AI Medical Compendium Topic

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Skin Diseases

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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 ...

Deep Learning-Based Synthetic Skin Lesion Image Classification.

Studies in health technology and informatics
Advances in general-purpose computers have enabled the generation of high-quality synthetic medical images that human eyes cannot differ between real and AI-generated images. To analyse the efficacy of the generated medical images, this study propose...

Large Language and Vision Assistant in dermatology: a game changer or just hype?

Clinical and experimental dermatology
The integration of artificial intelligence (AI) in healthcare, particularly in the field of dermatology, has experienced significant progress through the creation of advanced tools such as the Large Language and Vision Assistant (LLaVA). This compreh...

Derm-T2IM: Harnessing Synthetic Skin Lesion Data via Stable Diffusion Models for Enhanced Skin Disease Classification using ViT and CNN.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
This study explores the utilization of Dermatoscopic synthetic data generated through stable diffusion models as a strategy for enhancing the robustness of machine learning model training. Synthetic data plays a pivotal role in mitigating challenges ...

Generative artificial intelligence in dermatology: Recommendations for future studies evaluating the clinical knowledge of models.

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)

Classifying real-world macroscopic images in the primary-secondary care interface using transfer learning: implications for development of artificial intelligence solutions using nondermoscopic images.

Clinical and experimental dermatology
BACKGROUND: The application of deep learning (DL) to diagnostic dermatology has been the subject of numerous studies, with some reporting skin lesion classification performance on curated datasets comparable to that of experienced dermatologists. Mos...