Radiology Training Research Articles

The Role of Plain Radiography in Assessing Aborted Foetal Musculoskeletal Anomalies in Everyday Practice.

Conventional radiography is widely used for postmortem foetal imaging, but its role in diagnosing congenital anomalies is debated. This study aimed to assess the effectiveness of X-rays in detecting skeletal abnormalities and guiding genetic analysis and counselling. This is a retrospective analysis of all post-abortion diagnostic imaging studies conducted at a centre serving a population of over 300,000 inhabitants from 2008 to 2023. The data were analysed using descriptive statistics. X-rays of 81 aborted foetuses (total of 308 projections; mean: 3.8 projections/examination; SD: 1.79) were included. We detected 137 skeletal anomalies. In seven cases (12.7%), skeletal anomalies identified through radiology were missed by prenatal sonography. The autopsy confirmed radiological data in all cases except for two radiological false positives. Additionally, radiology failed to identify a case of syndactyly, which was revealed by anatomopathology. X-ray is crucial for accurately classifying skeletal abnormalities, determining the causes of spontaneous abortion, and guiding the request for genetic counselling. Formal training for both technicians and radiologists, as well as multidisciplinary teamwork, is necessary to perform X-ray examinations on aborted foetuses and interpret the results effectively.

Analysis about radiation risk perception among university students from San Carlos de Bariloche and radiological protection experts from Argentina

Radiation risk perception must be studied by communication and radiological protection specialists, taking account of engineering, social and cultural variables. An anonymous and voluntary survey was peformed to enquire about the radiation risk perception among university students and radiological protection experts from Argentina, and to become aware of how it is perceived compared to other health risks. A fifteen-question dedicated questionnaire was designed including socio-demographic variables and questions about work environment, risk perception and risk communication. A five-point Likert-type scale was used for most of the questions. Surveyed people were asked to compare radiological risks in medicine with other risks (i.e.: smoking, sports, leisure activities, stress). The research involved 10 (38.5%) experts, 25 (45%) physics and engineering students and 40 (100%) bio-images production bachelor students. The results were analysed based on the professional or academic background. Although participants considered the radiation exposure in medicine as a low-risk activity, the results showed a disagreement about the radiation risk perception in particular situations (i.e.: living near a nuclear power plant, training in radiological protection while at college) and about radiation risk communication strategies. There is neither right nor wrong risk perception; however, a lot of work must be done to build an agreement between radiological risk perception and risk assessment due to radiation exposure in medicine. It should include working on the design of public policies focused on radiological protection training for health staff and clinical research training for medical physicists and radiological protection specialists, while strengthening communication skills and channels.

A National Survey of Interventional Radiology Trainees on Peripheral and Aortic Endovascular Training.

The development of technical skills in peripheral and aortic endovascular intervention is an essential part of interventional radiology (IR) training. Access to endovascular training remains contentious, with IR and vascular surgery (VS) trainees competing for opportunities. The Collaborative Peripheral and Aortic Endovascular Training Survey (CPAETS) aimed to evaluate IR trainees' experiences, expectations, and barriers to endovascular training. CPAETS was a joint survey between the British Society of Interventional Radiology and the Rouleaux Club (UK Vascular Trainees' Association), open for 12 weeks and distributed to UK-based IR and VS trainees. This article focuses on IR trainee responses. Thirty-two responses were received from IR trainees across England, Scotland and Wales. Overall, 59% of respondents were satisfied with their endovascular training. IR trainees reported less regular hands-on experience of aortic endovascular procedures (50%) compared to peripheral endovascular procedures (93%). Consequently fewer trainees (65%) felt confident in achieving the necessary aortic endovascular competencies by the end of their training, compared to peripheral procedures (89%). Limited exposure to aortic endovascular procedures resulted in reduced confidence levels in performing aortic intervention as compared to peripheral procedures. Potential solutions to bridge some of these IR training gaps include greater pre-operative and post-operative presence, the use of simulators and IR fellowships to ensure adequate training opportunities. This article provides a snapshot of the current gaps in IR endovascular training in the UK, with insight into solutions that can enable trainees to develop clinical and technical competencies required for IR consultant practice.

Women in Radiology: Challenges and Opportunities.

