Large Language Models (LLMs) show promise in medical diagnosis, but their performance varies with prompting. Recent studies suggest that modifying prompts may enhance diagnostic capabilities. This study aimed to test whether a prompting approach that aligns with general clinical reasoning methodology-specifically, using a standardized template to first organize clinical information into predefined categories (patient information, history, symptoms, examinations, etc.) before making diagnoses, instead of one-step processing-can enhance the LLM's medical diagnostic capabilities.

The purposes of the study are to assess the diagnostic performance of preoperative imaging for staging factors in gastric-type endocervical adenocarcinoma (GEA) and to compare the performance for GEA with that of usual-type endocervical adenocarcinoma (UEA) among patients preoperatively deemed locally early stage (DLES) (< T2b without distant metastasis).

Fractures are one of the most common reasons of admission to emergency department affecting individuals of all ages and regions worldwide that can be misdiagnosed during radiologic examination. Accurate and timely diagnosis of fracture is crucial for patients, and artificial intelligence that uses algorithms to imitate human intelligence to aid or enhance human performs is a promising solution to address this issue. In the last few years, numerous commercially available algorithms have been developed to enhance radiology practice and a large number of studies apply artificial intelligence to fracture detection. Recent contributions in literature have described numerous advantages showing how artificial intelligence performs better than doctors who have less experience in interpreting musculoskeletal X-rays, and assisting radiologists increases diagnostic accuracy and sensitivity, improves efficiency, and reduces interpretation time. Furthermore, algorithms perform better when they are trained with big data on a wide range of fracture patterns and variants and can provide standardized fracture identification across different radiologist, thanks to the structured report. In this review article, we discuss the use of artificial intelligence in fracture identification and its benefits and disadvantages. We also discuss its current potential impact on the field of radiology and radiomics.

Sarcopenia, defined as skeletal muscle loss, is thought to be a hallmark of cancer cachexia. It has an impact on mortality, especially in cancer patients. There are also opposing views regarding the relationship between definitive concurrent chemoradiotherapy (CRT) and sarcopenia in locally advanced lung cancer. Our aim was to investigate the prognostic effect of sarcopenia in our patients with locally advanced stage III non-small cell lung cancer (NSCLC) who received definitive concurrent CRT by using many markers, and to determine the overall survival (OS). The study was designed as a retrospective cohort. 54 patients with stage III NSCLC who received definitive concurrent CRT at the Radiation Oncology Unit of Health Sciences University Izmir Dr Suat Seren Chest Diseases and Surgery Training Hospital, between January 1, 2018 and December 31, 2019, were included in the study.92% of our patients were sarcopenic with international L3-skeletal muscle index (SMI) and Psoas muscle index (PMI) threshold values. The mean OS time was 32.4 months, and the 4-year survival rate was 38.9%. While the new threshold values specific to our patient group were 26.21 for SMI and 2.94 for PMI, SMI and PMI did not indicate OS with these values. Even with the new values, most proposed criteria for sarcopenia did not indicate OS. However, low BMI (≤21.30), low serum albumin (≤4.24 mg/dl) and low visceral fat tissue area (≤37) in univariate analysis, and low visceral fat tissue area (≤37) in multivariate analysis indicated OS. OS was poor in patients with low fat tissue area. In patients with stage III NSCLC who received definitive concurrent CRT, low visceral fat tissue area (≤37) indicated OS, rather than SMI, PMI and other sarcopenia indices.

In this narrative review, we review the applications of artificial intelligence (AI) into clinical magnetic resonance imaging (MRI) exams, with a particular focus on Japan's contributions to this field. In the first part of the review, we introduce the various applications of AI in optimizing different aspects of the MRI process, including scan protocols, patient preparation, image acquisition, image reconstruction, and postprocessing techniques. Additionally, we examine AI's growing influence in clinical decision-making, particularly in areas such as segmentation, radiation therapy planning, and reporting assistance. By emphasizing studies conducted in Japan, we highlight the nation's contributions to the advancement of AI in MRI. In the latter part of the review, we highlight the characteristics that make Japan a unique environment for the development and implementation of AI in MRI examinations. Japan's healthcare landscape is distinguished by several key factors that collectively create a fertile ground for AI research and development. Notably, Japan boasts one of the highest densities of MRI scanners per capita globally, ensuring widespread access to the exam. Japan's national health insurance system plays a pivotal role by providing MRI scans to all citizens irrespective of socioeconomic status, which facilitates the collection of inclusive and unbiased imaging data across a diverse population. Japan's extensive health screening programs, coupled with collaborative research initiatives like the Japan Medical Imaging Database (J-MID), enable the aggregation and sharing of large, high-quality datasets. With its technological expertise and healthcare infrastructure, Japan is well-positioned to make meaningful contributions to the MRI-AI domain. The collaborative efforts of researchers, clinicians, and technology experts, including those in Japan, will continue to advance the future of AI in clinical MRI, potentially leading to improvements in patient care and healthcare efficiency.

