Dermatology

Evaluation of an AI-powered tool in improving lesion visualization on standard-dose contrast-enhanced brain MRI: a retrospective, multicenter study.

BACKGROUND AND PURPOSE: Artificial intelligence (AI) algorithms have been used to synthesize standard-dose images from low-dose images in brain MRI, but have less been evaluated to boost standard-dose contrast to approximate higher-dose effect. This study aims to evaluate the performance of a deep learning-based post-processing tool (AiMIFY) in enhancing contrast and improving lesion visualization...

Harmonizing Multi-Institutional Clinical Documentation Using Natural Language Processing in Neurofibromatosis Type 1.

BACKGROUND AND OBJECTIVES: Machine learning (ML) and natural language processing (NLP) approaches are increasingly used to support nuanced phenotyping, surveillance, and trial readiness using electronic health records (EHRs) in neurologic disease. However, inconsistent clinical documentation limits data harmonization and model performance, particularly in complex heterogeneous disorders such as ne...

Artificial intelligence in dermatologic pharmacokinetics: quantitative modeling, clinical translation, and optimization of precision therapeutics.

INTRODUCTION: Dermatology is rapidly transitioning from broad-spectrum therapies toward biologics, nanotechnology-based drug delivery, and precision t...

AI-Driven Digital Tools for Atopic Dermatitis: A Scoping Review.

BACKGROUND: Atopic dermatitis (AD) affects around 20% of children and up to 10% of adults. Its fluctuating course, severe pruritus, and impact on slee...

Deep learning for CT-less attenuation correction synthesis in non-small cell lung cancer: A proof-of-concept evaluated for quantitative and physical characteristics.

To develop and rigorously validate a deep learning framework for CT-free positron emission tomography (PET) attenuation correction in non-small cell l...

Ultra-low dose chest-abdomen-pelvis CT with deep-learning image reconstruction for cancer follow-up: Impact on image quality and lesion detection.

PURPOSE: The purpose of this study was to compare the image quality and lesion detection between ultra-low dose (ULD) chest-abdomen-pelvis computed to...

From decay to discovery: A new antimicrobial peptide from Chrysomya megacephala (Diptera: Calliphoridae) larvae.

The larvae of Chrysomya megacephala (Diptera: Calliphoridae) thrive in environments rich in decaying organic matter and dead animals, which are often ...

Automated breast ultrasound (ABUS) meets artificial intelligence: a new accurate classification tool for BIRADS 3-4 breast lesions.

PURPOSE: Breast ultrasound (US) has often interpretation challenges (BIRADS 3-BIRADS 4 lesions), leading to a high demand for core needle biopsies (CN...

An Organ-Guided Lightweight Multi-frame Integration Network for Real-Time Abdominal Lesion Detection in Ultrasound Video Streams.

OBJECTIVE: Abdominal ultrasound is widely used for the routine screening of hepatobiliary and renal diseases because it is safe, inexpensive and broad...

Do not smile when acquiring consumer selfies for multi-attribute skin profiling and baseline deep learning evaluation.

The increasing demand for personalized skincare solutions highlights a significant gap: many consumers struggle to find suitable products without prof...

[Diagnostic efficacy and teaching value of artificial intelligence-assisted diagnostic systems in cervical liquid-based cytology screening].

Objective: To systematically evaluate the diagnostic performance of an artificial intelligence (AI)-assisted diagnostic system in identifying squamous...

Willingness to pay among individuals from the Dutch general population for artificial intelligence based apps for skin cancer detection: a survey-based mixed-methods study.

BACKGROUND: Rising skin cancer incidence increases pressure on the healthcare system. Artificial intelligence (AI)-based mobile health applications (m...

Human-AI Interaction With AI-Assisted Tumor Overlays in Pediatric Whole-Body Magnetic Resonance Imaging: Exploratory Reader Study.

BACKGROUND: AI tools have the potential to enhance personalized clinical care, particularly in radiology. However, their integration into clinical wor...

Small lesion, big challenge? Diagnosis of small diameter melanocytic lesions by dermatologists using AI support.

BACKGROUND: Dermoscopy enhances melanoma detection, but small-diameter melanomas (SDMs) remain diagnostically challenging. Convolutional neural networ...

Advanced imaging in systemic sclerosis: distinguishing activity from damage across organ systems.

PURPOSE OF REVIEW: A central challenge in systemic sclerosis (SSc) is the inability to distinguish active, potentially reversible disease, from damage...

Frequency of use and attitudes toward AI-based health counseling among patients attending the dermatology outpatient clinic: a cross-sectional study.

PURPOSE: To quantify the prevalence of pre-visit AI consultation among dermatology outpatients and to assess associated attitudes, anxiety changes, an...

Non-invasive prediction of dupilumab response in facial atopic dermatitis using early post-treatment reflectance confocal microscopy features.

BACKGROUND: Non-invasive prediction of the efficacy of dupilumab on facial lesions in patients with atopic dermatitis (AD) was unresolved. OBJECTIVE: ...

Clinical-guided deep learning framework for diabetic retinopathy: integrating lesion-aware attention, adversarial augmentation, and uncertainty quantification.

The global prevalence of diabetic retinopathy (DR) is increasing in parallel with the rising burden of diabetes, posing a substantial public health ch...

Artificial Intelligence-Based Assessment With Line-Field Confocal Optical Coherence Tomography (LC-OCT) During Topical Ruxolitinib Therapy for Vitiligo.

Response to letter regarding…letter to the editor-Evaluation of the clinical applicability of artificial intelligence in determining systemic therapy indications and treatment selection in psoriasis.