Meta-Radiology最新文献

{"title":"Assessment of Myocardial Stiffness in Healthy Adults by Magnetic Resonance Elastography: Effects of Slice Location and Demographic Factors","authors":"Yong Cheng ,&nbsp;Shutian An ,&nbsp;Zhen Wang ,&nbsp;Yewen Jiang ,&nbsp;Zijun Zheng ,&nbsp;Zhanglei Li ,&nbsp;Jun Wang ,&nbsp;Jun Chen ,&nbsp;Yongqiang Yu ,&nbsp;Ren Zhao ,&nbsp;Xiaohu Li","doi":"10.1016/j.metrad.2026.100220","DOIUrl":"10.1016/j.metrad.2026.100220","url":null,"abstract":"<div><h3>Purpose</h3><div>To establish normal myocardial stiffness values in healthy adults using cardiac MR elastography (MRE) and assess the impact of slice location and demographic factors.</div></div><div><h3>Methods</h3><div>125 healthy volunteers underwent cardiac MRE using a 140-Hz vibration frequency, with data acquired during early systole (approximately 100 ms after the R-wave). Stiffness was measured in basal, mid, and apical short-axis views. Statistics included repeated-measures ANOVA (slice locations), t-tests (sex), Spearman correlation (age), and multivariable linear regression (controlling for age, sex, body mass index, and body surface area).</div></div><div><h3>Results</h3><div>Global myocardial stiffness was 8.1 ± 1.1 kPa. A significant apical-to-basal gradient existed (apex: 8.7 ± 1.1, mid: 8.3 ± 1.2, base: 7.4 ± 1.3 kPa; all <em>P</em> &lt; 0.05). Males had non-significantly higher global stiffness than females (8.3 ± 1.0 vs. 8.0 ± 1.1 kPa, <em>P</em> = 0.068). Stiffness correlated with age in both sexes (<em>r</em> = 0.48 in male, <em>r</em> = 0.42 in female, both <em>P</em> &lt; 0.05). Multivariable regression confirmed age as an independent predictor (<em>β</em> = 0.037 kPa/year, <em>P</em> &lt; 0.001) with no age-sex interaction (<em>P</em> = 0.929). Intra-/inter-observer and scan-rescan reproducibility were excellent (ICCs ≥ 0.97).</div></div><div><h3>Conclusion</h3><div>This study establishes that myocardial stiffness in healthy adults follows an apical-to-basal gradient and is independently influenced by age, providing location-specific baseline stiffness values for the clinical interpretation of cardiac MR elastography.</div></div><div><h3>delete this paragraph--Advances in knowledge</h3><div>This study establishes comprehensive, location-specific normal ranges for myocardial stiffness across the entire left ventricle (apex, mid, base) in healthy adults, addressing a key gap in the existing CMRE literature which has been largely confined to mid-ventricular data. It demonstrates the significant impact of slice location on stiffness values, revealing an apical-to-basal gradient. These findings underscore the necessity of specifying and standardizing slice location in future CMRE protocols for accurate interpretation and comparison, both in research and potential clinical applications. The study also provides a robust, confounder-adjusted analysis confirming the independent effect of age on myocardial stiffness.</div></div>","PeriodicalId":100921,"journal":{"name":"Meta-Radiology","volume":"4 2","pages":"Article 100220"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178647","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":"Toward vibe medicine: A self-evolving multi-agent framework for clinical decision support","authors":"Qianxue Zhang ,&nbsp;Yiming Ren ,&nbsp;Shihuan Qin ,&nbsp;Xiao Zhang ,&nbsp;Liao Zhang ,&nbsp;Jinyang Huang ,&nbsp;Zhengliang Liu ,&nbsp;Chenbin Liu ,&nbsp;Hongying Feng ,&nbsp;Jingyuan Chen ,&nbsp;Yuzhen Ding ,&nbsp;Weihang You ,&nbsp;Hanqi Jiang ,&nbsp;Yi Pan ,&nbsp;Yifan Zhou ,&nbsp;Junhao Chen ,&nbsp;Lifeng Chen ,&nbsp;Wei Liu ,&nbsp;Tianming Liu ,&nbsp;Zengren Zhao ,&nbsp;Lian Zhang","doi":"10.1016/j.metrad.2026.100223","DOIUrl":"10.1016/j.metrad.2026.100223","url":null,"abstract":"<div><div>In recent years, the advances of large language models and autonomous agents have revolutionized the healthcare field, facilitating diagnosis and improving treatment results. However, most existing AI systems rely on pre-trained knowledge and predefined pipelines, which struggle to learn dynamically from the interactive chat session history that contains patient outcomes and past failures. To address this limitation, we propose VIBEMed, a multi-agent framework with a built-in self-evolution mechanism and