To compare image quality and diagnostic performance among SS-EPI diffusion weighted imaging (DWI), multi-shot (MS) EPI DWI, and reduced field-of-view (rFOV) DWI for muscle-invasive bladder cancer (MIBC).

The purpose of this study was to investigate whether the high-precision magnetic resonance (MR) sequence using modified Fast 3D mode wheel and Precise IQ Engine (PIQE), that was collected in a wheel shape with sequential data filling in the k-space in the phase encode-slice encode plane, is feasible for breath-hold (BH) three-dimensional (3D) T1-weighted imaging of the hepatobiliary phase (HBP) of gadoxetic acid-enhanced MRI in comparison to the compressed sensing (CS) sequence using Advanced Intelligent Clear-IQ Engine (AiCE).

To evaluate the risk factors of non-diagnostic results based on cause of error in liver tumor biopsy.

In radiology, large language models (LLMs), including ChatGPT, have recently gained attention, and their utility is being rapidly evaluated. However, concerns have emerged regarding their reliability in clinical applications due to limitations such as hallucinations and insufficient referencing. To address these issues, we focus on the latest technology, retrieval-augmented generation (RAG), which enables LLMs to reference reliable external knowledge (REK). Specifically, this study examines the utility and reliability of a recently released RAG-equipped LLM (RAG-LLM), NotebookLM, for staging lung cancer.

The ACR Thyroid Imaging, Reporting, and Data System (TI-RADS) uses a score based on ultrasound (US) imaging to stratify the risk of nodule malignancy and recommend appropriate follow-up. This study aims to analyze US reports and explore how Natural Language Processing (NLP) leveraging Transformers models can classify ACR TI-RADS from text reports using the description of thyroid nodule features.

Based on coronary computed tomography angiography (CCTA), this study aimed to evaluate the predictive value of pericoronary fat attenuation index (FAI) for graft occlusion in patients following coronary artery bypass grafting (CABG).

The breast shape differs between the prone position in breast magnetic resonance imaging (MRI) and the supine position on an operating table. We sought to determine the relationship between patient position-induced changes on prone and supine MRI in breast shape and mammographic breast density or thickness.

To evaluate the impact of the visceral adipose tissue (VAT) area and density on the accuracy of tumor T-staging of gastric cancer in preoperative computed tomography (CT).

Artificial intelligence (AI) algorithms for lung nodule detection have been developed to assist radiologists. However, external validation of its performance on low-dose CT (LDCT) images is insufficient. We examined the performance of the commercially available deep-learning-based lung nodule detection (DL-LND) using LDCT images at Japanese lung cancer screening (LCS).

To evaluate the efficacy of MRI findings for differentiating between ovarian metastasis from stomach cancer (OMSC) and colorectal cancer (OMCC).

To compare image quality and visibility of anatomical structures on contrast-enhanced thin-slice abdominal CT images reconstructed using super-resolution deep learning reconstruction (SR-DLR), deep learning-based reconstruction (DLR), and hybrid iterative reconstruction (HIR) algorithms.

Primary hyperparathyroidism (pHPT) is a common endocrine disorder characterized by one or more hyperfunctioning parathyroid glands. Definitive surgical treatment demands precise preoperative localisation of hyperfunctioning parathyroid tissue. The purpose of our study is to assess the value of F-fluorocholine positron emission tomography (PET/CT) in preoperative localisation of hyperfunctioning parathyroid glands in patients with biochemically confirmed pHPT and negative or inconclusive cervical ultrasonography and Tc-MIBI scintigraphy.

So far, there is no gold standard to diagnosis dysthyroid optic neuropathy (DON). Diffusion kurtosis imaging (DKI) has the potential to provide imaging biomarkers for the timely and accurate diagnosis of DON. This study aimed to explore the white matter (WM) alterations in thyroid-associated ophthalmopathy (TAO) patients with and without DON using DKI with tract-based spatial statistics method.

To optimize NTO parameters in non-coplanar RapidArc (RA) stereotactic radiosurgery (SRS) for multiple brain metastases and compare them with HyperArc (HA) plans.

Despite a global decrease in the number of COVID-19 patients, early prediction of the clinical course for optimal patient care remains challenging. Recently, the usefulness of image generation for medical images has been investigated. This study aimed to generate short-term follow-up chest CT images using a latent diffusion model in patients with COVID-19.