RESUMO
Ulcerative colitis (UC) is an intractable disease that affects young adults. Histological findings are essential for its diagnosis; however, the number of diagnostic pathologists is limited. Herein, we used a no-code artificial intelligence (AI) platform "Teachable Machine" to train a model that could distinguish between histological images of UC, non-UC coloproctitis, adenocarcinoma, and control. A total of 5100 histological images for training and 900 histological images for testing were prepared by pathologists. Our model showed accuracies of 0.99, 1.00, 0.99, and 0.99, for UC, non-UC coloproctitis, adenocarcinoma, and control, respectively. This is the first report in which a no-code easy AI platform has been able to comprehensively recognize the distinctive histologic patterns of UC.
RESUMO
Recently, a medical care function has become differentiated from medical system's perspectives, and accordingly the number of home-care patients has been increasing. Pharmacists working at drug stores not only prescribe drugs for home-care patients but also visit them at home to help them learn how to properly take their medications and to have correct dosages. To do this, drug store pharmacists have begun sharing information on their patients through care conferences attended with doctors at clinics and nurses at visiting nursing stations. In the present study with regard to the administration of medications for patients in the end-stage cancer, we described how pharmacists provided information on prescribed drugs to the wives and daughters of these patients. This report highlighted the issues surrounding these cases.