architecture-level safety sandbox for robust clinical decision support. The system integrates three specialized agents, including a Clinical Diagnostic Agent (CDA) for hypothesis generation, a Therapeutic Execution Agent (TEA) for treatment planning, and a Clinical Evolution Manager Agent (CEMA) that distills longitudinal clinical feedback into reusable knowledge, transforming multimodal patient information into personalized medical decisions. Through self-evolution mechanism, the framework enables iterative updates across memory, model behavior, and decision strategies, allowing the system to improve over time. Experimental results show that VIBEMed demonstrates superior performance through its evolving mechanism in complex clinical cases, particularly in tasks that require integrated decision-making and longitudinal planning. The framework also supports reliable end-to-end decisions in challenging scenarios such as oncology treatment planning, highlighting its feasibility in real-world clinical contexts. Overall, VIBEMed provides a practical path beyond static AI systems toward adaptive, experience-driven clinical decision support, demonstrating the value of combining multi-agent collaboration with continuous evolution for advancing precision medicine.</div></div>","PeriodicalId":100921,"journal":{"name":"Meta-Radiology","volume":"4 2","pages":"Article 100223"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178673","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":"MLLIFA: Multi-level learning and interactive fusion algorithm combined with large foundation models for Alzheimer's disease diagnosis and etiology extraction","authors":"Jinxiong Fang ,&nbsp;Da-fang Zhang ,&nbsp;Kun Xie ,&nbsp;Luyun Xu ,&nbsp;Zhaoxu Xing ,&nbsp;Xia-an Bi","doi":"10.1016/j.metrad.2026.100217","DOIUrl":"10.1016/j.metrad.2026.100217","url":null,"abstract":"<div><div>The joint analysis of macro-level imaging and micro-level genetic information facilitates a holistic understanding of the pathological processes underlying Alzheimer's disease (AD). Although recent studies have made advances, most current approaches struggle to exploit the critical features embedded in imaging genetics data and fail to effectively reveal biological interactions. To remedy these shortcomings, this paper proposes a multi-level learning and interactive fusion framework based on large foundation models (LFMs), and accordingly develops an algorithm termed MLLIFA for the diagnosis of AD and the extraction of etiology. Specifically, MLLIFA first employs two LFMs to construct high-quality representations of brain region and gene features. Then, two sparse attention mechanisms are applied to extract key information from the constructed features. Finally, interaction learning is utilized to explore the latent relationships between features within biological contexts, guiding the effective fusion of multi-omics information. Experimental results on the ADNI dataset demonstrate that MLLIFA achieves an AD classification accuracy of 91.22%, outperforming state-of-the-art methods. Moreover, the proposed MLLIFA successfully identifies disease-related brain regions, risk genes, and brain-gene associations. These findings not only provide strong support for the precise diagnosis of AD but also offer new insights into the extraction of etiology.</div></div>","PeriodicalId":100921,"journal":{"name":"Meta-Radiology","volume":"4 2","pages":"Article 100217"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178674","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":"Cross-scale prediction of glioblastoma MGMT methylation status based on deep learning combined with magnetic resonance images and pathology images","authors":"Xusha Wu ,&nbsp;Wei Wei ,&nbsp;Yan Li ,&nbsp;Menghang Ma ,&nbsp;Zhenyuan Hu ,&nbsp;Yongqiang Xu ,&nbsp;Wenzhong Hu ,&nbsp;Gang Chen ,&nbsp;Rui Zhao ,&nbsp;Xiaowei Kang ,&nbsp;Xiaoliang Zhang ,&nbsp;Hong Yin ,&nbsp;Yibin Xi","doi":"10.1016/j.metrad.2025.100201","DOIUrl":"10.1016/j.metrad.2025.100201","url":null,"abstract":"<div><h3>Background</h3><div>The methylation status of the O⁶-methylguanine-DNA methyltransferase (MGMT) promoter is a key predictive biomarker for chemotherapy response in glioblastoma (GBM). Current reliance on complex molecular testing necessitates the development of alternative predictive methods for postoperative decision support during the waiting period. Although both preoperative MRI and histopathological images contain valuable biological information, their combined potential for predicting MGMT status remains unexplored. We aimed to develop and validate a deep learning radiopathomics model (DLRPM) that integrates MRI and pathological images for predicting MGMT promoter methylation.