Dermatology

Dynamic AI-assisted Ipsilateral Tissue Matching for Digital Breast Tomosynthesis

To compare Digital Breast Tomosynthesis (DBT) tissue matching errors with and without artificial intelligence (AI) assistance to typical screen-detected breast tumor sizes, evaluating whether AI ameliorates lesion mislocalization beyond tumor boundaries, especially for nonexpert radiologists. The technology category is deep learning. This multicenter retrospective feasibility study conducted in Ap...

Predicting Prostate Cancer Without a Prostate: A Potential Problem with AI

Machine learning (ML) algorithms have demonstrated great potential for the identification and classification of prostate cancer from Magnetic Resonance (MR) Imaging data. Many of these algorithms remain a “black-box,” however, and debate persists as to how and if they should be explained. This study hypothesized that a widely-used family of methods, Convolutional Neural Networks (CNNs), may identi...

Tumor-infiltrating lymphocytes in breast cancer through artificial intelligence: biomarker analysis from the results of the TIGER challenge

The prognostic significance of tumor-infiltrating lymphocytes (TILs) in breast cancer has been recognized for over a decade. Although histology-based ...

irAE-GPT: Leveraging large language models to identify immune-related adverse events in electronic health records and clinical trial datasets

Large language models (LLMs) have emerged as transformative technologies, revolutionizing natural language understanding and generation across various...

Patient Attitudes Toward Artificial Intelligence in Cancer Care: A Scoping Review

To synthesize existing literature on patient attitudes toward AI in cancer care and identify knowledge gaps that can inform future research and clinic...

Proteome-wide autoantibody screening and holistic autoantigenomic analysis unveil COVID-19 signature of autoantibody landscape

This study presents “aUToAntiBody Comprehensive Database (UT-ABCD)”, a comprehensive catalog of autoantibody profiles in 284 human individuals. The su...

Behavioral Clusters and Lesion Distributions in Ischemic Stroke, based on NIHSS Similarity Network

Stroke is a leading cause of death and disability. The subsequent dysfunctions relate to brain lesion location. The complex relationship between behav...

Quantifying cortical lesions in large legacy multiple sclerosis clinical trial MRI datasets using multi-contrast post-processing and deep learning

Multiple sclerosis (MS) is a chronic neurological disease affecting both white and gray matter of the central nervous system. Despite the well-establi...

Transformers in Skin Lesion Classification and Diagnosis: A Systematic Review

This systematic review aims to provide a comprehensive overview of the current state of research on the application of transformers in skin lesion cla...

Optic Nerve Lesion Volume, White Matter Hyperintensities, and Brain Volumetrics in Multiple Sclerosis: A Multi-Sequence MRI-Based Analysis

To investigate the relationship between optic nerve lesion volume (ONLV), measured on double inversion recovery (DIR) MRI, and other radiological biom...

Artificial Intelligence-based Diagnosis of Kaposi Sarcoma using Photographs in Dark-skinned Patients

Advanced-stage disease at the time of diagnosis, with resultant high mortality, is among the most urgent issues for HIV-related Kaposi sarcoma (KS) in...

Bridging Data Gaps in Oncology: Large Language Models and Collaborative Filtering for Cancer Treatment Recommendations

Patients with rare cancers face substantial challenges due to limited evidence-based treatment options, resulting from sparse clinical trials. Advance...

Corr-A-Net: Interpretable Attention-Based Correlated Feature Learning framework for predicting of HER2 Score in Breast Cancer from H&E Images

Human epidermal growth factor receptor 2 (HER2) expression is a critical biomarker for assessing breast cancer (BC) severity and guiding targeted anti...

Independent Evaluation of Deep Learning Models for Detecting Focal Cortical Dysplasia

The objective of this study is to perform an independent assessment of the diagnostic utility of three state-of-the-art tools for the detection of foc...

Clinicodemographic Prediction of Overall Survival in Patients with Head and Neck Merkel Cell Carcinoma: A Machine Learning Approach

Merkel cell carcinoma (MCC) is a rare cutaneous neuroendocrine malignancy with a higher case-fatality rate than melanoma. The prognosis of MCC is comp...

Histopathology-based Protein Multiplex Generation using Deep Learning

Multiplexed protein imaging offers valuable insights into interactions between tumors and their surrounding tumor microenvironment (TME), but its wide...

Machine learning prediction for early-stage melanoma outcomes: recurrence-free survival, disease-specific survival, and overall survival

This study compared machine-learning models for predicting recurrence-free survival (RFS), disease-specific survival (DSS), and overall survival (OS) ...

A Standard Framework for Converting Coronary Angiography Reports into Machine-Readable Format Using Large Language Models

Coronary angiography (CAG) reports contain many details about coronary anatomy, lesion characteristics, and interventional procedures. However, their ...

A Clinical Neuroimaging Platform for Rapid, Automated Lesion Detection and Personalized Post-Stroke Outcome Prediction

Predicting long-term functional outcomes for individuals with stroke is a significant challenge. Solving this challenge will open new opportunities fo...

AI-detected tumor-infiltrating lymphocytes for predicting outcomes in anti-PD1 based treated melanoma

Easy and accessible biomarkers to predict response to immune checkpoint inhibition (ICI)-treated melanoma are limited. To evaluate artificial intellig...