Large language models (LLMs) in radiology exams for medical students: Performance and consequences.

The evolving field of medical education is being shaped by technological advancements, including the integration of Large Language Models (LLMs) like ChatGPT. These models could be invaluable resources for medical students, by simplifying complex concepts and enhancing interactive learning by providing personalized support. LLMs have shown impressive performance in professional examinations, even without specific domain training, making them particularly relevant in the medical field. This study aims to assess the performance of LLMs in radiology examinations for medical students, thereby shedding light on their current capabilities and implications.This study was conducted using 151 multiple-choice questions, which were used for radiology exams for medical students. The questions were categorized by type and topic and were then processed using OpenAI's GPT-3.5 and GPT- 4 via their API, or manually put into Perplexity AI with GPT-3.5 and Bing. LLM performance was evaluated overall, by question type and by topic.GPT-3.5 achieved a 67.6% overall accuracy on all 151 questions, while GPT-4 outperformed it significantly with an 88.1% overall accuracy (p<0.001). GPT-4 demonstrated superior performance in both lower-order and higher-order questions compared to GPT-3.5, Perplexity AI, and medical students, with GPT-4 particularly excelling in higher-order questions. All GPT models would have successfully passed the radiology exam for medical students at our university.In conclusion, our study highlights the potential of LLMs as accessible knowledge resources for medical students. GPT-4 performed well on lower-order as well as higher-order questions, making ChatGPT-4 a potentially very useful tool for reviewing radiology exam questions. Radiologists should be aware of ChatGPT's limitations, including its tendency to confidently provide incorrect responses. · ChatGPT demonstrated remarkable performance, achieving a passing grade on a radiology examination for medical students that did not include image questions.. · GPT-4 exhibits significantly improved performance compared to its predecessors GPT-3.5 and Perplexity AI with 88% of questions answered correctly.. · Radiologists as well as medical students should be aware of ChatGPT's limitations, including its tendency to confidently provide incorrect responses.. · Gotta J, Le Hong QA, Koch V et al. Large language models (LLMs) in radiology exams for medical students: Performance and consequences. Fortschr Röntgenstr 2024; DOI 10.1055/a-2437-2067.

Malignancy predictors and treatment strategies for adult intestinal intussusception.

Intussusception in adult patients is a rare medical finding, which is accompanied by an underlying tumor in some cases. However, no accepted method has been established to identify patients at risk for tumor-related intussusception. This study aimed to identify imaging features as predictors for tumor-related intussusception.CT images of patients with confirmed intussusception were retrospectively acquired between 01/2008 and 12/2022. Available follow-up images and medical health records were evaluated to identify various imaging features, the cause of intussusception, and treatment strategies. Imaging interpretation was conducted by two blinded radiologists. A third radiologist was consulted in cases of disagreement.A total of 71 consecutive patients were included in this study (42 males, 29 females) with a median age of 56 years (interquartile range: 40.5-73.8 years). Enteroenteric intussusceptions in the small bowel were the most common type observed in adult patients. In contrast, colocolic intussusception was more frequently associated with malignancy, and this association was statistically significant (p < 0.05). Among the malignant tumors, adenocarcinoma was the most common, followed by metastases and lymphoma. Additionally, bowel obstruction and wall thickening were significantly correlated with malignancy (p < 0.05). The high negative predictive values (NPVs) and high specificities for ileus (NPV 88.5%, specificity 82.1%), bowel wall thickening (NPV 90.9%, specificity 71.4%), and acute abdomen (NPV 84.6%, specificity 78.8%) suggest that the absence of these features strongly predicts a low probability of malignancy in cases of adult intussusception.Active surveillance with follow-up exams is suitable for asymptomatic and transient intussusception when imaging features suggest a low likelihood of a neoplasm. Additionally, malignancy predictors such as ileus and thickening of the bowel wall in the affected segment could guide tailored treatment. Surgical interventions are essential for symptomatic cases, with adenocarcinoma being the most common malignancy found in colocolic intussusceptions.Intussusception in adults is rare and is often associated with underlying tumors, particularly in colocolic intussusceptions. Key imaging predictors for malignancy include bowel obstruction, wall thickening in the affected segment, and the presence of acute abdomen, with high NPVs and specificities indicating low malignancy risk when these features are absent. Active surveillance is recommended for asymptomatic cases with low neoplasm probability, while surgical intervention is the method of choice for symptomatic patients. · Reschke P, Le Hong QA, Gruenewald LD et al. Malignancy predictors and treatment strategies for adult intestinal intussusception. Fortschr Röntgenstr 2024; DOI 10.1055/a-2434-7932.

From pixels to prognosis: Imaging biomarkers for discrimination and outcome prediction of pulmonary embolism : Original Research Article.

