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All Diagnostics & Imaging Drug Discovery Mental Health Hospitals & Care

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From Correlation to Clinical Translation: The Biological-Grounding×Translational-Readiness Framework for Artificial Intelligence in Non-Small-Cell Lung Cancer

Non-small-cell lung cancer (NSCLC) remains the leading cause of cancer death worldwide, and clinicians now face a rapidly expanding array of artificial intelligence (AI) tools promising earlier detection, better treatment selection, and more precise radiotherapy, yet few have altered what happens at the bedside. The problem is not poor benchmark performance; it is that strong benchmark performance has repeatedly failed to translate into demonstrable patient benefit, because most published NSCLC models are retros…

American Journal of Clinical Oncology · Health

From Correlation to Clinical Translation: The Biological-Grounding×Translational-Readiness Framework for Artificial Intelligence in Non-Small-Cell Lung Cancer

Evidence-backed gain

NDIB-Sim: A Multimodal Bidirectional PINN Model for Simulating Brain Dynamics

Constructing dynamic virtual brain models is essential for understanding brain functions and pathological mechanisms, crucial in computational neuroscience. Current modeling methods can be grouped into two paradigms: deep learning models for accurate simulation, and neural dynamics models emphasizing physiological interpretability. However, these methods entail a fundamental tradeoff between accuracy and interpretability. To address this challenge, we introduce the neurodynamics-informed brain simulator (NDIB-Si…

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AI‐Driven Personalized Nutrition: Integrating Omics, Ethics, and Digital Health

Personalized nutrition (PN) aims to prevent and manage chronic diseases by providing individualized dietary guidance based on genetic, metabolic, and lifestyle data. Artificial intelligence (AI) has become a key enabler in PN by analyzing large-scale, multiomics datasets in obesity, diabetes, cardiovascular, and gastrointestinal disorders, where digital twins and health knowledge graphs support personalized interventions. Current findings demonstrate that AI models can guide microbiome-based dietary intervention…

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GIN-CRC-Pareto: A graph-based pareto-optimized multi-task learning framework to identify miRNA-target interactions in colorectal cancer

Background Colorectal cancer (CRC) ranks as the third highest incidence among malignancies for human and the second most common cause of cancer-related mortality in the United States. Accumulating evidence has established microRNAs (miRNAs) as critical regulators of cancer development and therapeutic response. Understanding miRNA-mRNA interactions is critical for elucidating the molecular mechanisms driving CRC and other malignancies. However, accurately modeling miRNA-mRNA interactions and their binding pattern…

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Improving 10-year cardiovascular disease risk prediction using automated machine learning

Aims To develop a cardiovascular disease (CVD) risk prediction model with improved accuracy and interpretability by integrating diverse risk factors and applying Automated Machine Learning (AutoML), thereby enhancing clinical utility over conventional models. Methods This is a prospective cohort study. Data were obtained from the Multi-Ethnic Study of Atherosclerosis (MESA), including baseline and fifth follow-up visits, comprising 4713 participants. Exercise and dietary data were harmonized via Metabolic Equiva…

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Dual-Approach AI for Pediatric Supracondylar Fractures: Multiclass Radiograph Classification with Explainable AI and Diagnostic Meta-analysis of AI-Based Computational Approaches

Rationale and objectives Pediatric supracondylar fractures (SCFs) are the most common elbow injury in children, yet radiographic diagnosis remains challenging due to complex developmental anatomy, with initially missed fracture rates of 17-77%. Prior artificial intelligence (AI) studies have been limited to binary classification frameworks without Gartland subtype differentiation, and no diagnostic test accuracy meta-analysis specific to supracondylar fractures exists. This study aimed to develop the first multi…

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Evidence-backed problem

The Translational Gap in AI for Oropharyngeal Squamous Cell Carcinoma: A TRIPOD+AI Scoping Review of Methodological Barriers to Treatment Deintensification

Rationale and objectives To map artificial intelligence (AI) and radiomics applications in computed tomography (CT), magnetic resonance imaging (MRI), and fluorodeoxyglucose positron emission tomography/CT (FDG-PET/CT) for oropharyngeal squamous cell carcinoma (OPSCC) in the context of human papillomavirus (HPV) status and treatment deintensification, evaluate reporting quality using TRIPOD+AI (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis + Artificial Intelligen…

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Ultrasound Habitat Radiomics for Preoperative Prediction of Invasive Breast Cancer With a DCIS Component: A Dual-center Retrospective Study

Purpose To develop and externally validate an ultrasound-based habitat subregional radiomics model for preoperative prediction of invasive breast cancer with concomitant ductal carcinoma in situ (IBC-DCIS). Methods A total of 1063 pathologically confirmed breast cancer patients from two centers were retrospectively enrolled and divided into a training cohort (n = 637) and an external validation cohort (n = 426). Tumor regions of interest were manually delineated on two-dimensional ultrasound images and further p…

