Search Results (1,849)

Background/Objectives: Multimodal large language models (LLMs) may directly interpret medical images, including thyroid ultrasounds (USs). Whether these models can reliably assess thyroid nodules—where subtle echogenic and morphological details are critical—remains uncertain. The American College of Radiology (ACR) TI-RADS system provides a structured framework for benchmarking artificial intelligence. This study evaluates GPT-5.0’s ability to interpret thyroid US images according to TI-RADS criteria and contextualizes its performance relative to expert radiologist assessment, using FNA cytology as the reference standard. Methods: This retrospective study included 100 patients (mean age 49.8 ± 12.6 years; 72 women) with cytology-confirmed diagnoses: Bethesda II (benign) or Bethesda V–VI (malignant). Each nodule had longitudinal and transverse US images acquired with high-frequency linear probes. A board-certified radiologist (>10 years’ experience) and GPT-5.0 independently assessed TI-RADS features (composition, echogenicity, shape, margin, echogenic foci) and assigned final categories. Agreement was analyzed using Cohen’s κ, and diagnostic performance was calculated using TR4–TR5 as positive for malignancy. Results: Agreement was substantial for composition (κ = 0.62), shape (κ = 0.70), and margin (κ = 0.68); moderate for echogenicity (κ = 0.48); and poor for echogenic foci (κ = 0.12). GPT-5.0 demonstrated a systematic, risk-averse tendency to up-classify nodules, leading to increased TR4–TR5 assignments. Overall, the TI-RADS agreement was 58% (κ = 0.31). The radiologist showed superior diagnostic performance (sensitivity 89%, specificity 85%) compared with GPT-5.0 (sensitivity 67%, specificity 49%), largely driven by false-positive TR4 classifications among benign nodules. Conclusions: GPT-5.0 recognizes several high-level TI-RADS features but struggles with microcalcifications and tends to overestimate malignancy risk within a risk-stratification framework, limiting its standalone clinical use. Ultrasound-specific training and domain adaptation may enable meaningful adjunctive roles in thyroid nodule assessment. Full article

Lipodystrophy is a rare group of metabolic disorders characterized by the abnormal distribution of body fat, which can lead to various metabolic complications due to the body’s inability to adequately process carbohydrates and fat. We report the case of a female, aged 53 years, who was admitted as an outpatient for progressive weight loss of the upper part of the body (face, neck, arms, and chest), dyspeptic complaints, fatigue, mild insomnia, and anxious behavior. Her medical history was characterized by the presence of dyslipidemia, hypertension, and a minor stroke episode. However, she denied any family-relevant medical history. Although the clinical perspective suggested a possible late onset of partial acquired lipodystrophy, due to the imaging exam that revealed an enlarged liver with inhomogeneous structure with multiple nodular lesions, scattered over both lobes, a lot of lab work-ups and complementary studies were performed. Eventually, a liver biopsy was performed by a laparoscopic approach during cholecystectomy, the histology consistent with metabolic disease-associated steatohepatitis (MASH). In conclusion, given their heterogeneity and rarity, lipodystrophies may be either overlooked or misdiagnosed for other entities. Barraquer–Simons syndrome (BSS) may be associated with liver disease, including cirrhosis and liver failure. Liver lipodystrophy in BSS may sometimes feature steatosis with a focal, multi-nodular aspect, multiplying the diagnostic burden. Liver lipodystrophy may manifest as asymptomatic fat accumulation but may progress to severe conditions, representing one of the major causes of mortality in BSS, apart from the cardio-vascular comorbidities. Given the potential of severe outcomes, it is mandatory to correctly assess the stage of liver disease since the first diagnosis. Full article