Improving the status of women in radiology is crucial to better work environments. There is strong evidence in the business world that women leaders improve the workplace by making it more financially viable and by increasing collaboration, job satisfaction, and engagement. Diverse leadership fosters innovation, and women approach problem-solving with unique insights and collaborative styles. Gender diversity in leadership correlates with improved patient outcomes because women leaders prioritize patient-centered care and communication. Women create sustainable, productive work and improve radiology. Women serve as powerful role models, inspiring the next generation of women in radiology and addressing gender disparities. Increasing women leaders in radiology is essential to increase the number of women in radiology. This article summarizes many challenges women face when taking leadership roles: organizational biases prioritizing male viewpoints and marginalizing women's voices and contributions, a lack of role models, a lack of time ("second shift"), a lack of confidence, a lack of interest or perceived benefit, a lack of support, burnout, and previous poor experiences. While systemic issues are difficult to overcome, this article assists in the training and development of women radiologists by offering strategies to enhance job satisfaction and bring new and valuable perspectives to leadership.

Neph-ex: a 3D printed interventional radiology training tool for nephrostomy exchange.

Fluoroscopic guided procedures are a mainstay for interventional radiology (IR) procedures. Practice is needed for the novice to interpret fluoroscopic images and simultaneously perform the procedure hands-on as well as control the foot pedal to screen. We describe the development of a training simulation model which simulates the human kidney, ureter, and bladder. Stereolithography (SLA) 3D print technology using SLA resin and Anycubic SLA printer were employed. A plastic tubing was used to connect the 3D printed kidney and bladder as the ureter. This simulation model permits fluoroscopic guided filling of "pelvicalyceal system" with contrast as well as ureteric stenting, guidewire, and drainage catheter manipulation. Effectiveness of the model to attain skills for nephrostomy exchange and ureteric stenting was obtained via questionnaire from trainees prior to and after utilizing the model. The 3D printing simulation model of the kidney, ureter, and bladder system enables trainees to perform nephrostomy exchange, nephrostogram, and antegrade stenting. Participants felt more confident to perform the procedures as they were more familiar with the procedure. Besides that, participants felt their wire and catheter manipulation skills have improved after using the simulation model. Neph-ex simulation model is safe and effective for hands-on training in improving proficiency of fluoroscopy-guided nephrostomy exchange and antegrade ureteric stenting. As of our knowledge, there are no commercially available simulation models for fluoroscopic guided nephrostomy exchange and ureteric stent deployment. No article on nephrostomy exchange simulation model has been published before.

ThP6.4 - Psoas Sarcopaenia as a Predictor of Mortality in Patients Over 65 with PR Bleeding: A Retrospective Case Controlled Study

Abstract Aims Sarcopaenia has been examined as a predictor for functional baseline and frailty in surgical patients and may predict mortality. The hypothesis of this study is that sarcopaenia is associated with increased risk of death after per rectal (PR) bleeding. Methods Patients over 65 who were admitted between January 2017 and December 2022, having had an episode of PR bleeding, that underwent cross sectional imaging were identified from the Southern health and social care trust database. Cross sectional imaging was accessed via PACS and total psoas area (TPA) calculated at the third lumbar vertebra by a radiology trainee and validated by a consultant radiologist. Clinical, mortality and demographic data was obtained from Northern Ireland Electronic Care Record (NIECR). Psoas muscle index (PMI) was then calculated by TPA and standardising for height in mm2/m2. Sarcopaenia was defined as PMI male < 545 mm2/m2 female < 385 mm2/m2. Results 77 Patients were included in this study. Patients were excluded based on the ability to standardise TPA for height. 27% (n 21) of patients were identified as sarcopaenic, 66% (n 14) of these patients have died. The median PMI showed differences between female mortality (490.44 mm2/m2) and non-mortality (559.49 mm2/m2) groups. Male patients also showed lower median PMI between mortality (599.79 mm2/m2) and non-mortality (643.05 mm2/m2 ) groups. Conclusions Sarcopaenia as a predictor for frailty is an important risk factor for all-cause mortality in PR bleeding. Cross-sectional imaging provides an opportunity to quantify risk as well as investigate aetiology for PR bleeding. Although radiation risk and cost effectiveness may limit use, it should be considered when imaging is available.

