Latest AI and machine learning research in dermatology for healthcare professionals.
The KIADEKU project combines datascience and the clinical expertise of wound experts to develop and evaluate an AI-application for incontinence-associated-dermatitis (IAD) and pressure ulcer (PU) wound care. The evaluation study is a controlled, non-randomized clinical trial that investigates the effects of the AI-based system on wound care time, guideline adherence and the task load of the nurse,...
The integration of artificial intelligence (AI) into healthcare is revolutionising the industry by enhancing diagnostic accuracy, personalising treatment strategies, and improving administrative efficiency. This study aims to evaluate the impact of AI interventions on health outcomes across various medical applications. A scoping review was conducted using relevant search terms, focusing exclusive...
Currently, dermatologists need to check numerous image reports (high resolution) for diagnosing skin conditions, and Machine Learning (ML) models can ...
Deep learning models hold significant promise for disease diagnosis but often lack transparency in their decision-making processes, limiting trust and...
Recent advances in artificial intelligence (AI) and multimodal data collection are revolutionizing dermatology. Generative AI and machine learning a...
Dermatological diagnosis represents a complex multimodal challenge that requires integrating visual features with specialized clinical knowledge. Wh...
Non-linear laser spectroscopy methods such as two-dimensional infrared (2D-IR) produce large, information-rich datasets, while developments in laser t...
BACKGROUND/OBJECTIVES: This study explored the views of dermatologists in Australia on the use of Artificial Intelligence (AI) in dermatology.
Pregnancy-associated dermatologic conditions emerge from intricate hormonal, immunologic, genetic, and environmental changes, often complicating mater...
Engineered scaffold-based proteins that bind to concrete targets with high affinity offer significant advantages over traditional antibodies in theran...
Recent advances in artificial intelligence (AI) and multimodal data collection are revolutionizing dermatology. Generative AI and machine learning app...
Cutaneous spindle cell (CSC) lesions encompass a spectrum from benign to malignant neoplasms, often posing significant diagnostic challenges. Computer...
Skin hemangioma is a tumor originating from skin blood vessels, which often occurs in infants and children. Brachytherapy with the P-based radionuclid...
The rising incidence of skin cancer, coupled with limited public awareness and a shortfall in clinical expertise, underscores an urgent need for adv...
The advent of next-generation antibody-drug conjugates (ADCs), particularly trastuzumab deruxtecan (T-DXd), has transformed our understanding of human...
Deep learning reconstruction (DLR) provides an elegant solution for MR acceleration while preserving image quality. This advancement is crucial for bo...
Manual interpretation of CT images for bone metastasis (BM) detection in primary cancer remains challenging. We present an automated Bone Lesion Detec...
The prevalence of dementia is growing worldwide due to the fast ageing of the population. Dementia is an intricate illness that is frequently produced...
Atopic dermatitis is a chronic skin disease, causing itching and recurrent eczematous lesions. In Danish national register data, adults with atopic de...
Many aspects of our life are affected by technology. One of the most discussed advancements of modern technologies is artificial intelligence. It invo...