlights the need for refinement to ensure safe and effective use.

Evaluating the Performance of Reasoning Large Language Models on Japanese Radiology Board Examination Questions.

Evaluating the Performance of Reasoning Large Language Models on Japanese Radiology Board Examination Questions.

Exploring Interventional Radiology: A Multicentre Study on Saudi Medical and Radiology Technology Students' Perspectives.

Interventional radiology (IR) is a subspecialty of diagnostic radiology that uses image-guided radiological methods to carry out minimally invasive procedures. Medical schools in Saudi Arabia minimally expose students to IR unless it is part of an elective rotation. The study aims to gauge how well informed medical and radiology technology students are regarding the variations in educational and clinical experiences offered at different universities in Jeddah, Saudi Arabia. It also aims to assess students' interest in IR as a potential career path and their opinions about their life experiences concerning the department's future. This study used a cross-sectional study design. Between April and May 2023 in Jeddah, Saudi Arabia, students studying radiology technology and medicine who were in their second year to internship year were given access to a cross-sectional questionnaire. The study found that 31.5% of the students reported having poor knowledge of IR, while 7.8% reported not knowing about it at all. Additionally, 45.9% of respondents felt that their knowledge was adequate, while a minority of 14.7% reported having an excellent understanding of IR concepts. Therefore, in order to enhance students' knowledge about IR, IR courses should be introduced early into curricula, IR symposiums and conferences. The limited exposure of medical and radiology technology students to IR was highlighted. Over one-third indicated interest in IR as a career, with radiology technology students demonstrating greater familiarity. Enhancing IR education through early curriculum integration, symposiums, and conferences is essential. Furthermore, addressing the lack of a standardized radiology curriculum in Saudi medical schools could further enhance IR awareness and career development.

Recent Trends in Academic Versus Nonacademic Radiologist Compensation and Clinical Productivity.

Recent Trends in Academic Versus Nonacademic Radiologist Compensation and Clinical Productivity.

210 Addressing burnout in radiologists: Causes, impact on patient care, and potential solutions

Objectives/Goals: This study objective is to evaluate the prevalence and risk factors of burnout in practicing radiologists, with a focus on personal as well as systemic factors. It aims to identify and assess the existing strategies to mitigate burnout, enhance radiologist performance, and improve the quality of patient care. Methods/Study Population: The present study is a systematic review that summarizes existing literature on burnout in radiology, examining its prevalence, risk factors, and effect on diagnostic accuracy, decision-making, and job satisfaction. The review will synthesize validated evidence for emotional exhaustion, depersonalization, and professional fulfillment. The review discusses trends and solutions that have emerged from analysis of data within differing countries, subspecialties, and career stages, focusing on elevated risk of burnout in radiologists. It also assesses downstream effects on patient care quality such as missed diagnoses and increased medical errors. The review also discusses potential strategies for mitigating these negative effects on healthcare delivery. Results/Anticipated Results: The anticipated results of this review are expected to reveal significant variability in burnout rates across radiology subspecialties and practice settings, with prevalence ranging from 33% to 88% (Fawzy et al., 2023). Emotional exhaustion and depersonalization emerge as the most reported symptoms as consistently highlighted in previous studies. Major contributors such as workload, administrative burdens, and technological isolation (e.g., remote work and reduced face-to-face interaction) are anticipated. Radiologists in high-demand areas like interventional radiology and those in private practice may show higher burnout levels than those in academic settings. Protective factors, like exercise, supportive environments, and work-life balance, are expected to reduce burnout levels. Discussion/Significance of Impact: This study calls attention to the importance of addressing radiologist burnout as a key institutional priority. Early and effective interventions are essential for improving job satisfaction, reducing medical errors resulting in enhanced patient care. Addressing burnout is crucial for maintaining a sustainable and effective radiology workflow.

Generative pre-trained transformer 4o (GPT-4o) in solving text-based multiple response questions for European Diploma in Radiology (EDiR): a comparative study with radiologists