</div></div><div><h3>Methods</h3><div>A retrospective collection of pathologically confirmed isocitrate dehydrogenase (IDH) wild-type GBM patients (n = 207) from three centers was performed, all of whom underwent MRI scanning within 2 weeks prior to surgery. The pre-trained ResNet50 was used as the feature extractor. Features of 1024 dimensions were extracted from MRI and pathological images, respectively, and the features were screened for modeling. Then feature fusion was performed by calculating the normalized multimode MRI fusion features and pathological features, and prediction models of MGMT based on deep learning radiomics, pathomics, and radiopathomics (DLRM, DLPM, DLRPM) were constructed and applied to internal and external validation cohorts.</div></div><div><h3>Results</h3><div>In the training, internal and external validation cohorts, the DLRPM further improved the predictive performance, with a significantly better predictive performance than the DLRM and DLPM, with AUCs of 0.920 (95 % CI 0.870–0.968), 0.854 (95 % CI 0.702–1.000), and 0.840 (95 % CI 0.625–1.000) and the corresponding accuracy was 83.2 %, 82.1 % and 80.0 %, respectively. The AUCs of DLRM were 0.786, 0.771, and 0.600 respectively, with accuracy rates of 67.3 %, 67.9 %, and 60.0 % respectively. The AUCs of DLPM were 0.864, 0.844, and 0.780 respectively, with accuracy rates of 79.6 %, 78.6 %, and 66.7 % respectively.</div></div><div><h3>Conclusion</h3><div>By integrating MRI and histopathological images, the DLRPM narrows the diagnostic gap created by the waiting period for molecular testing, offering a practical approach to predicting MGMT methylation status. The model's robust performance across validation cohorts demonstrates its potential as a practical clinical tool to supplement or reduce reliance on invasive tissue sampling, thereby aiding in personalized treatment planning for GBM patients. Future studies will focus on prospective validation and explore its utility in predicting other molecular markers and treatment outcomes.</div></div>","PeriodicalId":100921,"journal":{"name":"Meta-Radiology","volume":"4 1","pages":"Article 100201"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147424563","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":"Carotid DECTA in patients with dental hardware: Impact of deep learning image reconstruction and metal artifact reduction","authors":"Yuncheng Li,&nbsp;Yong Cheng,&nbsp;Rong He,&nbsp;Yanzimeng Ye,&nbsp;Zhanglei Li,&nbsp;Liucheng Li,&nbsp;Shuangshuang Xu,&nbsp;Zijun Zheng,&nbsp;Yewen Jiang,&nbsp;Yongqiang Yu,&nbsp;Xiaohu Li","doi":"10.1016/j.metrad.2025.100188","DOIUrl":"10.1016/j.metrad.2025.100188","url":null,"abstract":"<div><h3>Purpose</h3><div>To evaluate the effectiveness of deep learning image reconstruction (DLIR) with metal artifact reduction (MAR) (DLIR-MAR) for improving carotid dual-energy CT angiography (DECTA) in patients with dental hardware.</div></div><div><h3>Methods</h3><div>This retrospective study included 49 patients with dental hardware who underwent carotid DECTA. Virtual monochromatic images (VMIs) were reconstructed with DLIR-MAR, adaptive statistical iterative reconstruction-Veo (ASIR-V), and ASIR-V with MAR (ASIRV-MAR) at 40-keV and 50-keV energy levels. We quantitatively compared image noise, CT attenuation, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and artifact index (AI) among different reconstructions, and performed a qualitative assessment of internal carotid artery (ICA) visualization affected by metal artifacts and overall image quality using a 5-point scale. Statistical analyses used repeated measures ANOVA or Friedman tests, with paired t-tests or Wilcoxon signed-rank tests as appropriate.