The classification of breast cancer using machine learning techniques has become a critical tool in modern medical diagnostics. This study analyzes the performance of hybrid models that combine traditional machine learning algorithms (TMLAs) with a convolutional neural network (CNN)-based VGG16 model for feature extraction to improve accuracy for classifying eight breast cancer subtypes (BCS). The methodology consists of three steps. First, image preprocessing is performed on the BreakHis dataset at 400× magnification, which contains 1820 histopathological images classified into eight BCS. Second, the CNN VGG16 is modified to function as a feature extractor that converts images into representative vectors. These vectors constitute the training set for TMLAs, such as Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naive Bayes (NB), leveraging VGG16’s ability to capture relevant features. Third, k-fold cross-validation is applied to evaluate the model’s performance by averaging the metrics obtained across all folds. The results reveal that hybrid models leveraging a CNN-based VGG16 model for feature extraction, followed by TMLAs, achieve accuracy outstanding experimental accuracy. The KNN-based hybrid model stood out with a precision of 0.97, accuracy of 0.96, sensitivity of 0.96, specificity of 0.99, F1-score of 0.96, and ROC-AUC of 0.97. These findings suggest that, with an appropriate methodology, hybrid models based on TMLA have strong potential in classification tasks, offering a balance between performance and predictive capability. Full article

Endometrial lesions are common in gynecology, exhibiting considerable clinical heterogeneity across different subtypes. Although ultrasound imaging is the preferred diagnostic modality due to its noninvasive, accessible, and cost-effective nature, its diagnostic performance remains highly operator-dependent, leading to subjectivity and inconsistent results. To address these limitations, this study proposes a hierarchical feature selection and optimization framework for endometrial lesions, aiming to enhance the objectivity and robustness of ultrasound-based diagnosis. Firstly, Kernel Principal Component Analysis (KPCA) is employed for nonlinear dimensionality reduction, retaining the top 1000 principal components. Secondly, an ensemble of three filter-based methods—information gain, chi-square test, and symmetrical uncertainty—is integrated to rank and fuse features, followed by thresholding with Maximum Scatter Difference Linear Discriminant Analysis (MSDLDA) for preliminary feature selection. Finally, the Whale Migration Algorithm (WMA) is applied to population-based feature optimization and classifier training under the constraints of a Support Vector Machine (SVM) and a macro-averaged F1 score. Experimental results demonstrate that the proposed closed-loop pipeline of “kernel reduction—filter fusion—threshold pruning—intelligent optimization—robust classification” effectively balances nonlinear structure preservation, feature redundancy control, and model generalization, providing an interpretable, reproducible, and efficient solution for intelligent diagnosis in small- to medium-scale medical imaging datasets. Full article

Ionic liquids (ILs) have emerged as multifunctional compounds with low volatility, high thermal stability, and tunable solvation capabilities, making them highly promising for biomedical applications. First explored in the late 1990s and early 2000s for enhancing the thermal stability of enzymes, antimicrobial agents, and controlled release systems, ILs have since gained significant attention in drug delivery, antimicrobial treatments, medical imaging, and biosensing. This review examines the diverse functions of ILs in contemporary therapeutics and diagnostics, highlighting their transformative capabilities in improving drug solubility, bioavailability, transdermal permeability, and pathogen inactivation. In drug delivery, ILs improve solubility of bioactive compounds, with several IL formulations achieving substantial solubility enhancements for poorly soluble drugs. Bio-ILs, in particular, show promise in enhancing drug delivery systems, such as improving transdermal permeability. ILs also exhibit significant antimicrobial and antiviral activity, offering new avenues for combating resistant pathogens. Despite their broad potential, challenges such as cytotoxicity, long-term metabolic effects, and the stability of ILs in physiological conditions persist. While much research has focused on their physicochemical properties, biological activity and in vivo studies are still underexplored. The future directions for ILs in biomedical applications include the development of bioengineered ILs and hybrid ILs, combining functional components like nanoparticles and polymers to create multifunctional materials. These ILs, derived from renewable resources, show great promise in personalized medicine and clinical applications. Further research is necessary to evaluate their pharmacokinetics, biodistribution, and long-term safety to fully realize their biomedical potential. This study emphasizes the potential of ILs to transform therapeutic and diagnostic technologies by highlighting present shortcomings and offering pathways for clinical translation, while also debating the need for continuous research to fully utilize their biomedical capabilities. Full article