PURPOSE: Recent advancements in medical imaging have transformed diagnostic assessments, offering exciting possibilities for extracting biomarker-based information. This study aims to investigate the capabilities of a machine learning classifier that incorporates dual-energy computed tomography (DECT) radiomics. The primary focus is on discerning and predicting outcomes related to pulmonary embolism (PE). METHODS: The study included 131 participants who underwent pulmonary artery DECT angiography between January 2015 and March 2022. Among them, 104 patients received the final diagnosis of PE and 27 patients served as a control group. A total of 107 radiomic features were extracted for every case based on DECT imaging. The dataset was divided into training and test sets for model development and validation. Stepwise feature reduction identified the most relevant features, which were used to train a gradient-boosted tree model. Receiver operating characteristics analysis and Cox regression tests assessed the association of texture features with overall survival. RESULTS: The trained machine learning classifier achieved a classification accuracy of 0.94 for identifying patients with acute PE with an area under the receiver operating characteristic curve of 0.91. Radiomics features could be valuable for predicting outcomes in patients with PE, demonstrating strong prognostic capabilities in survival prediction (c-index, 0.991 [0.979-1.00], p = 0.0001) with a median follow-up of 130 days (IQR, 38-720). Notably, the inclusion of clinical or DECT parameters did not enhance predictive performance. CONCLUSION: In conclusion, our study underscores the promising potential of leveraging radiomics on DECT imaging for the identification of patients with acute PE and predicting their outcomes. This approach has the potential to improve clinical decision-making and patient management, offering efficiencies in time and resources by utilizing existing DECT imaging without the need for an additional scoring system.

Indicators for Hospitalization in Acute Pulmonary Embolism: Uncover the Association Between D-dimer Levels, Thrombus Volume and Radiomics.

BACKGROUND: The advent of advanced computed tomography (CT) technology and the field of radiomics has opened up new avenues in diagnostic assessments. Increasingly, there is substantial evidence advocating for the incorporation of quantitative imaging biomarkers in the clinical decision-making process. This study aimed to examine the correlation between D-dimer levels and thrombus size in acute pulmonary embolism (PE) combining dual-energy CT (DECT) and radiomics and to investigate the diagnostic utility of a machine learning classifier based on dual-energy computed tomography (DECT) radiomics for identifying patients with a complicated course, defined as at least hospitalization at IMC. METHODS: The study was conducted including 136 participants who underwent pulmonary artery CT angiography from January 2015 to March 2022. Based on DECT imaging, 107 radiomic features were extracted for each patient using standardized image processing. After dividing the dataset into training and test sets, stepwise feature reduction based on reproducibility, variable importance and correlation analyses were performed to select the most relevant features; these were used to train and validate the gradient-boosted tree models.Receiver operating characteristics (ROC) analysis was utilized to evaluate the association between volumetric, laboratory data and adverse outcomes. RESULTS: In the central PE group, we observed a significant correlation between thrombus volumetrics and D-dimer levels (p = 0.0037), as well as between thrombus volumetrics and hospitalization at the Intermediate Care Unit (IMC) (p = 0.0001). In contrast, no statistically significant differences were identified in thrombus sizes between patients who experienced complications and those who had a favorable course (p = 0.3162). The trained machine learning classifier achieved an accuracy of 61% and 55% in identifying patients with a complicated course, as indicated by an area under the ROC curve of 0.63 and 0.58. CONCLUSION: In conclusion, our findings indicate a positive correlation between D-dimer levels and central PE's pulmonary embolic burden. Thrombus volumetrics may serve as an indicator for complications and outcomes in acute PE patients. Thus, thrombus volumetrics, as opposed to D-dimers, could be an additional marker for evaluating embolic disease severity. Moreover, DECT-derived radiomic feature models show promise in identifying patients with a complicated course, such as hospitalization at IMC.

New Perspectives for Mucolytic, Anti-inflammatory and Adjunctive Therapy with 1,8-Cineole in COPD and Asthma: Review on the New Therapeutic Approach.

The mucolytic monoterpene 1,8-cineole (eucalyptol), the major constituent of eucalyptus species, is well known for its anti-inflammatory, antioxidant, bronchodilatory, antiviral and antimicrobial effects. The main protective antiviral, anti-inflammatory and mucolytic mechanisms of 1,8-cineole are the induction of interferon regulatory factor 3 (IRF3), the control of nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) along with decreasing mucin genes (MUC2, MUC19). In normal human monocytes direct inhibition was shown of reactive oxygen species (ROS)-mediated mucus hypersecretion and of steroid resistence inducing superoxides (O2·-) and pro-inflammatory hydrogen peroxides (H2O2) with partial control of superoxide dismutase (SOD), which enzymatically metabolizes O2·- into H2O2. By inhibition of NF-κB, 1,8-cineole, at relevant plasma concentrations (1.5 µg/ml), strongly and significantly inhibited in normal human monocyte lipopolysaccharide (LPS)-stimulated cytokines relevant for exacerbation (tumour necrosis factor alpha (TNFα), interleukin (IL)-1ß and systemic inflammation (IL-6, IL-8). Infectious agents and environmental noxa have access via TNFα and IL-1ß to the immune system with induction of bronchitis complaints and exacerbations of chronic obstructive pulmonary disease (COPD), asthma and asthma-COPD overlap. In lymphocytes from healthy human donors 1,8-cineole inhibited TNFα, IL-1ß, IL-4 and IL-5 and demonstrated for the first time control of Th1/2-type inflammation. 1,8-Cineole at relevant plasma levels increased additively in vitro the efficacy of inhaled guideline medications of budesonide (BUD) and budesonide + formoterol ,and preliminary data also showed increased efficacy of long-acting muscarinic receptor antagonist (LAMA)-mediated cytokine inhibition in vitro. On the basis of the preclinical data, earlier randomised controlled studies with adjunctive therapy of 1,8-cineole (3 × 200 mg/day) for 6 months showed improvement of uncontrolled asthma by significant improvement of lung function, nocturnal asthma and quality of life scores and in COPD decrease of exacerbations (- 38.5%) (during wintertime). This review reports an update with reference to the literature of 1,8-cineole, also as adjunctive therapy, as a therapeutic agent for the protection and control of inflammatory airway diseases.