AIMC Topic: Skin Neoplasms

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Comparing preferences for skin cancer screening: AI-enabled app vs dermatologist.

Social science & medicine (1982)
BACKGROUND AND AIM: Skin cancer is a major public health issue. While self-examinations and professional screenings are recommended, they are rarely performed. Mobile health (mHealth) apps utilising artificial intelligence (AI) for skin cancer screen...

Exploring a specialized programmed-cell death patterns to predict the prognosis and sensitivity of immunotherapy in cutaneous melanoma via machine learning.

Apoptosis : an international journal on programmed cell death
The mortality and therapeutic failure in cutaneous melanoma (CM) are mainly caused by wide metastasis and chemotherapy resistance. Meanwhile, immunotherapy is considered a crucial therapy strategy for CM patients. However, the efficiency of currently...

Histological interpretation of spitzoid tumours: an extensive machine learning-based concordance analysis for improving decision making.

Histopathology
The histopathological classification of melanocytic tumours with spitzoid features remains a challenging task. We confront the complexities involved in the histological classification of these tumours by proposing machine learning (ML) algorithms tha...

Optimizing time prediction and error classification in early melanoma detection using a hybrid RCNN-LSTM model.

Microscopy research and technique
Skin cancer is a terrifying disorder that affects all individuals. Due to the significant increase in the rate of melanoma skin cancer, early detection of skin cancer is now more critical than ever before. Malignant melanoma is one of the most seriou...

Current safeguards, risk mitigation, and transparency measures of large language models against the generation of health disinformation: repeated cross sectional analysis.

BMJ (Clinical research ed.)
OBJECTIVES: To evaluate the effectiveness of safeguards to prevent large language models (LLMs) from being misused to generate health disinformation, and to evaluate the transparency of artificial intelligence (AI) developers regarding their risk mit...

SASAN: ground truth for the effective segmentation and classification of skin cancer using biopsy images.

Diagnosis (Berlin, Germany)
OBJECTIVES: Early skin cancer diagnosis can save lives; however, traditional methods rely on expert knowledge and can be time-consuming. This calls for automated systems using machine learning and deep learning. However, existing datasets often focus...

Enhancing skin lesion classification with advanced deep learning ensemble models: a path towards accurate medical diagnostics.

Current problems in cancer
Skin cancer, including the highly lethal malignant melanoma, poses a significant global health challenge with a rising incidence rate. Early detection plays a pivotal role in improving survival rates. This study aims to develop an advanced deep learn...