Each year, around 23 million miscarriages occur worldwide, which have a substantial emotional impact on parents, and impose significant societal costs. While medical care accounts for most expenses, work productivity loss contributes significantly. Addressing underlying causes of miscarriage could improve parents’ mental health and potentially their economic impact. In most countries, investigations into miscarriage causes are only recommended after recurrent cases, focusing mainly on maternal factors. Fetal and placental tissue are rarely examined, as current guidelines do not advise routine genetic analyses of pregnancy tissue, because the impact of further clinical decision making and individual prognosis is unclear. However, this leaves over 90% of all miscarriage cases unexplained and highlights the need for alternative methods. We therefore conducted a narrative review on genetic analysis, autopsy, and imaging of products of conception (POC). Karyotyping, QF-PCR, SNP array, and aCGH were reviewed in different research settings, with QF-PCR being the most cost-effective, while obtaining the highest technical success rate. Karyotyping, historically being considered the gold standard for POC examination, was the least promising. Post-mortem imaging techniques including post-mortem ultrasound (PMUS), ultra-high-field magnetic resonance imaging (UHF-MRI), and microfocus computed tomography (micro-CT) show promising diagnostic capabilities in miscarriages, with micro-CT achieving the highest cost-effective performance. In conclusion, current guidelines do not recommend diagnostic testing for most cases, leaving the majority unexplained. Although genetic and imaging techniques show promising diagnostic potential, they should not yet be implemented in routine clinical care and require thorough evaluation within research settings—assessing not only diagnostic and psychosocial outcomes but also economic implications. Full article

Aortic stenosis (AS) is the most prevalent valvular heart disease in Western countries and it is especially associated with older age. With its progressive course, AS leads to ventricular hypertrophy, impaired diastolic and systolic function, and symptomatic deterioration. The natural history of AS is closely linked to the extent of myocardial and extracardiac damage in association with the patients comorbidities. Diagnosis relies primarily on transthoracic echocardiography, which assesses valve morphology, quantifies stenosis severity, and evaluates cardiac remodeling. However, discordant grading is frequent, necessitating advanced imaging to clarify the severity and the mechanism of the stenosis and stratify risk. Treatment is predominantly interventional, as no medical therapy is able to stop disease progression. Surgical aortic valve replacement (SAVR) and transcatheter aortic valve replacement (TAVR) are the two treatment options. Special clinical scenarios—such as cardiogenic shock or concomitant cardiac amyloidosis—pose additional diagnostic and therapeutic challenges and require individualized, multidisciplinary management. Overall, contemporary AS care increasingly integrates multimodality imaging, refined risk stratification, and tailored interventional strategies to optimize outcomes. Full article

Background/Objectives: Artificial intelligence (AI) is an emerging technology for diagnosing liver fibrosis in Metabolic-Dysfunction-Associated Steatotic Liver Disease (MASLD), but a comprehensive synthesis of its performance is lacking. This systematic review (SR) aimed to evaluate the current evidence of AI models for diagnosing or staging liver fibrosis in patients with MASLD compared to conventional diagnostic tools. Methods: A comprehensive search was conducted in PubMed, Scopus, Web of Science, ScienceDirect, Embase, LILACS, IEEE Series, and Association for Computing Machinery (ACM). Primary studies applying AI to diagnose fibrosis in adults with MASLD were included. Risk of bias was assessed using the QUADAS-2 tool, and methodological reporting was evaluated according to the MINimum Information for Medical AI Reporting (MINIMAR) guideline. A narrative synthesis was performed, grouping studies by data type (clinical/laboratory vs. imaging) and summarizing diagnostic performance and clinical application. A frequency-based analysis was applied to identify the most recurrent predictive features, and an analysis of the AI architecture and application was reported. The review was registered in PROSPERO (CRD420251035919). Results: Twenty-one studies were included, encompassing 19,221 patients and 5237 images. Across studies, AI models consistently outperformed non-invasive scores such as Fibrosis-4 Index (FIB-4) and NAFLD Fibrosis Score (NFS). The most frequent predictive variables were identified. Despite an overall low risk of bias, methodological transparency and external validation were limited. Conclusions: AI is feasible for the non-invasive diagnosis of liver fibrosis in MASLD, demonstrating superior accuracy to standard clinical scores. Broader clinical application is limited by the lack of external validation and high heterogeneity among the studies. Prospective validation in diverse, multicenter cohorts is essential before AI can be integrated into routine clinical practice. Full article