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Artificial Intelligence Accurately Assists in Billing for Orthopaedic Lower Extremity Surgery: Performance of the Mistral-NeMo Language Model

Purpose To elucidate Mistral-NeMo's proficiency as a novel artificial intelligence assistant to verify coding accuracy and improve efficiency in manual medical coding practices in orthopaedic surgery. Methods This study tested Mistral-NeMo on 1000 operative notes labeled with the Current Procedural Terminology (CPT) codes from 177 providers. In total, there were 46 unique CPT codes; the most common were 29881 (knee arthroscopy with meniscectomy for both medial and lateral menisci), 29880 (knee arthroscopy with m…

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Artificial Intelligence Accurately Assists in Billing for Orthopaedic Lower Extremity Surgery: Performance of the Mistral-NeMo Language Model

The tire antioxidant derivative 6PPD-quinone exacerbates IBD by targeting NR1H4-mediated lipid metabolism and mitochondrial dysfunction in human colon epithelial cells

N- (1,3-Dimethylbutyl)-N'-phenyl-p-phenylenediamine quinone (6PPD-Q), a tire rubber antioxidant derivative, accumulates in air, soil, and water and has been found in urine, blood, and cerebrospinal fluid, posing significant health risks. Although 6PPD-Q exhibits intestinal toxicity, its role in inflammatory bowel disease (IBD) remains unclear. The objective of this study was to identify key molecular targets of 6PPD-Q in IBD and to validate their involvement in 6PPD-Q-induced intestinal epithelial cell injury. U…

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Canadian Medical Students Interested in Radiology Report Greater Perceived Importance of Procedural Roles and Greater Career Sustainability Amid Artificial Intelligence

Rationale and objectives Artificial intelligence (AI) integration and perceptions of radiology's procedural scope may influence medical students' interest in radiology. We aimed to determine whether perceptions of radiology's scope and AI integration differed according to student interest in radiology. Methods An anonymous cross-sectional survey containing binary, Likert-scale, and open-ended questions was distributed to medical students at a Canadian institution (N=73; 19.7% effective response rate). Responses…

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"Reports in Medical Illustration (REMIL) in Musculoskeletal Radiology: An Evaluation of Evolving AI Models"

Objective Radiology reports remain predominantly text-based, requiring clinicians and patients to mentally reconstruct imaging findings. Reports in Medical Illustration (REMIL) represent an emerging approach in which artificial intelligence (AI) generates simplified visual summaries directly from report text. This study aimed to evaluate the feasibility, anatomical accuracy, and clinical utility of AI-generated REMIL in musculoskeletal (MSK) radiology. Methods Twenty-five MSK imaging cases were selected. Identic…

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Digital pathology, image analysis, and artificial intelligence in liver disease

Advances in digital pathology, image analysis, and artificial intelligence (AI) are rapidly transforming how pathologists and researchers interact with tissue samples and enable the development of diagnostic tools that harness high-resolution whole-slide images; these advances are in turn creating new opportunities for research, education, and routine clinical care globally. Liver disease is no exception, and digital pathology and AI have many applications in the diagnosis of liver cancer and liver diseases and…

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Digital pathology, image analysis, and artificial intelligence in liver disease

Predicting longitudinal depressive symptom trajectories among older Chinese adults with chronic health conditions: An interpretable machine learning study

Objective This study leveraged interpretable machine learning (ML) to map heterogeneous trajectories of depressive symptoms in Chinese older adults with chronic diseases, aiming to develop an interpretable, prediction-oriented framework for personalized mental health interventions. Methods We analyzed four-wave longitudinal data from 5492 participants in the China Health and Retirement Longitudinal Study. Following trajectory identification, 10 ML algorithms were compared. A 50-iteration bootstrap Recursive Feat…

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Prognostic Value of Admission D-dimer Levels and Total Bleeding Volume in Aneurysmal Subarachnoid Hemorrhage: A Retrospective Cohort Study with Machine Learning-Based Modeling

Background Plasma D-dimer levels are independently associated with poor prognosis following aneurysmal subarachnoid hemorrhage (aSAH). However, the underlying mechanisms contributing to early D-dimer elevation remain unclear. This study aimed to evaluate the association between admission D-dimer levels and total bleeding volume (TBV) and to further explore their combined predictive power for functional outcomes using interpretable machine learning approaches. Methods We analyzed data from 473 patients with aSAH…

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Prognostic Value of Admission D-dimer Levels and Total Bleeding Volume in Aneurysmal Subarachnoid Hemorrhage: A Retrospective Cohort Study with Machine Learning-Based Modeling