Unveiling the power of convolutional neural networks in melanoma diagnosis

Convolutional neural networks are a type of deep learning algorithm. They are mostly applied in visual recognition and can be used for the identification of melanomas. Multiple studies have evaluated the performance of convolutional neural networks, and most algorithms match or even surpass the accuracy of dermatologists. However, only 23.8% of dermatologists have good or excellent knowledge of the topic. We believe that the lack of knowledge physicians experience regarding artificial intelligence is an obstacle to its clinical implementation. We describe how a convolutional neural network differentiates a benign from a malignant lesion. We systematically searched the Web of Science, Medline (PubMed), and The Cochrane Library on the 9th February, 2022. We focused on articles describing the role and use of artificial intelligence in melanoma recognition between 2017 and 2022, using the following MeSH terms: "melanoma," "diagnosis," and "artificial intelligence". Traditional machine learning algorithms comprise different parts which must preprocess, segment, extract features and classify the lesion into benign or malignant. Deep learning algorithms can perform these steps simultaneously, which significantly enhances efficiency. Convolutional neural networks include a convolutional layer, a pooling layer, and a fully connected layer. Convolutional and pooling layers extract features from the lesion and reduce computational power, whereas fully connected layers classify the image into two or more categories. Additionally, we suggest that further studies should be performed to accelerate the clinical implementation of artificial intelligence, to create comprehensive datasets and to generate explainable algorithms.

[Health care in non-insulin dependent diabetics in lower Normandy]

To contribute to better quality health care for patients with type 2 diabetes mellitus living in the French Department Basse-Normandie, the Regional Union of General Practitioners (URML) and the Regional Union of the Nation Health Insurance (URCAM) studied ambulatory practices in this population in 1999. Eight hundred diabetics whose health care reimbursements suggested they were not followed by a specialized center were studied: 400 received a mailed questionnaire and for the 400 others, another questionnaire was filed out by the primary care physician. The physicians' responses demonstrated that they do not feel certain recommended complementary investigations are always necessary for following diabetic patients (microalbuminuria, glycated hemoglobin, search for neuropathy, ophthalmology consultation). Interaction with diabetologists, nutritionists and chiropodists was found to be insufficient. Globally, the responses of the diabetics was coherent with the physicians' statements, providing further precision concerning health care delivery and propositions for improvement. These results can be used to better target education programs for associations, specialists and general practitioners.