An imperforate hymen is a rare congenital genital anomaly causing menstrual blood retention during puberty. Treatment consists of a simple surgical incision of the hymenal membrane. We present a case of a 14-year-old girl who was admitted to the Emergency Department with severe lower abdominal pain mimicking appendicitis. Medical history revealed a lack of menses and several months of cyclic abdominal pain. Imaging diagnostics confirmed an imperforate hymen with hematometrocolpos. Hymenotomy was performed with full recovery without complications. An imperforate hymen should be considered in the differential diagnosis of abdominal pain in adolescent girls, especially without expected menstruation. Early recognition allows for prompt treatment and prevents complications. Full article

Recent progress in machine learning (ML) has fueled the emergence of intelligent clinical decision support systems (CDSSs) designed to optimize diagnostic and prognostic accuracy through the analysis of complex and heterogeneous medical data. The analysis provides a comprehensive perspective on the use of machine learning in the medical field by integrating a bibliometric assessment of the recent literature and a detailed examination of the algorithms used in current studies. The bibliometric component highlights the evolution of publications, the thematic distribution of research and emerging directions within various medical specialties. In addition, the evaluation of selected articles sheds light on the concrete ways of applying ML algorithms, as well as the methodological limitations encountered in clinical practice. Random forest and gradient boosting are commonly used in internal medicine and cardiology, while convolutional neural networks (CNNs) dominate neuroimaging in neurology and image-based analyses in oncology and radiology. Full article

Artificial intelligence (AI) is rapidly transforming medical diagnostics by allowing for early, accurate, and data-driven clinical decision-making. This review provides an overview of how machine learning (ML), deep learning, and emerging multimodal foundation models have been used in diagnostic procedures across imaging, pathology, molecular analysis, physiological monitoring, and electronic health record (EHR)-integrated decision-support systems. We have discussed the basic computational foundations of supervised, unsupervised, and reinforcement learning and have also shown the importance of data curation, validation metrics, interpretability methods, and feature engineering. The use of AI in many different applications has shown that it can find abnormalities and integrate some features from multi-omics and imaging, which has shown improvements in prognostic modeling. However, concerns about data heterogeneity, model drift, bias, and strict regulatory guidelines still remain and are yet to be addressed in this field. Looking forward, future advancements in federated learning, generative AI, and low-resource diagnostics will pave the way for adaptable and globally accessible AI-assisted diagnostics. Full article

Accurate medical image segmentation plays a crucial role in clinical diagnosis by precisely delineating diseased tissues and organs from various medical imaging modalities. However, existing segmentation methods often fail to effectively capture low-level structural details and exhibit inconsistencies in feature connection, which may compromise diagnostic reliability. To address these limitations, this study proposes a novel Multilayer Perceptron–Transformer encoding architecture that integrates the Shift Multilayer Perceptron and Transformer mechanisms. Specifically, a SENet-based Atrous Spatial Pyramid Pooling module is designed to extract multi-scale contextual representations, while the Shift MLP refines underlying spatial features. Moreover, a channel–feature aggregation attention module is introduced to strengthen information flow between the encoder and decoder layers. Experimental results on the Automatic Cardiac Diagnostic Challenge dataset show an average Dice Similarity Coefficient (DSC) of 87.01% (83.32% for the right ventricle, 90.90% for the left ventricle, and 86.83% for the myocardium). On the Synapse multi-organ segmentation dataset, the proposed model achieves an average DSC of 79.35% and a 95% Haus Dorff Distance of 20.07 mm. These results demonstrate that MPT effectively captures both local and global anatomical structures, providing reliable support for clinical diagnosis. Full article