Development of an online authentic radiology viewing and reporting platform to test the skills of radiology trainees in Low- and Middle-Income Countries

Development of an online authentic radiology viewing and reporting platform to test the skills of radiology trainees in Low- and Middle-Income Countries

Evaluating current acute aortic syndrome pathways: Collaborative Acute Aortic Syndrome Project (CAASP)

Abstract Background Diagnosis of acute aortic syndrome is challenging and associated with high perihospital mortality rates. The study aim was to evaluate current pathways and understand the chronology of acute aortic syndrome patient care. Method Consecutive patients with acute aortic syndrome imaging diagnosis between 1 January 2018 and 1 June 2021 were identified using a predetermined search strategy and followed up for 6 months through retrospective case note review. The UK National Interventional Radiology Trainee Research and Vascular and Endovascular Research Network co-ordinated the study. Results From 15 UK sites, 620 patients were enrolled. The median age was 67 (range 25–98) years, 62.0% were male and 92.9% Caucasian. Type-A dissection (41.8%) was most common, followed by type-B (34.5%); 41.2% had complicated acute aortic syndrome. Mode of presentation included emergency ambulance (80.2%), self-presentation (16.2%), and primary care referral (3.6%). Time (median (i.q.r.)) to hospital presentation was 3.1 (1.8–8.6) h and decreased by sudden onset chest pain but increased with migratory pain or hypertension. Time from hospital presentation to imaging diagnosis was 3.2 (1.3–6.5) h and increased by family history of aortic disease and decreased by concurrent ischaemic limb. Time from diagnosis to treatment was 2 (1.0–4.3) h with interhospital transfer causing delay. Management included conservative (60.2%), open surgery (32.2%), endovascular (4.8%), hybrid (1.4%) and palliative (1.4%). Factors associated with a higher mortality rate at 30 days and 6 months were acute aortic syndrome type, complicated disease, no critical care admission and age more than 70 years (P < 0.05). Conclusions This study presents a longitudinal data set linking time-based delays to diagnosis and treatment with clinical outcomes. It can be used to prioritize research strategies to streamline patient care.

Decision Support System Based on Deep Learning with Big Data for Pneumonia Prediction

A bacterial, viral, or fungal infection of the lungs causes pneumonia in humans. It is the primary cause of global decline. Around 27% of deaths worldwide are attributable to this illness. A World Health Organisation report states that this serious infectious disease claims the lives of almost 2.5 million people annually. Early detection of chest illness, including pneumonia, can reduce the likelihood that the condition will worsen by allowing for the proper course of treatment. Accurate CT scans and CXR (chest X-ray) reports are renowned for being exact methods of diagnosis and prediction. This approach is trustworthy and non-invasive, and it can be used as a screening tool when a disease diagnosis is necessary. Nevertheless, research has demonstrated that even highly trained radiologists regularly fail to correctly identify pneumonia disease in its early stages. The severity of the sickness and the death rate increase as a result of diagnostic delays. Machine learning has affected every aspect of human life in the twenty-first century. It has a significant effect on the medical field in terms of early diagnosis of serious illnesses. Therefore, an intelligent health care system based on artificial intelligence (AI) was presented in this work for the early and accurate diagnosis of pneumonia disease.

Choosing a Career after Radiology Training: Academic, Private Practice, or Industry?

Choosing a Career after Radiology Training: Academic, Private Practice, or Industry?

Generating Synthetic Data for Medical Imaging.

Artificial intelligence (AI) models for medical imaging tasks, such as classification or segmentation, require large and diverse datasets of images. However, due to privacy and ethical issues, as well as data sharing infrastructure barriers, these datasets are scarce and difficult to assemble. Synthetic medical imaging data generated by AI from existing data could address this challenge by augmenting and anonymizing real imaging data. In addition, synthetic data enable new applications, including modality translation, contrast synthesis, and professional training for radiologists. However, the use of synthetic data also poses technical and ethical challenges. These challenges include ensuring the realism and diversity of the synthesized images while keeping data unidentifiable, evaluating the performance and generalizability of models trained on synthetic data, and high computational costs. Since existing regulations are not sufficient to guarantee the safe and ethical use of synthetic images, it becomes evident that updated laws and more rigorous oversight are needed. Regulatory bodies, physicians, and AI developers should collaborate to develop, maintain, and continually refine best practices for synthetic data. This review aims to provide an overview of the current knowledge of synthetic data in medical imaging and highlights current key challenges in the field to guide future research and development.