</div></div><div><h3>Results</h3><div>Both ASIRV-MAR and DLIR-MAR reduced metal artifacts compared with ASIR-V, with lower AI and higher vascular visualization scores (<em>p</em> &lt; 0.001). Compared to ASIRV-MAR, DLIR-MAR reduced image noise at both keV levels (<em>P</em> &lt; 0.001). DLIR-MAR 40-keV VMIs exhibited comparable image noise (<em>p</em> &gt; 0.05), higher CT attenuation, SNR, CNR, and overall image quality compared with ASIRV-MAR 50-keV VMIs (<em>p</em> &lt; 0.05). Although DLIR-MAR 40-keV VMIs had higher AI values than ASIRV-MAR 50-keV VMIs (<em>p</em> &lt; 0.001), this did not significantly affect ICA visualization (<em>p</em> <em>&gt;</em> 0.05).</div></div><div><h3>Conclusions</h3><div>DLIR reconstructed with MAR improves carotid DECTA images in patients with dental hardware, reducing image noise in lower-keV VMIs to capture their contrast enhancement benefits.</div></div>","PeriodicalId":100921,"journal":{"name":"Meta-Radiology","volume":"4 1","pages":"Article 100188"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147424567","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":"Diagnostic accuracy of artificial intelligence models in ultrasound-based detection of abdominal trauma: A systematic review and meta-analysis","authors":"Helvécio N. Feitosa Filho ,&nbsp;João Filipe Cavalcante Uchoa Furtado ,&nbsp;Eduardo Correia Eulálio ,&nbsp;Pedro Vianna Caldas Ribeiro ,&nbsp;Felipe Cavalcanti Dias ,&nbsp;Lucas Macêdo Aurélio Paiva ,&nbsp;Rian Vilar Lima ,&nbsp;Débora Vianna dos Santos","doi":"10.1016/j.metrad.2025.100189","DOIUrl":"10.1016/j.metrad.2025.100189","url":null,"abstract":"<div><h3>Purpose</h3><div>Abdominal trauma detection via ultrasound, particularly through Focused Assessment with Sonography for Trauma (FAST), is a cornerstone of emergency medicine due to its portability, real-time imaging and applicability in unstable patients. Artificial intelligence (AI) models have been used to enhance diagnostic accuracy and reduce operator dependency. Conduct a systematic review and meta-analysis to evaluate the effectiveness of AI models in ultrasound-based detection of abdominal trauma.</div></div><div><h3>Methods</h3><div>A search was carried out across MEDLINE, Embase, and Cochrane databases in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Diagnostic Test Accuracy (PRISMA-DTA) 2018 guidelines. Diagnostic studies that assessed the precision of AI in ultrasound-based detection of abdominal trauma, particularly FAST, were included.</div></div><div><h3>Results</h3><div>The analysis included five studies with 209,577 frames evaluated across all included studies. The pooled analysis of AI models demonstrated a sensitivity of 90.7 % (95 % CI: 79.1–96.2 %; I² = 99 %), specificity of 96.9 % (95 % CI: 95.1–98.0 %; I² = 89 %), and an area under the curve (AUC) of 0.979 (95 % CI: 0.964–0.983).</div></div><div><h3>Conclusion</h3><div>This study suggests that AI models may demonstrate promising sensitivity and specificity in ultrasound-based detection of abdominal trauma, with an overall AUC of 0.979. However, given the limited number of studies and sample sizes included, these findings should be considered preliminary, highlighting the need for further validation in larger and more diverse cohorts.</div></div>","PeriodicalId":100921,"journal":{"name":"Meta-Radiology","volume":"4 1","pages":"Article 100189"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147424562","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":"The role of pain catastrophizing in modulating irritable bowel syndrome symptoms through brain-spinal cord integration","authors":"Chenxi Wang ,&nbsp;Ping Chen ,&nbsp;Zilong Hong ,&nbsp;Shujun Wang ,&nbsp;Jia Huang ,&nbsp;Zhaoxing Wei ,&nbsp;Mengjiao Ge ,&nbsp;Yazhuo Kong ,&nbsp;Kai Yuan ,&nbsp;Jixin Liu","doi":"10.1016/j.metrad.2025.100187","DOIUrl":"10.1016/j.metrad.2025.100187","url":null,"abstract":"<div><h3>Background</h3><div>Irritable bowel syndrome (IBS) is a prevalent disorder characterized by abnormal brain-gut interactions. Notable, psychosocial stressors have been consistently observed to increase susceptibility to additional physical symptoms of IBS. However, the neuropsychological mechanism underlining this disordered top-down control by the central nervous system remains unclear.