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HealthEvidence-backed gain

An interpretable multi-instance learning method for accurate differentiation of malignant and benign laryngeal lesions in laryngoscopy

Background Laryngeal cancer is a significant global health issue with high mortality, and early diagnosis is critical for survival. Developing accurate diagnostic models for laryngoscopy can reduce potential repeated biopsies and lessen the patient burden, representing an urgent clinical need. However, existing artificial intelligence models often function as black boxes and are trained on single, pre-selected images, which does not reflect the clinical workflow where multiple images are assessed. Methods We con…

European Archives of Oto-Rhino-Laryngology

Evidence-backed gain

Radiological assessment of pulmonary vascularity in congenital heart disease: standardized clinician assessment versus deep learning-based prediction

Evidence-backed problem

Explainable artificial intelligence in medical imaging: how to interpret, evaluate, and use artificial intelligence explanations

Evidence-backed problem

EXPRESS: Relation between Albumin-Corrected Anion Gap and In-Hospital Mortality in Patients with Traumatic Lung Injury: A Multicenter Retrospective Cohort Study and the Development of Machine Learning-Based Prediction Models

Evidence-backed gain

AI patients, real practice: exploring the use of AI-simulated patients to support primary healthcare training for combat medical technicians

Introduction Combat Medical Technicians (CMTs) are central to military primary care but have limited opportunity for clinical exposure. Simulated patients offer a controlled method to maintain clinical currency. Advances in conversational artificial intelligence (AI) enable realistic and interactive simulated consultations. We present our evaluation of the feasibility, acceptability and educational impact of AI-simulated patients for CMT training. Methods Five military primary care simulated patients were develo…

Evidence-backed gain

Evaluation of Differential Diagnosis of Odontogenic Lesions in Cone Beam Computed Tomography Images Using Radiomics-Based Machine Learning

Objective This study aimed to evaluate the structural characteristics of mandibular alveolar bone in patients with Type 1 diabetes mellitus (T1DM), Type 2 diabetes mellitus (T2DM), and systemically healthy controls using panoramic radiography-based radiomic analysis combined with machine learning algorithms. Materials and methods A total of 225 panoramic radiographs (75 T1DM, 75 T2DM, 75 healthy controls) were retrospectively analyzed. ROIs were segmented from eight anatomical mandibular segments per subject, an…

Both readings

Multi-omics strategies for biomarker discovery and application in personalized oncology

Multi-omics strategies, integrating genomics, transcriptomics, proteomics, and metabolomics, have revolutionized biomarker discovery and enabled novel applications in personalized oncology. Despite rapid technological developments, a comprehensive synthesis addressing integration strategies, analytical workflows, and translational applications has been lacking. This review presents a comprehensive framework of multi-omics integration, encompassing workflows, analytical techniques, and computational tools for bot…

Evidence-backed gain

Beyond Visual Scoring: Computational CT-analysis for HRCT based quantification of Interstitial Lung Disease in Inflammatory Rheumatic Disease

Interstitial lung disease (IRD-ILD) is a significant cause of morbidity and mortality in patients with inflammatory rheumatic disorders (IRD). High-resolution computed tomography (HRCT) is widely considered the gold standard for the non-invasive assessment of ILD; however, its interpretation is constrained by substantial inter-observer variability and the need for time-consuming expert evaluation. Computer-based image analysis including artificial intelligence (AI) has emerged as a promising approach for the aut…

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Challenges in Medical Algorithmic Fairness

Artificial intelligence (AI) is increasingly being integrated into oncology for applications including cancer detection, risk stratification, treatment planning, and clinical documentation. Concerningly, growing evidence demonstrates that AI systems can reproduce or amplify existing disparities across patient populations. Although considerable effort has focused on developing computational methods to reduce algorithmic bias, many challenges surrounding fairness extend beyond technical implementation. In this com…

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Large language models and prostate MRI reporting: a stringent testbed for safe deployment under evolving AI, health-data, and cybersecurity regulation

Prostate magnetic resonance imaging (MRI) reporting is a high-impact communication task because small differences in lesion laterality, sector localization, lesion size, Prostate Imaging-Reporting and Data System (PI-RADS) categorization, or staging language can change biopsy targeting, surveillance, counseling, and treatment planning. At the same time, widespread patient-portal access means that many patients encounter radiology reports before clinical discussion and may seek explanations from public large lang…

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Evaluation of gastric cancer using artificial intelligence iterative reconstruction on abdominal CT: image quality and diagnostic accuracy

To investigate the clinical performance of a novel deep-learning based image reconstruction algorithm, namely artificial intelligence iterative reconstruction (AIIR), for assessing gastric cancer (GC) on CT. We prospectively enrolled 132 GC patients without history of treatment, all of whom subsequently underwent surgical resection or staging laparoscopy. All patients underwent preoperative abdominal contrast-enhanced CT examinations, and images were reconstructed with both hybrid iterative reconstruction (HIR)…

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IShuffleNet-PCNN hybrid classifier within an MResU-Net-based framework for spinal cord injury detection and classification using CT images

Spinal cord injury (SCI) is a serious medical condition. Spinal Cord Injury limits the movement of the body, blocks the nervous system and affects the quality of life of an injured patient. Accurate detection and classification of these fractures are essential for timely diagnosis and treatment planning; however, conventional assessment methods often struggle with noise, variability, and subtle injury patterns in CT imaging.This study aimed to develop an integrated deep learning framework for accurate and robust…

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A multimodal MRI-based deep learning model for non-invasive diagnosis of pineal region germinoma

PURPOSE: To develop a deep learning model for the preoperative, non-invasive identification of germinomas in the pineal region. This retrospective study included 114 patients with pathologically confirmed pineal region tumors. The cohort was randomly divided into a training set (n = 91) and a test set (n = 23). The training set was further partitioned into three folds for cross-validation. A convolutional neural network (CNN) enhanced with contrastive learning was used to extract discriminative features from ind…

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A comprehensive review on automated diabetic retinopathy detection and classification using fundus image

Diabetic Retinopathy (DR) is a vision-threatening complication in diabetic patients. It harms retinal vessels and may lead to blindness. Detection at an early stage and its classification can prevent the risk of vision loss. However, fundus image-based manual screening of DR is a time-consuming and complex process. In recent years, many automated techniques for DR detection have been developed to screen and diagnose the disease condition at an early stage. These techniques are explored using the keywords diabeti…

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Clustering of rheumatoid arthritis patients: an unsupervised machine learning approach for characterizing the TNFi response

This study aimed to identifydistinct profiles of response to Tumor Necrosis Factor inhibitors (TNFi) in rheumatoid arthritis (RA) patients using unsupervised machine learning to integrate clinical, demographic, and genetic data. A cohort of 294 RA patients was analyzed using hierarchical clustering techniques. Data collected included body mass index (BMI), adherence to physical activity, prevalence of comorbidities, seropositivity, Health Assessment Questionnaire (HAQ) scores, and genetic polymorphisms in TNF pa…