Applications of artificial intelligence in computed tomography imaging for phenotyping pulmonary hypertension.

Pulmonary hypertension is a heterogeneous condition with significant morbidity and mortality. Computer tomography (CT) plays a central role in determining the phenotype of pulmonary hypertension, informing treatment strategies. Many artificial intelligence tools have been developed in this modality for the assessment of pulmonary hypertension. This article reviews the latest CT artificial intelligence applications in pulmonary hypertension and related diseases. Multistructure segmentation tools have been developed in both pulmonary hypertension and nonpulmonary hypertension cohorts using state-of-the-art UNet architecture. These segmentations correspond well with those of trained radiologists, giving clinically valuable metrics in significantly less time. Artificial intelligence lung parenchymal assessment accurately identifies and quantifies lung disease patterns by integrating multiple radiomic techniques such as texture analysis and classification. This gives valuable information on disease burden and prognosis. There are many accurate artificial intelligence tools to detect acute pulmonary embolism. Detection of chronic pulmonary embolism proves more challenging with further research required. There are numerous artificial intelligence tools being developed to identify and quantify many clinically relevant parameters in both pulmonary hypertension and related disease cohorts. These potentially provide accurate and efficient clinical information, impacting clinical decision-making.

Proposed changes to pediatric radiology training and certification: report of the alternate training working group.

Proposed changes to pediatric radiology training and certification: report of the alternate training working group.

Optimizing mammography interpretation education: leveraging deep learning for cohort-specific error detection to enhance radiologist training.

Accurate interpretation of mammograms presents challenges. Tailoring mammography training to reader profiles holds the promise of an effective strategy to reduce these errors. This proof-of-concept study investigated the feasibility of employing convolutional neural networks (CNNs) with transfer learning to categorize regions associated with false-positive (FP) errors within screening mammograms into categories of "low" or "high" likelihood of being a false-positive detection for radiologists sharing similar geographic characteristics. Mammography test sets assessed by two geographically distant cohorts of radiologists (cohorts A and B) were collected. FP patches within these mammograms were segmented and categorized as "difficult" or "easy" based on the number of readers committing FP errors. Patches outside 1.5 times the interquartile range above the upper quartile were labeled as difficult, whereas the remaining patches were labeled as easy. Using transfer learning, a patch-wise CNN model for binary patch classification was developed utilizing ResNet as the feature extractor, with modified fully connected layers for the target task. Model performance was assessed using 10-fold cross-validation. Compared with other architectures, the transferred ResNet-50 achieved the highest performance, obtaining receiver operating characteristics area under the curve values of 0.933 ( ) and 0.975 ( ) on the validation sets for cohorts A and B, respectively. The findings highlight the feasibility of employing CNN-based transfer learning to predict the difficulty levels of local FP patches in screening mammograms for specific radiologist cohort with similar geographic characteristics.

Contrast enhanced ultrasound of liver lesions in patients treated for childhood malignancies

BackgroundPatients treated for cancer have a higher incidence of focal liver lesions than the general population and there is often concern for a malignant etiology. This can result in patient, caregiver and physician anxiety and is managed by a “wait and watch” approach, or immediate additional imaging, or biopsy, depending on the degree of clinical concern. Because it is a low-cost, easily accessible, radiation and sedation free modality, we investigated the value of contrast enhanced ultrasound (CEUS) to accurately distinguish benign from malignant liver lesions in patients treated for childhood malignancies.MethodsWe performed an IRB approved retrospective study of 68 subjects who were newly diagnosed, on treatment or off treatment for a pediatric malignancy and had liver lesions discovered on CT, MRI or non-contrast ultrasound and subsequently underwent CEUS between September 2013 and September 2021. Two experienced pediatric radiologists and a radiology trainee, blinded to the etiology of the liver lesions, independently reviewed the CEUS examinations and categorized lesions as benign, indeterminate, or malignant. The reference standard was biopsy for 19 lesions and clinical follow-up for 49. The sensitivity, specificity, positive and negative predictive value, and diagnostic accuracy of CEUS were calculated using only the benign and malignant CEUS classifications. Inter-reviewer agreement was assessed by Cohen’s kappa statistic.ResultsThere were 26 males and 42 females, mean age, 14.9 years (range, 1–52 years). Fifty subjects were off therapy, twelve receiving treatment, and six with newly diagnosed cancer. By the reference standard, 59 (87%) lesions were benign and 9 (13%) were malignant. Sensitivities of CEUS for the three reviewers ranged from 83 to 100% (95% CI, 35.9-100%), specificities from 93.1 to 96.0% (95% CI, 83.5-99.6%), PPV 60.0-71.4% (95% CI, 29.0-96.3%), NPV 98.0-100% (95% CI, 89.2-100%) and accuracy from 93.8 to 94.6% (95% CI, 85.1-99.7%). The kappa statistic for agreement between the two experienced radiologists was moderate at 0.58.ConclusionsCEUS is highly accurate in distinguishing benign from malignant etiologies of liver lesions in patients treated for pediatric malignancies.