</div></div><div><h3>Methods</h3><div>To address this issue, we investigated gray matter characteristics in the brain and spinal cord of 53 IBS patients and 35 healthy controls (HCs) using simultaneous three-dimensional T1-weighted cortico-spinal imaging. All participants had their IBS symptom severity and pain-related negative emotions quantified using a comprehensive clinical and psychosocial symptom questionnaire battery. We used partial least-squares correlation analysis to assess the associations from complex, heterogeneous variable sets.</div></div><div><h3>Results</h3><div>Our results showed that gut-brain dysregulation may be related to abnormal changes in brain gray matter volumes, particularly in superior frontal gyrus, middle frontal gyrus and precentral gyrus. Mediation analyses indicated that pain catastrophizing is associated with these gray matter abnormalities, which may be linked to more severe physical symptoms of IBS. Additionally, the gray matter properties in segments C5 to C6 of the spinal cord serve as serial mediators in the relationship between the top-down brain control and gut function, influencing abdominal pain severity.</div></div><div><h3>Conclusions</h3><div>Our study provides novel insights into the top-down control mechanisms contributing to IBS symptoms, emphasizing the potential benefits of early intervention for pain catastrophizing.</div></div>","PeriodicalId":100921,"journal":{"name":"Meta-Radiology","volume":"4 1","pages":"Article 100187"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147424564","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":"Human knowledge-enhanced large language model agent for prediction of intestinal disease progression in patients with Crohn's disease: A multicenter retrospective study","authors":"Zhoulei Li ,&nbsp;Feifan He ,&nbsp;Ruonan Zhang ,&nbsp;Shanshan Zhu ,&nbsp;Qinxian Chen ,&nbsp;Shuntian Wu ,&nbsp;Fei Meng ,&nbsp;Siqi Chen ,&nbsp;Cicong Hu ,&nbsp;Yangdi Wang ,&nbsp;Jinjiang Lin ,&nbsp;Weikai Zheng ,&nbsp;Jixin Meng ,&nbsp;Yuqin Lin ,&nbsp;Dan-Chai Chen ,&nbsp;Canhui Sun ,&nbsp;Zhenpeng Peng ,&nbsp;Ren Mao ,&nbsp;Li Shi ,&nbsp;Shi-Ting Feng ,&nbsp;Xuehua Li","doi":"10.1016/j.metrad.2025.100186","DOIUrl":"10.1016/j.metrad.2025.100186","url":null,"abstract":"<div><h3>Purpose</h3><div>Effective tools for risk stratification of intestinal disease progression (IDP) in Crohn's disease (CD) patients remain limited. Electronic medical records (EMRs) contain detailed information describing disease characteristics. Large language model (LLM) excels at summarizing and analyzing these data.</div></div><div><h3>Materials and methods</h3><div>We developed an LLM-agent using EMRs to predict IDP. In this retrospective study, we collected EMRs from 563 patients across six centers, built a human knowledge base incorporating literature-derived and EMRs-derived knowledge. Three levels of prompts were developed through a step-by-step superposition strategy: basic task description, Chain-of-Thought technique, and “Self-Reflection” module. The third level further integrated the human knowledge base. Three prompt versions (V1-3) were paired with three LLMs (ChatGPT-4omini, Gemini-1.5-Pro, ChatGPT-4o), creating nine LLM-agents. We compared their predictive performance for IDP to select the optimal model and contrasted the readability and semantics of LLM-generated reports with physician-written summaries.</div></div><div><h3>Results</h3><div>After evaluating 304 initial factors using a human-in-the-loop strategy with LLM and 13 CD specialists, we selected 98 IDP-related factors to construct the knowledge base. GPT-4o_V3 outperformed eight competing models in training set (accuracy, 84.4 % vs. 53.9 %–75.8 %; F1 score: 0.867 vs. 0.663–0.808) and test set (accuracy, 83.5 % vs. 59.8 %–72.2 %; F1 score, 0.882 vs. 0.748–0.814). Disease-progression-free survival of high-risk patients identified by GPT-4o_V3 was significantly shorter than low-risk patients (<em>P</em> &lt; 0.001). Prediction reports generated from GPT-4o_V3 showed intermediate characteristics between Gemini-1.5-Pro_V3 and GPT-4omini_V3 in both readability and semantics.</div></div><div><h3>Conclusion</h3><div>In conclusion, GPT-4o_V3 outperforms other models and accurately predicts IDP risk for CD patients across multiple medical centers, assisting clinicians in clinical decision-making.