Dermatology

Does Artificial Intelligence Make Dermatologists Less Intelligent?

Artificial intelligence is transforming dermatology, but may introduce risks of cognitive offloading and automation bias. Excessive reliance on AI could weaken independent clinical reasoning, particularly among trainees. Rather than replacing physician judgment, AI should function as a cognitive partner. Responsible integration and AI literacy can enhance dermatologists' efficiency, knowledge, and...

An Explainable Computer-Aided Framework for Skin Lesion Classification Using Deep Learning.

The utilization of deep convolutional neural networks for the purpose of diagnosing diseases in the skin area has proven to yield similar accuracy levels to those obtained by dermatologists in different studies. Nevertheless, many challenges are still present, including underperformance and poor generalization in some cases, as well as low interpretability related to the use of black box models. T...

[Synergistic learnable frequency and multi-scale spatial network for lightweight skin cancer classification].

Early screening of skin cancer is crucial to the survival rate of patients. Although deep learning has made significant progress in dermoscopic image ...

Diagnosis of Clinically Significant Prostate Cancer in Multiparametric MRI with Pseudo-Localization of Suspected Lesion.

To develop a two-stage diagnostic framework using pseudo-localization for patient-level diagnosis of clinically significant prostate cancer (csPCa) on...

Artificial Intelligence Detection of Interval Breast Cancers on False-negative Screening Mammograms: A Retrospective Lesion-level Analysis.

RATIONALE AND OBJECTIVES: Interval breast cancers (IBCs) occur after false-negative (FN) screening mammograms and can be diagnostically challenging. T...

International Application of Artificial Intelligence for Lesion Detection on Digital Breast Tomosynthesis: Comparing Western and Eastern Databases.

RATIONALE AND OBJECTIVES: The international application of artificial intelligence (AI) for lesion detection based on digital breast tomosynthesis (DB...

PRECISE v2 in active surveillance prostate MRI: toward a structured lexicon for longitudinal interpretation.

Active surveillance increasingly incorporates prostate MRI for longitudinal assessment of lesion stability or progression, yet terminology for serial ...

EffiSegNet: Advances in Automated Segmentation of Actinic Cheilitis and Lip Squamous Cell Carcinoma in Clinical Photographs.

BACKGROUND: Actinic cheilitis (AC) is a potentially malignant oral disorder linked to lip squamous cell carcinoma (LSCC). Clinical diagnosis is hinder...

Neoantigen in cancer immunotherapy: mechanism, therapeutic strategies and future perspectives.

Neoantigens are tumor-specific antigens resulting from genetic, transcriptomic, and proteomic changes, making them a promising avenue for personalized...

Artificial Intelligence-based 3D Assessment of Alopecia Areata Using Clinical Photographs.

Single-shot T2-Weighted Turbo Spin-echo Imaging of the Liver Improves Lesion Characterization Compared with Free-breathing Acquisition.

RATIONALE AND OBJECTIVES: T2-weighted imaging is essential for liver magnetic resonance imaging (MRI), but conventional free-breathing turbo spin-echo...

Exploring and validating hub genes associated with atopic dermatitis through weighted gene co-expression network analysis and machine learning.

This study aimed to identify novel biomarkers of atopic dermatitis (AD) and investigate their pathogenic mechanisms. We analyzed 7 AD-related datasets...

Single-photon emission computed tomography/computed tomography-based radiomics of bone metastases for predicting epidermal growth factor receptor mutation status and targeted therapy efficacy in non-small-cell lung cancer.

BACKGROUND: This study aimed to develop a single-photon emission computed tomography/computed tomography (SPECT/CT)-based radiomics model using bone m...

Age-adjusted machine learning identifies facial skin microbes associated with skin quality among Korean women.

Recognizing specific microbes that significantly influence skin quality is becoming an essential aspect of personalized skincare. However, conventiona...

Artificial intelligence in diabetic retinopathy: from automated screening to risk-stratified care.

Diabetic retinopathy (DR) is a leading cause of preventable blindness worldwide. The rising prevalence of diabetes has strained conventional screening...

A Multiparametric MRI‑Based Deep Learning Model for Binary Triage of Lung Versus Non‑lung Brain Metastases: A Multicenter Proof‑of‑Concept Study.

Identifying the origin of brain metastases (BM) is essential for personalized treatment, particularly in patients with carcinoma of unknown primary or...

Computational methods, clinical evidence, and translational readiness for artificial intelligence for HER2 assessment using silver in situ hybridization (SISH).

Accurate determination of human epidermal growth factor receptor 2 (HER2) gene amplification is a cornerstone of precision oncology in breast cancer a...

[Reflections on surgical management strategies for locally advanced gastric cancer after neoadjuvant immunotherapy].

Neoadjuvant immunotherapy is reshaping the treatment landscape of locally advanced gastric cancer (GC). The central clinical challenge has expanded fr...

Automated segmentation of postsurgical resection cavities on magnetic resonance imaging in focal epilepsy: A Multicentre Epilepsy Lesion Detection study.

OBJECTIVE: Quantitative assessment of extent of tissue resection following epilepsy surgery requires accurate delineation of the resection cavity on p...

Reflectance Confocal Microscopy Versus Line-Field Confocal Optical Coherence Tomography in Facial and Scalp Lesions Suspicious for Basal Cell Carcinoma: A Histopathology-Referenced Multi-Reader Study.

BACKGROUND: Non-invasive imaging techniques can support assessment of lesions suspicious for basal cell carcinoma (BCC) when dermoscopy is inconclusiv...