ObjectivesThis study aims to assess the accuracy of generative pre-trained transformer 4o (GPT-4o) in answering multiple response questions from the European Diploma in Radiology (EDiR) examination, comparing its performance to that of human candidates.Materials and methodsResults from 42 EDiR candidates across Europe were compared to those from 26 fourth-year medical students who answered exclusively using the ChatGPT-4o in a prospective study (October 2024). The challenge consisted of 52 recall or understanding-based EDiR multiple-response questions, all without visual inputs.ResultsThe GPT-4o achieved a mean score of 82.1 ± 3.0%, significantly outperforming the EDiR candidates with 49.4 ± 10.5% (p < 0.0001). In particular, chatGPT-4o demonstrated higher true positive rates while maintaining lower false positive rates compared to EDiR candidates, with a higher accuracy rate in all radiology subspecialties (p < 0.0001) except informatics (p = 0.20). There was near-perfect agreement between GPT-4 responses (κ = 0.872) and moderate agreement among EDiR participants (κ = 0.334). Exit surveys revealed that all participants used the copy-and-paste feature, and 73% submitted additional questions to clarify responses.ConclusionsGPT-4o significantly outperformed human candidates in low-order, text-based EDiR multiple-response questions, demonstrating higher accuracy and reliability. These results highlight GPT-4o’s potential in answering text-based radiology questions. Further research is necessary to investigate its performance across different question formats and candidate populations to ensure broader applicability and reliability.Critical relevance statementGPT-4o significantly outperforms human candidates in factual radiology text-based questions in the EDiR, excelling especially in identifying correct responses, with a higher accuracy rate compared to radiologists.Key PointsIn EDiR text-based questions, ChatGPT-4o scored higher (82%) than EDiR participants (49%).Compared to radiologists, GPT-4o excelled in identifying correct responses.GPT-4o responses demonstrated higher agreement (κ = 0.87) compared to EDiR candidates (κ = 0.33).Graphical

Explained Deep Learning Framework for COVID-19 Detection in Volumetric CT Images Aligned with the British Society of Thoracic Imaging Reporting Guidance: A Pilot Study.

In March 2020, the British Society of Thoracic Imaging (BSTI) introduced a reporting guidance for COVID-19 detection to streamline standardised reporting and enhance agreement between radiologists. However, most current DL methods do not conform to this guidance. This study introduces a multi-class deep learning (DL) model to identify BSTI COVID-19 categories within CT volumes, classified as 'Classic', 'Probable', 'Indeterminate', or 'Non-COVID'. A total of 56 CT pseudoanonymised images were collected from patients with suspected COVID-19 and annotated by an experienced chest subspecialty radiologist following the BSTI guidance. We evaluated the performance of multiple DL-based models, including three-dimensional (3D) ResNet architectures, pre-trained on the Kinetics-700 video dataset. For better interpretability of the results, our approach incorporates a post-hoc visual explainability feature to highlight the areas of the image most indicative of the COVID-19 category. Our four-class classification DL framework achieves an overall accuracy of 75%. However, the model struggled to detect the 'Indeterminate' COVID-19 group, whose removal significantly improved the model's accuracy to 90%. The proposed explainable multi-classification DL model yields accurate detection of 'Classic', 'Probable', and 'Non-COVID' categories with poor detection ability for 'Indeterminate' COVID-19 cases. These findings are consistent with clinical studies that aimed at validating the BSTI reporting manually amongst consultant radiologists.

Applications of artificial intelligence in thoracic imaging: a review

Artificial intelligence (AI) is transforming the field of radiology. Among various radiologic subspecialties, thoracic imaging has seen a significant rise in demand due to the global increase in heart, vascular, lung, and thoracic diseases such as lung cancer, pneumonia, pulmonary embolism, and cardiovascular diseases. AI promises to revolutionize radiologic diagnostics by enhancing detection, improving accuracy, and reducing the time required to interpret images. It leverages deep learning algorithms, particularly convolutional neural networks, which are increasingly integrated into thoracic imaging workflows to assist radiologists in diagnosing and evaluating heart, vascular, lung, and thoracic diseases. AI systems can help radiologists identify subtle findings that might otherwise be overlooked, thereby increasing efficiency and reducing diagnostic errors. Studies have shown that several AI algorithms have been trained to detect acute chest conditions such as pulmonary embolism, aortic dissection, pneumonia, rib fractures, and lung nodules with high sensitivity and specificity, offering substantial benefits in emergency and high-workload environments. This review article focuses on acute conditions presenting as acute chest syndrome or trauma in emergency settings. It provides an overview of AI applications in thoracic imaging, focusing on advancements in screening, early disease detection, triage and prioritization, automated image analysis, and workflow optimization. These points are supported by review articles published on the subject, including our own publications. We further explore challenges such as regulatory barriers, interpretability, and the need for large, diverse datasets. Finally, we discuss future directions for AI in thoracic imaging, highlighting its potential to enhance patient outcomes and healthcare system efficiencies.

Ranking the Relative Importance of Image Quality Features in CT by Consensus Survey

Ranking the Relative Importance of Image Quality Features in CT by Consensus Survey

Radiology resident proficiency in identifying misplaced lines, tubes, and devices: a simulation-based study using WIDI SIM.

