Current Problems in Diagnostic Radiology最新文献

{"title":"Ergonomic considerations for the modern radiology practice: An update","authors":"Samer Soussahn MD ,&nbsp;Kenneth Buckwalter MD ,&nbsp;Rony Sayegh MD ,&nbsp;Steven Soliman DO ,&nbsp;William Weadock MD ,&nbsp;Kara Gaetke-Udager MD","doi":"10.1067/j.cpradiol.2024.07.005","DOIUrl":"10.1067/j.cpradiol.2024.07.005","url":null,"abstract":"<div><p>The average post-pandemic modern radiology practice is experiencing an ever-increasing workload volume with overall relatively similar staffing levels, regardless of practice setting. This has resulted in an increased workload demand for the average diagnostic radiologist, which in many cases translates to longer working hours. It is now more important than ever to be cognizant of various work-related injuries, including repetitive-stress injuries and vision-related ailments as examples, in relation to the working conditions of the radiologist. This article will discuss commonly occurring conditions and ergonomic considerations that the radiologist can employ to reduce the risk of work-related injuries.</p></div>","PeriodicalId":51617,"journal":{"name":"Current Problems in Diagnostic Radiology","volume":"53 6","pages":"Pages 738-744"},"PeriodicalIF":1.5,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0363018824001154/pdfft?md5=4006c0222185e0de5218ad54c5197761&pid=1-s2.0-S0363018824001154-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141604645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Flipping the script: A multiyear assessment and comparison of flipped classroom versus traditional didactic teaching at two academic institutions","authors":"Michelle Ho MD ,&nbsp;Rishi Agrawal MD ,&nbsp;Diana Lam MD ,&nbsp;Jason Hartman MD ,&nbsp;Claire K. Sandstrom MD ,&nbsp;Kevin Chorath MD ,&nbsp;Puneet Bhargava MD ,&nbsp;Lei Wu MD","doi":"10.1067/j.cpradiol.2024.07.008","DOIUrl":"10.1067/j.cpradiol.2024.07.008","url":null,"abstract":"<div><h3>Rationale and objectives</h3><div>Historically radiology resident education has taken the form of workstation and didactic teaching. Due to increasing clinical demand and administrative burden for academic radiologists, the need for more efficient and effective teaching has increased. Flipped classroom teaching, where trainees independently learn material prior to interactive teaching sessions with faculty, is a possible alternative. While the use of flipped teaching in radiology has been studied in the medical student setting, its use in the radiology residency setting has been less published.</div></div><div><h3>Materials and methods</h3><div>At two academic institutions (University of Washington and Northwestern), exam scores from five PGY-2 Core rotations were collected. Flipped teaching was used for one rotation at the University of Washington (FR). The influence of teaching method, rotation, and institution on exam score was examined. Resident surveys were also collected to understand perceptions of flipped classroom teaching.</div></div><div><h3>Results</h3><div>At the University of Washington, the mean exam score for the flipped rotation was significantly higher than the majority of other rotations utilizing traditional teaching (p&lt;0.05). Between the University of Washington and Northwestern, there was no difference in exam scores when comparing comparable rotations. Among residents at the University of Washington, the flipped teaching rotation was perceived as more educationally valuable than traditional teaching rotations.</div></div><div><h3>Conclusion</h3><div>Flipped classroom teaching is at least as effective as the traditional teaching model and associated with better performance on standardized exams at one institution. Among residents, flipped learning is also associated with higher perceived educational value.</div></div>","PeriodicalId":51617,"journal":{"name":"Current Problems in Diagnostic Radiology","volume":"54 2","pages":"Pages 238-241"},"PeriodicalIF":1.5,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141636227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of information delivery techniques in reducing pre-procedural anxiety in computed tomography","authors":"H Shafeeq Ahmed,&nbsp;Deeksha Gupta,&nbsp;Deepika Reddy Aluru,&nbsp;Rohit Nellaiappan,&nbsp;T Arul Dasan","doi":"10.1067/j.cpradiol.2024.07.015","DOIUrl":"10.1067/j.cpradiol.2024.07.015","url":null,"abstract":"<div><h3>Objectives</h3><p>Patients undergoing medical procedures often experience heightened anxiety, which can affect their experience and overall health. The current study aimed at looking at a quality improvement initiative to compare written and audiovisual information delivery methods to reduce anxiety prior to Computed Tomography (CT).