Background and Objectives: Diffusion models, as a recent advancement in generative modeling, have become central to high-resolution image synthesis and reconstruction. Their rapid progress has notably shaped computer vision and health informatics, particularly by enhancing medical imaging and diagnostic workflows. However, despite these developments, researchers continue to face challenges due to the absence of a structured and comprehensive discussion on the use of diffusion models within clinical imaging. Methods: This systematic review investigates the application of diffusion models in medical imaging for diagnostic purposes. It provides an integrated overview of their underlying principles, major application areas, and existing research limitations. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines and included peer-reviewed studies published between 2013 and 2024. Studies were eligible if they employed diffusion models for diagnostic tasks in medical imaging; non-medical studies and those not involving diffusion-based methods were excluded. Searches were conducted across major scientific databases prior to the review. Risk of bias was assessed based on methodological rigor and reporting quality. Given the heterogeneity of study designs, a narrative synthesis approach was used. Results: A total of 68 studies met the inclusion criteria, spanning multiple imaging modalities and falling into eight major application categories: anomaly detection, classification, denoising, generation, reconstruction, segmentation, super-resolution, and image-to-image translation. Explainable AI components were present in 22.06% of the studies, clinician engagement in 57.35%, and real-time implementation in 10.30%. Overall, the findings highlight the strong diagnostic potential of diffusion models but also emphasize the variability in reporting standards, methodological inconsistencies, and the limited validation in real-world clinical settings. Conclusions: Diffusion models offer significant promise for diagnostic imaging, yet their reliable clinical deployment requires advances in explainability, clinician integration, and real-time performance. This review identifies twelve key research directions that can guide future developments and support the translation of diffusion-based approaches into routine medical practice. Full article

Artificial intelligence (AI) and machine learning (ML) are transforming medical diagnostics, but human nail, an easily accessible and rich biological substrate, is still not fully exploited in the digital health field. Nail pathologies are easily diagnosed, non-invasive disease biomarkers, including systemic diseases such as anemia, diabetes, psoriasis, melanoma, and fungal diseases. This review presents the first big synthesis of image analysis for nail lesions incorporating AI/ML for diagnostic purposes. Where dermatological reviews to date have been more wide-ranging in scope, our review will focus specifically on diagnosis and screening related to nails. The various technological modalities involved (smartphone imaging, dermoscopy, Optical Coherence Tomography) will be presented, together with the different processing techniques for images (color corrections, segmentation, cropping of regions of interest), and models that range from classical methods to deep learning, with annotated descriptions of each. There will also be additional descriptions of AI applications related to some diseases, together with analytical discussions regarding real-world impediments to clinical application, including scarcity of data, variations in skin type, annotation errors, and other laws of clinical adoption. Some emerging solutions will also be emphasized: explainable AI (XAI), federated learning, and platform diagnostics allied with smartphones. Bridging the gap between clinical dermatology, artificial intelligence and mobile health, this review consolidates our existing knowledge and charts a path through yet others to scalable, equitable, and trustworthy nail based medically diagnostic techniques. Our findings advocate for interdisciplinary innovation to bring AI-enabled nail analysis from lab prototypes to routine healthcare and global screening initiatives. Full article

Background: Recent advances in artificial intelligence (AI) have produced ChatGPT-4o, a multimodal large language model (LLM) capable of processing both text and image inputs. Although ChatGPT has demonstrated usefulness in medical examinations, few studies have evaluated its image analysis performance. Methods: This study compared GPT-4o and GPT-4 using public questions from the 116th–118th Japanese National Medical Licensing Examinations (JNMLE), each consisting of 400 questions. Both models answered in Japanese using simple prompts, including screenshots for image-based questions. Accuracy was analyzed across essential, general, and clinical questions, with statistical comparisons by chi-square tests. Results: GPT-4o consistently outperformed GPT-4, achieving passing scores in all three examinations. In the 118th JNMLE, GPT-4o scored 457 points versus 425 for GPT-4. GPT-4o demonstrated higher accuracy for image-based questions in the 117th and 116th exams, though the difference in the 118th was not significant. For text-based questions, GPT-4o showed superior medical knowledge, clinical reasoning, and ethical response behavior, notably avoiding prohibited options. Conclusion: Overall, GPT-4o exceeded GPT-4 in both text and image domains, suggesting strong potential as a diagnostic aid and educational resource. Its balanced performance across modalities highlights its promise for integration into future medical education and clinical decision support. Full article