Female interns are not choosing radiology as a career-national survey providing insights into gender imbalance.

Women are under-represented in Radiology, both globally and in Ireland. An annual review of the medical workforce in Ireland for the year 2021-2022 revealed that although the overall gender breakdown of trainees is similar, with 56% female and 44% male trainees, certain specialities have disproportionate numbers of a given gender. Females only account for 38% of Irish Radiology trainees, one of the lowest of all the specialties. The cause for this disparity is likely multifactorial, but a lack of interest inapplying for Radiology training by female doctors is a possible cause. The objectives of this study were to identify specific factors that attract or deter intern doctors from considering a career in Radiology and to identify underlying gender-specific differences. Anonymous online surveys were distributed to 50% of the intern population in Ireland between May and June 2022. The survey included questions on demographics, prior radiology exposure, and dedicated multiple-choice questions for those either considering or not considering radiology, which were subdivided into potentially influencing factors. Two-hundred-seven interns responded giving a response rate of 48.3%, which totalled almost 25% of the interns in Ireland. For those interns considering radiology, significantly more male interns (n = 24/67, 35.8%) are considering radiology compared with females (n = 26/139, 18.71%), (p < 0.009). Significantly more females were deterred by the perception of radiology as a male-based speciality (p < 0.004). Significantly fewer females are considering radiology, deterred by physics knowledge, use of technology, and, significantly, by the perception of radiology as a male-dominated speciality. This survey highlights important reasons that female interns are deterred from radiology. It will help direct future medical training and doctor recruitment policies, with a view to addressing the ongoing gender disparity in the Irish radiology workforce. Women are under-represented in the Irish and global Radiology workforce. Female interns are dissuaded from Radiology as a career by their perception of Radiology as male-dominated. This survey will shape medical training and doctor recruitment policies to improve gender diversity in radiology.

Performance of two different artificial intelligence (AI) methods for assessing carpal bone age compared to the standard Greulich and Pyle method.

Evaluate the agreement between bone age assessments conducted by two distinct machine learning system and standard Greulich and Pyle method. Carpal radiographs of 225 patients (mean age 8years and 10months, SD = 3 years and 1month) were retrospectively analysed at two separate institutions (October 2018 and May 2022) by both expert radiologists and radiologists in training as well as by two distinct AI software programmes, 16-bit AItm and BoneXpert® in a blinded manner. The bone age range estimated by the 16-bit AItm system in our sample varied between 1year and 1month and 15years and 8 months (mean bone age 9years and 5months SD = 3years and 3 months). BoneXpert® estimated bone age ranged between 8months and 15years and 7months (mean bone age 8years and 11months SD = 3years and 3months). The average bone age estimated by the Greulich and Pyle method was between 11months and 14years, 9months (mean bone age 8years and 4 months SD = 3years and 3 months). Radiologists' assessments using the Greulich and Pyle method were significantly correlated (Pearson's r > 0.80, p < 0.001). There was no statistical difference between BoneXpert® and 16-bit AItm (mean difference = - 0.19, 95%CI = (- 0.45; 0.08)), and the agreement between two measurements varies between - 3.45 (95%CI = (- 3.95; - 3.03) and 3.07 (95%CI - 3.03; 3.57). Both AI methods and GP provide correlated results, although the measurements made by AI were closer to each other compared to the GP method.

From Revisions to Insights: Converting Radiology Report Revisions into Actionable Educational Feedback Using Generative AI Models.