</p></div><div><h3>Methods</h3><p>In this prospective interventional study, we assessed state and trait anxiety in patients scheduled for their first CT scan. Three PDSA cycles were carried out over six months, with each cycle lasting for two months each. The participants were divided into three groups, the baseline, written, and audiovisual intervention groups. Anxiety levels were assessed using the State-Trait Anxiety Inventory (STAI) questionnaire. State anxiety is a temporary emotional response, while trait anxiety reflects enduring personality characteristics.</p></div><div><h3>Results</h3><p>The mean age of participants was 43.26 years (SD 15.07) in the baseline group, 39.9 years (SD 14.72) in the written group, and 48.59 years (SD 13.54) in the audiovisual group. For state anxiety, the baseline mean was 58.4 (SD 6.9), notably reduced to 43.2 (SD 5.5) with written intervention and to 38.6 (SD 7.7) with audiovisual intervention (<em>p</em> &lt; 0.001). Trait anxiety scores remained relatively stable in all groups (<em>p</em> = 0.31).</p></div><div><h3>Conclusion</h3><p>Both written and audiovisual interventions successfully alleviate pre-imaging anxiety in patients undergoing CT scans. The findings underscore the superior efficacy of audiovisual materials in achieving a more substantial reduction in state anxiety compared to written information. These findings are particularly relevant in resource limited settings where simple interventions show significant improvements.</p></div>","PeriodicalId":51617,"journal":{"name":"Current Problems in Diagnostic Radiology","volume":"53 6","pages":"Pages 723-727"},"PeriodicalIF":1.5,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141636226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gender and race in radiology: An intersectional analysis of the American Association of Medical Colleges (AAMC) database from 1966 to 2021","authors":"Ishraq Joarder HBSc ,&nbsp;Shukria Ahmadi HBSc ,&nbsp;Jeffrey Ding ,&nbsp;Faisal Khosa MD, MBA, TI, FFRRCSI, FRCPC, FACR","doi":"10.1067/j.cpradiol.2024.07.013","DOIUrl":"10.1067/j.cpradiol.2024.07.013","url":null,"abstract":"<div><h3>Purpose</h3><div>To describe trends in gender and racial representation within academic radiology in the United States over a 55-year period from 1966 to 2021.</div></div><div><h3>Methods</h3><div>A retrospective analysis of the American Association of Medical Colleges database of radiology faculty members from 1966 to 2021 was conducted. Trends in academic rank and tenure status for five different ethnoracial groups (White, Asian, Black, Hispanic, and Native American) each subdivided into two gender identities (men and women) were analyzed. The proportional change over time of each demographic group per outcome was analyzed with linear regression models.</div></div><div><h3>Results</h3><div>Between 1966 and 2021, White men comprised the largest demographic in academic radiology each year. A significant decrease in both White individuals (−0.51 % per year) and men (−0.30 % per year) were observed over the study period. Additionally, the only demographic groups with meaningful changes in representation were White women (+0.20 % per year) and Asian men (+0.23 % per year), followed by Asian women in later years. Underrepresented minority groups (Black, Hispanic and Native American) collectively comprised less than 5 % of all academic radiologists. Finally, within each demographic group, the representation of women consistently lagged behind men. Despite these relative increases, in 2021, the majority of US academic radiologists were White (59.1 %) and male (70.1 %).</div></div><div><h3>Conclusion</h3><div>Historically underrepresented minorities, especially women within these groups, continue to face discrimination and are consistently the least represented in radiology. Initiatives that incorporate the intersectionality framework are imperative to break this cycle.</div></div>","PeriodicalId":51617,"journal":{"name":"Current Problems in Diagnostic Radiology","volume":"54 2","pages":"Pages 191-196"},"PeriodicalIF":1.5,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141694170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of Hepatocellular Carcinoma Surveillance with Contrast-enhanced MRI in a High-Risk Western European Cohort","authors":"Federico I.F. Fiduzi MD ,&nbsp;François E.J.A. Willemssen MD ,&nbsp;Céline van de Braak MSc ,&nbsp;Quido G. de Lussanet de la Sablonière MD, PhD ,&nbsp;Jan N.M. IJzermans MD, PhD ,&nbsp;Daniel Bos MD, PhD ,&nbsp;Robert A. de Man MD, PhD ,&nbsp;Roy S. Dwarkasing MD, PhD","doi":"10.1067/j.cpradiol.2024.07.001","DOIUrl":"10.1067/j.cpradiol.2024.07.001","url":null,"abstract":"<div><h3>Aim</h3><p>To investigate the utilization of MRI using a MRI liver protocol with extracellular contrast-enhanced series for hepatocellular carcinoma (HCC) surveillance in high-risk patients.