AIMC Topic: Skin Diseases

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Dermacen analytica: A novel methodology integrating multi-modal large language models with machine learning in dermatology.

International journal of medical informatics
OBJECTIVE: To design, implement, evaluate, and quantify a novel and adaptable Artificial Intelligence-empowered methodology aimed at supporting a dermatologist's workflow in assessing and diagnosing skin conditions, leveraging AI's deep image analyti...

Improving skin lesion classification through saliency-guided loss functions.

Computers in biology and medicine
Deep learning has significantly advanced computer-aided diagnosis, particularly in skin lesion classification. However, achieving high classification performance and providing explainable model predictions remain challenging in medical imaging. To ta...

Diagnostic tools and methods for dermatological assessment.

British journal of nursing (Mark Allen Publishing)
Advanced clinical practitioners (ACPs) play an essential role in dermatological care but often encounter challenges due to limited training in dermatological assessments and investigations. This two-part series aims to address these gaps by offering ...

Artificial intelligence in dermatology and healthcare: An overview.

Indian journal of dermatology, venereology and leprology
Many aspects of our life are affected by technology. One of the most discussed advancements of modern technologies is artificial intelligence. It involves computational methods which in some way mimic the human thought process. Just like other branch...

The use of ChatGPT in the dermatological field: a narrative review.

Clinical and experimental dermatology
Artificial intelligence (AI) encompasses the development of computer systems capable of tasks typically requiring human intelligence, such as visual perception, speech recognition, decision-making and language translation. Over time, numerous applica...

Artificial intelligence in dermatopathology: a systematic review.

Clinical and experimental dermatology
Medical research, driven by advancing technologies like artificial intelligence (AI), is transforming healthcare. Dermatology, known for its visual nature, benefits from AI, especially in dermatopathology with digitized slides. This review explores A...

ArsenicNet: An efficient way of arsenic skin disease detection using enriched fusion Xception model.

PloS one
Arsenic contamination of drinking water is a significant health risk. Countries such as Bangladesh's rural areas and regions are in the red alert zone because groundwater is the only primary source of drinking. Early detection of arsenic disease is c...

Attention-aware Deep Learning Models for Dermoscopic Image Classification for Skin Disease Diagnosis.

Current medical imaging
BACKGROUND: The skin, being the largest organ in the human body, plays a vital protective role. Skin lesions are changes in the appearance of the skin, such as bumps, sores, lumps, patches, and discoloration. If not identified and treated promptly, s...

Assessing diagnostic performance for common skin diseases using an AI-assisted tele-expertise platform: a proof of concept.

European journal of dermatology : EJD
Advancements in machine learning (ML) are making artificial intelligence more feasible in dermatology, with promising results for diagnosing skin cancers, though few studies cover common or inflammatory dermatoses. To evaluate the diagnostic accuracy...

Artificial intelligence in psychodermatology: A brief report of applications and impact in clinical practice.

Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)
BACKGROUND: This report evaluates the potential of artificial intelligence (AI) in psychodermatology, emphasizing its ability to enhance diagnostic accuracy, treatment efficacy, and personalized care. Psychodermatology, which explores the connection ...