Deep Neural Frameworks Improve the Accuracy of General Practitioners in the Classification of Pigmented Skin Lesions

This study evaluated whether deep learning frameworks trained in large datasets can help non-dermatologist physicians improve their accuracy in categorizing the seven most common pigmented skin lesions. Open-source skin images were downloaded from the International Skin Imaging Collaboration (ISIC) archive. Different deep neural networks (DNNs) (n = 8) were trained based on a random dataset…

Artificial intelligence: a disruptive tool for a smarter medicine

Although highly successful, the medical R&D model is failing at improving people’s health due to a series of flaws and defects inherent to the model itself. A new collective intelligence, incorporating human and artificial intelligence (AI) could overcome these obstacles. Because AI will play a key role in this new collective intelligence, it is necessary…

Robust COVID-19-related condition classification network

COVID-19 can exponentially precipitate life-threatening emergencies as witnessed during the recent spreading of a novel coronavirus infection which can rapidly evolve into lung collapse and respiratory distress (among other various severe clinical conditions). Our study evaluates the performance of a tailor-designed deep convolutional network on the tasks of early detection and localization of radiological signs…

Deep neural frameworks improve the accuracy of general practitioners in the classification of pigmented skin lesions

Artificial intelligence can be a key tool in the context of assisting in the diagnosis of dermatological conditions, particularly when performed by general practitioners with limited or no access to high resolution optical equipment. This study evaluates the performance of deep convolutional neural networks (DNNs) in the classification of seven pigmented skin lesions. Additionally, it…