</p></div><div><h3>Methods</h3><p>Consecutive high-risk patients of a western European cohort who underwent repeated liver MRI for HCC screening were included. Lesions were registered according to the Liver Reporting &amp; Data System (LIRADS) 2018. HCC was staged as very early stage HCC (BCLC stage 0) and more advanced stages of HCC (BCLC stage A-D). Differences in time interval between MRI's for BCLC stage 0 and stage A-D were calculated with the Mann-Whitney U test. The HCC cumulative incidence at one-, three- and five years was calculated with the Kaplan Meier estimator.</p></div><div><h3>Results</h3><p>From 2010 to 2019 a total of 240 patients were included (71% male; median age: 57 years; IQR: 50-64 years) with 1350 MRI's. Most patients (83 %) had cirrhosis with hepatitis C as the most common underlying cause. Patients underwent on average four MRI's (IQR: 3-7). Forty-two patients (17.5%) developed HCC (52 HCC lesions: 43 LIRADS-5, eight LIRADS-4, and one LIRADS-TIV). Eighteen patients (43%) had BCLC stage 0 HCC with a significant shorter screening time interval (10 months; IQR: 6-21) compared to patients with BCLC stage A-D (21 months; IQR: 10-32) (p = 0.03). Thirty seven percent of patients with a LIRADS-3 lesion (n=43) showed HCC development within twelve months (median: 7.4 months). One, three- and five-year HCC cumulative incidence in cirrhotic patients was 1%, 10% and 17%, respectively.</p></div><div><h3>Conclusion</h3><p>High-risk patients who underwent surveillance with contrast-enhanced MRI developed HCC in 17.5 % during a follow up period of over 4 years median. Very early stage HCC was seen in compensated cirrhosis after a median time interval of 10 months. Later stages of HCC were related to prolonged screening time interval (median 21 months).</p></div>","PeriodicalId":51617,"journal":{"name":"Current Problems in Diagnostic Radiology","volume":"53 6","pages":"Pages 709-716"},"PeriodicalIF":1.5,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0363018824001129/pdfft?md5=6a0b5700244a00089ae25b4abfad353a&pid=1-s2.0-S0363018824001129-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141604646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computed tomography (CT) derived radiomics to predict post-operative disease recurrence in gastric cancer; a systematic review and meta-analysis","authors":"Niall J. O'Sullivan ,&nbsp;Hugo C. Temperley ,&nbsp;Michelle T. Horan ,&nbsp;Benjamin M. Mac Curtain ,&nbsp;Maeve O'Neill ,&nbsp;Claire Donohoe ,&nbsp;Narayanasamy Ravi ,&nbsp;Alison Corr ,&nbsp;James F.M. Meaney ,&nbsp;John V. Reynolds ,&nbsp;Michael E. Kelly","doi":"10.1067/j.cpradiol.2024.07.011","DOIUrl":"10.1067/j.cpradiol.2024.07.011","url":null,"abstract":"<div><h3>Introduction</h3><p>Radiomics offers the potential to predict oncological outcomes from pre-operative imaging in order to identify ‘high risk’ patients at increased risk of recurrence. The application of radiomics in predicting disease recurrence provides tailoring of therapeutic strategies. We aim to comprehensively assess the existing literature regarding the current role of radiomics as a predictor of disease recurrence in gastric cancer.</p></div><div><h3>Methods</h3><p>A systematic search was conducted in Medline, EMBASE, and Web of Science databases. Inclusion criteria encompassed retrospective and prospective studies investigating the use of radiomics to predict post-operative recurrence in ovarian cancer. Study quality was assessed using the QUADAS-2 and Radiomics Quality Score tools.</p></div><div><h3>Results</h3><p>Nine studies met the inclusion criteria, involving a total of 6,662 participants. Radiomic-based nomograms demonstrated consistent performance in predicting disease recurrence, as evidenced by satisfactory area under the receiver operating characteristic curve values (AUC range 0.72 - 1). The pooled AUCs calculated using the inverse-variance method for both the training and validation datasets were 0.819 and 0.789 respectively</p></div><div><h3>Conclusion</h3><p>Our review provides good evidence supporting the role of radiomics as a predictor of post-operative disease recurrence in gastric cancer. Included studies noted good performance in predicting their primary outcome. Radiomics may enhance personalised medicine by tailoring treatment decision based on predicted prognosis.