Accurate placement of medical devices is crucial in critical care to prevent severe complications. This study aims to evaluate radiology residents' proficiency in identifying four specific critical misplacements of medical devices using the Wisdom in Diagnostic Imaging Simulation (WIDI SIM). A retrospective analysis was conducted on 1,102 responses from radiology residents who participated in the WIDI SIM between 2010 and 2022. The majority were first- and second-year residents from multiple institutions. The simulation presented four specific cases featuring misplacements of an endotracheal tube in the esophagus, an intrauterine device embedded in the myometrium, a peripherally inserted central catheter in the right internal jugular vein, and an umbilical venous catheter in the splenic vein. Residents provided free-text interpretations scored on a 0-10 scale by subspecialty radiologists. Errors were categorized as observational (failure to identify misplacement) or interpretive (misinterpretation of identified misplacement). Statistical analyses were performed using Kruskal-Wallis and Dunn's multiple comparisons tests. Across all cases, residents' average scores did not meet the acceptable standard of 7 points. Observational errors were predominant, indicating a failure to recognize these specific device misplacements. Effective report rates were low: 58% for the endotracheal tube case, 35% for the intrauterine device, 19% for the peripherally inserted central catheter, and 25% for the umbilical venous catheter. Significant performance improvements were observed between first- and second-year residents in three of the four cases (p-values ranging from < 0.0001 to 0.0238), but overall proficiency remained suboptimal even among senior residents. This study reveals gaps in radiology residents' ability to identify these specific misplaced lines, tubes, and devices accurately. The consistent pattern of underperformance, primarily due to observational errors, suggests a need for targeted educational interventions to improve resident proficiency in this aspect of emergency radiology.

Emergency imaging protocols for pregnant patients: a multi-institutional and multi- specialty comparison of physician education

PurposePrevious studies have demonstrated that radiologists and other providers perceive the teratogenic risks of radiologic imaging to be higher than they actually are. Thus, pregnant patients were less likely to receive ionizing radiation procedures. While it is imperative to minimize fetal radiation exposure, clinicians must remember that diagnostic studies should not be avoided due to fear of radiation, particularly if the imaging study can significantly impact patient care. Although guidelines do exist regarding how best to image pregnant patients, many providers are unaware of these guidelines and thus lack confidence when making imaging decisions for pregnant patients. This study aimed to gather information about current education, confidence in, and knowledge about emergency imaging of pregnant women among radiology, emergency medicine, and OB/GYN providers.MethodsWe created and distributed an anonymous survey to radiology, emergency medicine, and OB/GYN providers to evaluate their knowledge and confidence in imaging pregnant patients in the emergent setting. This study included a questionnaire with the intent of knowing the correct answers among physicians primarily across the United States (along with some international participation). We conducted subgroup analyses, comparing variables by specialty, radiology subspecialty, and training levels. Based on the survey results, we subsequently developed educational training videos.Results108 radiologists, of which 32 self-identified as emergency radiologists, ten emergency medicine providers and six OB/GYN clinicians completed the survey. The overall correct response rate was 68.5%, though performance across questions was highly variable. Within our 18-question survey, four questions had a correct response rate under 50%, while five questions had correct response rates over 90%. Most responding physicians identified themselves as either “fairly” (58/124, 47%) or “very” (51/124, 41%) confident. Amongst specialties, there were differences in performance concerning the knowledge assessment (p = 0.049), with the strongest performance from radiologists. There were no differences in knowledge by training level (p = 0.4), though confidence levels differed significantly between attending physicians and trainees (p < 0.001).ConclusionThis study highlights deficiencies in knowledge to support appropriate decision-making surrounding the imaging of pregnant patients. Our results indicate the need for improved physician education and dissemination of standardized clinical guidelines.

Testing the Ability and Limitations of ChatGPT to Generate Differential Diagnoses from Transcribed Radiologic Findings.

Background The burgeoning interest in ChatGPT as a potentially useful tool in medicine highlights the necessity for systematic evaluation of its capabilities and limitations. Purpose To evaluate the accuracy, reliability, and repeatability of differential diagnoses produced by ChatGPT from transcribed radiologic findings. Materials and Methods Cases selected from a radiology textbook series spanning a variety of imaging modalities, subspecialties, and anatomic pathologies were converted into standardized prompts that were entered into ChatGPT (GPT-3.5 and GPT-4 algorithms; April 3 to June 1, 2023). Responses were analyzed for accuracy via comparison with the final diagnosis and top 3 differential diagnosis provided in the textbook, which served as the ground truth. Reliability, defined based on the frequency of algorithmic hallucination, was assessed through the identification of factually incorrect statements and fabricated references. Comparisons were made between the algorithms using the McNemar test and a generalized estimating equation model framework. Test-retest repeatability was measured by obtaining 10 independent responses from both algorithms for 10 cases in each subspecialty, and calculating the average pairwise percent agreement and Krippendorff α. Results A total of 339 cases were collected across multiple radiologic subspecialties. The overall accuracy of GPT-3.5 and GPT-4 for final diagnosis was 53.7% (182 of 339) and 66.1% (224 of 339; P < .001), respectively. The mean differential score (ie, proportion of top 3 diagnoses that matched the original literature differential diagnosis) for GPT-3.5 and GPT-4 was 0.50 and 0.54 (P = .06), respectively. Of the references provided in GPT-3.5 and GPT-4 responses, 39.9% (401 of 1006) and 14.3% (161 of 1124; P < .001), respectively, were fabricated. GPT-3.5 and GPT-4 generated false statements in 16.2% (55 of 339) and 4.7% (16 of 339; P < .001) of cases, respectively. The range of average pairwise percent agreement across subspecialties for the final diagnosis and top 3 differential diagnosis was 59%-98% and 23%-49%, respectively. Conclusion ChatGPT achieved the best results when the most up-to-date model (GPT-4) was used and when it was prompted for a single diagnosis. Hallucination frequency was lower with GPT-4 than with GPT-3.5, but repeatability was an issue for both models. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Chang in this issue.

