Recent studies have demonstrated the usefulness of convolutional neural networks (CNNs) to classify images of melanoma, with accuracies comparable to those achieved by dermatologists. However, the performance of a CNN trained with only clinical images of a pigmented skin lesion in a clinical image classification task, in competition with dermatologists, has not been reported to date. In this study, we extracted 5846 clinical images of pigmented skin lesions from 3551 patients. Pigmented skin lesions included malignant tumors (malignant melanoma and basal cell carcinoma) and benign tumors (nevus, seborrhoeic keratosis, senile lentigo, and hematoma/hemangioma). We created the test dataset by randomly selecting 666 patients out of them and picking one image per patient, and created the training dataset by giving bounding-box annotations to the rest of the images (4732 images, 2885 patients). Subsequently, we trained a faster, region-based CNN (FRCNN) with the training dataset and checked the performance of the model on the test dataset. In addition, ten board-certified dermatologists (BCDs) and ten dermatologic trainees (TRNs) took the same tests, and we compared their diagnostic accuracy with FRCNN. For six-class classification, the accuracy of FRCNN was 86.2%, and that of the BCDs and TRNs was 79.5% (p = 0.0081) and 75.1% (p < 0.00001), respectively. For two-class classification (benign or malignant), the accuracy, sensitivity, and specificity were 91.5%, 83.3%, and 94.5% by FRCNN; 86.6%, 86.3%, and 86.6% by BCD; and 85.3%, 83.5%, and 85.9% by TRN, respectively. False positive rates and positive predictive values were 5.5% and 84.7% by FRCNN, 13.4% and 70.5% by BCD, and 14.1% and 68.5% by TRN, respectively. We compared the classification performance of FRCNN with 20 dermatologists. As a result, the classification accuracy of FRCNN was better than that of the dermatologists. In the future, we plan to implement this system in society and have it used by the general public, in order to improve the prognosis of skin cancer.

In recent years, advances in artificial intelligence (AI) technology have led to the rapid clinical implementation of devices with AI technology in the medical field. More than 60 AI-equipped medical devices have already been approved by the Food and Drug Administration (FDA) in the United States, and the active introduction of AI technology is considered to be an inevitable trend in the future of medicine. In the field of oncology, clinical applications of medical devices using AI technology are already underway, mainly in radiology, and AI technology is expected to be positioned as an important core technology. In particular, “precision medicine,” a medical treatment that selects the most appropriate treatment for each patient based on a vast amount of medical data such as genome information, has become a worldwide trend; AI technology is expected to be utilized in the process of extracting truly useful information from a large amount of medical data and applying it to diagnosis and treatment. In this review, we would like to introduce the history of AI technology and the current state of medical AI, especially in the oncology field, as well as discuss the possibilities and challenges of AI technology in the medical field.

Advanced malignant melanoma (MM) is treated with immune checkpoint inhibitor (ICI) therapy, which often results in several immune-related adverse events. Fulminant type 1 diabetes mellitus (T1DM) is a rare, rapidly progressive, life-threatening disease. Here, we summarize 8 cases of MM with ICI-induced T1DM and describe one case that developed fulminant T1DM due to nivolumab therapy. We retrospectively reviewed patients treated with ICI from 2014 to 2021 at our hospital. The clinical features and risk factors of ICI-induced T1DM were discussed. ICIs were administered to 426 MM patients at our hospital. Among these, nivolumab was administered in 5 cases, pembrolizumab in 1 case, and the combination of nivolumab and ipilimumab in 2 cases. The frequency of ICI-associated T1DM was 1.88%. The mean glycated hemoglobin level at T1DM onset was 8.0 ± 1.0%. Of the patients, 75% were diagnosed with fulminant T1DM, 62.5% developed diabetic ketoacidosis, and 25% had glutamic acid decarboxylase (GAD) antibodies (an early predictive marker for T1DM). The mean interval between the first ICI administration and T1DM development was 201 ± 187 days. The mean duration of resumption was 13 ± 7 days. We should monitor for T1DM development following treatment with ICIs. ICI can be continued to be used to treat MM if insulin therapy successfully controls T1DM. A 67-year-old patient who received adjuvant nivolumab therapy developed fulminant T1DM and thyrotoxicosis 57 days later and tested positive for GAD antibodies. Subsequently, he developed hypophysitis and an isolated adrenocorticotropin deficiency. He continued receiving nivolumab along with self-injected insulin without developing recurrence.