Expert feedback on trainees' preliminary reports is crucial for radiologic training, but real-time feedback can be challenging due to non-contemporaneous, remote reading and increasing imaging volumes. Trainee report revisions contain valuable educational feedback, but synthesizing data from raw revisions is challenging. Generative AI models can potentially analyze these revisions and provide structured, actionable feedback. This study used the OpenAI GPT-4 Turbo API to analyze paired synthesized and open-source analogs of preliminary and finalized reports, identify discrepancies, categorize their severity and type, and suggest review topics. Expert radiologists reviewed the output by grading discrepancies, evaluating the severity and category accuracy, and suggested review topic relevance. The reproducibility of discrepancy detection and maximal discrepancy severity was also examined. The model exhibited high sensitivity, detecting significantly more discrepancies than radiologists (W = 19.0, p < 0.001) with a strong positive correlation (r = 0.778, p < 0.001). Interrater reliability for severity and type were fair (Fleiss' kappa = 0.346 and 0.340, respectively; weighted kappa = 0.622 for severity). The LLM achieved a weighted F1 score of 0.66 for severity and 0.64 for type. Generated teaching points were considered relevant in ~ 85% of cases, and relevance correlated with the maximal discrepancy severity (Spearman ρ = 0.76, p < 0.001). The reproducibility was moderate to good (ICC (2,1) = 0.690) for the number of discrepancies and substantial for maximal discrepancy severity (Fleiss' kappa = 0.718; weighted kappa = 0.94). Generative AI models can effectively identify discrepancies in report revisions and generate relevant educational feedback, offering promise for enhancing radiology training.

The Effect of Time Pressure on Motion Economy and Smoothness of Interventional Radiology Trainee Performance in Simulated Central Venous Line Placement.

To evaluate the effect of being under time pressure on procedural performance using hand motion analysis. Eight radiology trainees performed central venous access on a phantom while recording video and hand motion data using an electromagnetic motion tracker. Each trainee performed the procedure six times: the first three trials without any prompts (control), while for the next three, they were asked to perform the task as fast as possible (time pressure). Validated hand motion metrics were analyzed, and two blinded and independent evaluators rated procedural performance using a previously validated task-specific global rating scale (GRS). Motion/time ratios and linear mixed-effect methods were used to control for time, and constants for both strategies were compared. Hand motion analysis showed that trainees completed the simulated procedure faster under time pressure (46 ± 18s vs. 56 ± 27s, p = 0.008) than during the control strategy. However, when controlling for time, trainees moved their hands 79 more centimeters (p = 0.04), made 15 more translational movements (p = 0.003) and 18 more rotational movements (p = 0.01) when under time pressure compared to at their own pace. Although trainees could perform the procedure faster under time pressure, there was a deterioration in hand motion economy and smoothness. This suggests that hand motion metrics offer a more comprehensive assessment of technical performance than time alone.

Enhancing Radiology Training with GPT-4: Pilot Analysis of Automated Feedback in Trainee Preliminary Reports

Rationale and ObjectivesRadiology residents often receive limited feedback on preliminary reports issued during independent call. This study aimed to determine if Large Language Models (LLMs) can supplement traditional feedback by identifying missed diagnoses in radiology residents’ preliminary reports. Materials & MethodsA randomly selected subset of 500 (250 train/250 validation) paired preliminary and final reports between 12/17/2022 and 5/22/2023 were extracted and de-identified from our institutional database. The prompts and report text were input into the GPT-4 language model via the GPT-4 API (gpt-4-0314 model version). Iterative prompt tuning was used on a subset of the training/validation sets to direct the model to identify important findings in the final report that were absent in preliminary reports. For testing, a subset of 10 reports with confirmed diagnostic errors were randomly selected. Fourteen residents with on-call experience assessed the LLM-generated discrepancies and completed a survey on their experience using a 5-point Likert scale. ResultsThe model identified 24 unique missed diagnoses across 10 test reports with i% model prediction accuracy as rated by 14 residents. Five additional diagnoses were identified by users, resulting in a model sensitivity of 79.2%. Post-evaluation surveys showed a mean satisfaction rating of 3.50 and perceived accuracy rating of 3.64 out of 5 for LLM-generated feedback. Most respondents (71.4%) favored a combination of LLM-generated and traditional feedback. ConclusionThis pilot study on the use of LLM-generated feedback for radiology resident preliminary reports demonstrated notable accuracy in identifying missed diagnoses and was positively received, highlighting LLMs' potential role in supplementing conventional feedback methods.