Comparing Diagnostic Accuracy of Radiologists versus GPT-4V and Gemini Pro Vision Using Image Inputs from Diagnosis Please Cases.

Background The diagnostic abilities of multimodal large language models (LLMs) using direct image inputs and the impact of the temperature parameter of LLMs remain unexplored. Purpose To investigate the ability of GPT-4V and Gemini Pro Vision in generating differential diagnoses at different temperatures compared with radiologists using Radiology Diagnosis Please cases. Materials and Methods This retrospective study included Diagnosis Please cases published from January 2008 to October 2023. Input images included original images and captures of the textual patient history and figure legends (without imaging findings) from PDF files of each case. The LLMs were tasked with providing three differential diagnoses, repeated five times at temperatures 0, 0.5, and 1. Eight subspecialty-trained radiologists solved cases. An experienced radiologist compared generated and final diagnoses, considering the result correct if the generated diagnoses included the final diagnosis after five repetitions. Accuracy was assessed across models, temperatures, and radiology subspecialties, with statistical significance set at P < .007 after Bonferroni correction for multiple comparisons across the LLMs at the three temperatures and with radiologists. Results A total of 190 cases were included in neuroradiology (n = 53), multisystem (n = 27), gastrointestinal (n = 25), genitourinary (n = 23), musculoskeletal (n = 17), chest (n = 16), cardiovascular (n = 12), pediatric (n = 12), and breast (n = 5) subspecialties. Overall accuracy improved with increasing temperature settings (0, 0.5, 1) for both GPT-4V (41% [78 of 190 cases], 45% [86 of 190 cases], 49% [93 of 190 cases], respectively) and Gemini Pro Vision (29% [55 of 190 cases], 36% [69 of 190 cases], 39% [74 of 190 cases], respectively), although there was no evidence of a statistically significant difference after Bonferroni adjustment (GPT-4V, P = .12; Gemini Pro Vision, P = .04). The overall accuracy of radiologists (61% [115 of 190 cases]) was higher than that of Gemini Pro Vision at temperature 1 (T1) (P < .001), while no statistically significant difference was observed between radiologists and GPT-4V at T1 after Bonferroni adjustment (P = .02). Radiologists (range, 45%-88%) outperformed the LLMs at T1 (range, 24%-75%) in most subspecialties. Conclusion Using direct radiologic image inputs, GPT-4V and Gemini Pro Vision showed improved diagnostic accuracy with increasing temperature settings. Although GPT-4V slightly underperformed compared with radiologists, it nonetheless demonstrated promising potential as a supportive tool in diagnostic decision-making. © RSNA, 2024 See also the editorial by Nishino and Ballard in this issue.

CETARS/CAR Practice Guideline on Imaging the Pregnant Trauma Patient.

CETARS/CAR Practice Guideline on Imaging the Pregnant Trauma Patient.

ResearchConnect.info: An Interactive Web-Based Platform for Building Academic Collaborations

ResearchConnect.info: An Interactive Web-Based Platform for Building Academic Collaborations

The Applications of Artificial Intelligence in Radiology: Opportunities and Challenges

Purpose: This article aims to provide insight and a better understanding of how the rapid development of artificial intelligence (AI) affects radiology practice and research. The article reviews existing scientific literature on the applications of AI in radiology and the opportunities and challenges they pose. Materials and Methods: This article uses available scientific literature on AI applications in radiology and its subspecialties from PubMed, Google Scholar and ScienceDirect. Results: The article finds that the applications of AI in radiology have grown significantly in the past decade, spanning across virtually all radiology subspecialties or areas of activity and all modalities of imaging such as the radiographer, computer tomography (CT) scan, magnetic resonance imaging (MRI), ultrasound and others. The AI applications in radiology present challenges related to testing and validation, professional uptake, and education and training. Nevertheless, artificial intelligence provides an opportunity for greater innovation in the field, improved accuracy, reduced burden of radiologists and better patient care among others. Conclusions: Despite the challenges it presents, artificial intelligence provides many worthwhile opportunities for the development of radiology and the next frontier in medicine.