</div></div>","PeriodicalId":100921,"journal":{"name":"Meta-Radiology","volume":"4 1","pages":"Article 100186"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147424566","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":"Prompt engineering for healthcare: Methodologies and applications","authors":"Jiaqi Wang ,&nbsp;Enze Shi ,&nbsp;Sigang Yu ,&nbsp;Zihao Wu ,&nbsp;Huawen Hu ,&nbsp;Chong Ma ,&nbsp;Haixing Dai ,&nbsp;Qiushi Yang ,&nbsp;Yanqing Kang ,&nbsp;Jinru Wu ,&nbsp;Chenxi Yue ,&nbsp;Haiyang Zhang ,&nbsp;Yiheng Liu ,&nbsp;Yi Pan ,&nbsp;Zhengliang Liu ,&nbsp;Lichao Sun ,&nbsp;Xiang Li ,&nbsp;Bao Ge ,&nbsp;Xi Jiang ,&nbsp;Yixuan Yuan ,&nbsp;Shu Zhang","doi":"10.1016/j.metrad.2025.100190","DOIUrl":"10.1016/j.metrad.2025.100190","url":null,"abstract":"<div><div>Prompt engineering is a critical technique in the field of natural language processing (NLP) that involves designing and optimizing the prompts used to input information into models, aiming to enhance their performance on specific tasks. With the recent advancements in large language models (LLM), prompt engineering has shown significant superiority across various domains and has become increasingly important in the healthcare domain. However, there is a lack of comprehensive reviews specifically focusing on prompt engineering in the medical field. This review will introduce the latest advances in prompt engineering in the field of NLP for the medical field. First, we will provide the development of prompt engineering and emphasize its significant contributions to healthcare NLP applications such as question-answering systems, text summarization, and machine translation. With the continuous improvement of general LLM, the importance of prompt engineering in the healthcare domain is becoming increasingly prominent. The aim of this article is to provide useful resources and bridges for healthcare NLP researchers to better explore the application of prompt engineering in this field. We hope that this review can provide new ideas and inspire for research and application in medical NLP.</div></div>","PeriodicalId":100921,"journal":{"name":"Meta-Radiology","volume":"4 1","pages":"Article 100190"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147424602","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":"Quantum artificial intelligence: A comprehensive survey","authors":"Hanqi Jiang ,&nbsp;Yi Pan ,&nbsp;Junhao Chen ,&nbsp;Zhengliang Liu ,&nbsp;Lichao Sun ,&nbsp;Quanzheng Li ,&nbsp;Lu Zhang ,&nbsp;Dajiang Zhu ,&nbsp;Xianqiao Wang ,&nbsp;Wei Liu ,&nbsp;Xiang Li ,&nbsp;Gang Li ,&nbsp;Wei Zhang ,&nbsp;Lin Zhao ,&nbsp;Xiaowei Yu ,&nbsp;Yingfeng Wang ,&nbsp;Tianming Liu","doi":"10.1016/j.metrad.2026.100205","DOIUrl":"10.1016/j.metrad.2026.100205","url":null,"abstract":"<div><div>Quantum Artificial Intelligence (QAI) has emerged at the nexus of quantum computing and AI, promising to redefine computational frontiers. This survey critically synthesizes the state-of-the-art through 2024, elucidating the profound bidirectional synergy between these fields. We analyze how classical machine learning is accelerating quantum hardware control, circuit optimization, and error correction. Conversely, we assess the potential quantum advantage of algorithms, including variational and kernel-based methods, across domains such as drug discovery, financial modeling, and cybersecurity. Our analysis reveals a critical trade-of between the utility of near-term Noisy Intermediate-Scale Quantum (NISQ) devices and the long-term promise of fault-tolerant architectures. We identify fundamental obstacles to QAI's advancement, including hardware decoherence, algorithmic barren plateaus, and data-encoding bottlenecks. While QAI's potential is transformative, achieving practical quantum advantage requires a concerted effort to overcome these core challenges at the hardware-software interface. This work provides a roadmap for navigating the current landscape and prioritizing future research in this rapidly evolving discipline.</div></div>","PeriodicalId":100921,"journal":{"name":"Meta-Radiology","volume":"4 1","pages":"Article 100205"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147424568","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}