</p></div>","PeriodicalId":51617,"journal":{"name":"Current Problems in Diagnostic Radiology","volume":"53 6","pages":"Pages 717-722"},"PeriodicalIF":1.5,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141713994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cybersecurity in radiology: Cautionary Tales, Proactive Prevention, and What to do When You Get Hacked","authors":"Xuan V. Nguyen MD, PhD ,&nbsp;Jonelle M. Petscavage-Thomas MD, MPH ,&nbsp;Christopher M. Straus MD ,&nbsp;Ichiro Ikuta MD, MMSc","doi":"10.1067/j.cpradiol.2024.07.010","DOIUrl":"10.1067/j.cpradiol.2024.07.010","url":null,"abstract":"<div><div>To improve awareness and understanding of cybersecurity threats to radiology practice and better equip healthcare practices to manage cybersecurity risks associated with medical imaging, this article reviews topics related to cybersecurity in healthcare, with emphasis on common vulnerabilities in radiology operations. This review is intended to assist radiologists and radiology administrators who are not information technology specialists to attain an updated overview of relevant cybersecurity concepts and concerns relevant to safe and effective practice of radiology and provides a succinct reference for individuals interested in learning about imaging-related vulnerabilities in healthcare settings. As cybersecurity incidents have become increasingly common in healthcare, we first review common cybersecurity threats in healthcare and provide updates on incidence of healthcare data breaches, with emphasis on the impact to radiology. Next, we discuss practical considerations on how to respond to a healthcare data breach, including notification and disclosure requirements, and elaborate on a variety of technical, organizational, and individual actions that can be adopted to minimize cybersecurity risks applicable to radiology professionals and administrators. While emphasis is placed on specific vulnerabilities within radiology workflow, many of the preventive or mitigating strategies are also relevant to cybersecurity within the larger digital healthcare arena. We anticipate that readers, upon completing this review article, will gain a better appreciation of cybersecurity issues relevant to radiology practice and be better equipped to mitigate cybersecurity risks associated with medical imaging.</div></div>","PeriodicalId":51617,"journal":{"name":"Current Problems in Diagnostic Radiology","volume":"54 2","pages":"Pages 245-250"},"PeriodicalIF":1.5,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141604644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simplifying risk stratification for thyroid nodules on ultrasound: validation and performance of an artificial intelligence thyroid imaging reporting and data system","authors":"Benjamin Wildman-Tobriner ,&nbsp;Jichen Yang ,&nbsp;Brian C. Allen ,&nbsp;Lisa M. Ho ,&nbsp;Chad M. Miller ,&nbsp;Maciej A. Mazurowski","doi":"10.1067/j.cpradiol.2024.07.006","DOIUrl":"10.1067/j.cpradiol.2024.07.006","url":null,"abstract":"<div><h3>Purpose</h3><p>To validate the performance of a recently created risk stratification system (RSS) for thyroid nodules on ultrasound, the Artificial Intelligence Thyroid Imaging Reporting and Data System (AI TI-RADS).</p></div><div><h3>Materials and methods</h3><p>378 thyroid nodules from 320 patients were included in this retrospective evaluation. All nodules had ultrasound images and had undergone fine needle aspiration (FNA). 147 nodules were Bethesda V or VI (suspicious or diagnostic for malignancy), and 231 were Bethesda II (benign). Three radiologists assigned features according to the AI TI-RADS lexicon (same categories and features as the American College of Radiology TI-RADS) to each nodule based on ultrasound images. FNA recommendations using AI TI-RADS and ACR TI-RADS were then compared and sensitivity and specificity for each RSS were calculated.</p></div><div><h3>Results</h3><p>Across three readers, mean sensitivity of AI TI-RADS was lower than ACR TI-RADS (0.69 vs 0.72, p &lt; 0.02), while mean specificity was higher (0.40 vs 0.37, p &lt; 0.02). Overall total number of points assigned by all three readers decreased slightly when using AI TI-RADS (5,998 for AI TI-RADS vs 6,015 for ACR TI-RADS), including more values of 0 to several features.</p></div><div><h3>Conclusion</h3><p>AI TI-RADS performed similarly to ACR TI-RADS while eliminating point assignments for many features, allowing for simplification of future TI-RADS versions.</p></div>","PeriodicalId":51617,"journal":{"name":"Current Problems in Diagnostic Radiology","volume":"53 6","pages":"Pages 695-699"},"PeriodicalIF":1.5,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0363018824001105/pdfft?md5=fe14e9d3f34bb79cf92b3bf4aa953afd&pid=1-s2.0-S0363018824001105-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141712372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Increasing magic number and other trends in diagnostic radiology NRMP match data","authors":"Trenton Taros MD,&nbsp;Christopher Thomas Zoppo MD,&nbsp;Anthony Michael Camargo BA,&nbsp;Carolynn Michelle DeBenedectis MD","doi":"10.1067/j.cpradiol.2024.07.014","DOIUrl":"10.1067/j.cpradiol.2024.07.014","url":null,"abstract":"<div><div>The magic number, or number of ranks needed to achieve a greater than 90 % chance of matching, has not been investigated for diagnostic radiology (DR). Somewhat reflective of a field's changing competitiveness, this individual metric can be useful for reassuring applicants or identifying a need to reach out to mentors. The NRMP's Charting Outcomes in the Match was accessed over the previous 10 cycles to assess changes to magic number and other match-related metrics. Over the last 10 cycles, there has been an increase in magic number for prospective radiologists. Based on the most 2022 recent report, the magic number was 14 compared to 5 and 2 in 2014 and 2016 respectively. Compared to the average US MD senior, those applying into DR were significantly more likely to match in 2014, 2016 and 2020 (p &lt; 0.01 for all), and significantly less likely to match in 2018 and 2022 (p = 0.03 and p &lt; 0.01, respectively). This trend has had important consequences for applicants and programs as the incentive to apply more widely grows. The increasing magic number demonstrates increasing competitiveness in the field, which might be due to a positive job market, changing medical student preferences, or increased access to radiology electives and mentors. The 2024 Charting Outcomes document will be the first to include data from a class almost entirely affected by the change to a pass/fail Step1 and the new preference signaling supplement. It is currently unclear how either change will affect the overall competitiveness of the field and the magic number.</div></div>","PeriodicalId":51617,"journal":{"name":"Current Problems in Diagnostic Radiology","volume":"54 2","pages":"Pages 242-244"},"PeriodicalIF":1.5,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141636229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ChatGPT and assistive AI in structured radiology reporting: A systematic review","authors":"Ethan Sacoransky BSc ,&nbsp;Benjamin Y.M. Kwan MD ,&nbsp;Donald Soboleski MD","doi":"10.1067/j.cpradiol.2024.07.007","DOIUrl":"10.1067/j.cpradiol.2024.07.007","url":null,"abstract":"<div><h3>Introduction</h3><p>The rise of transformer-based large language models (LLMs), such as ChatGPT, has captured global attention with recent advancements in artificial intelligence (AI). ChatGPT demonstrates growing potential in structured radiology reporting—a field where AI has traditionally focused on image analysis.</p></div><div><h3>Methods</h3><p>A comprehensive search of MEDLINE and Embase was conducted from inception through May 2024, and primary studies discussing ChatGPT's role in structured radiology reporting were selected based on their content.</p></div><div><h3>Results</h3><p>Of the 268 articles screened, eight were ultimately included in this review. These articles explored various applications of ChatGPT, such as generating structured reports from unstructured reports, extracting data from free text, generating impressions from radiology findings and creating structured reports from imaging data. All studies demonstrated optimism regarding ChatGPT's potential to aid radiologists, though common critiques included data privacy concerns, reliability, medical errors, and lack of medical-specific training.</p></div><div><h3>Conclusion</h3><p>ChatGPT and assistive AI have significant potential to transform radiology reporting, enhancing accuracy and standardization while optimizing healthcare resources. Future developments may involve integrating dynamic few-shot prompting, ChatGPT, and Retrieval Augmented Generation (RAG) into diagnostic workflows. Continued research, development, and ethical oversight are crucial to fully realize AI's potential in radiology.</p></div>","PeriodicalId":51617,"journal":{"name":"Current Problems in Diagnostic Radiology","volume":"53 6","pages":"Pages 728-737"},"PeriodicalIF":1.5,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0363018824001130/pdfft?md5=e3006ef32f98fe95868a662fec5d5a58&pid=1-s2.0-S0363018824001130-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141617846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}