OBJECTIVE: The objective of this research is to survey the requirements of Oriental Medical Informatics, and to suggest a direction that Oriental Medical Informatics development may take. METHODS: Consumers and medical experts were randomly selected, and 14 uestions for consumers and 17 questions for medical experts were sent to respondents by mail and e-mail. RESULTS: Both consumers and medical experts were greatly concerned with the systemized dissemination of Oriental Medical Information, but they were not satisfied with it because of the perceived low quality of the information. Medical experts responded that they need standards and statistical evidences for Oriental Medicine. Consumers demanded good-quality information about diseases and health management. CONCLUSION: To carry out Oriental Medical Informatics, it is necessary to conduct a joint research between the sectors of Oriental Medicine and Information Technology, followed by the development of a standard information infrastructure. Oriental Medicine must also have standards in terms of medical data content, data format, and data communication, to ensure the reliability of the disseminated information on Oriental Medicine.
Surveys and Questionnaires ; Electronic Mail ; Joints ; Medical Informatics ; Medicine, East Asian Traditional* ; Postal Service

Purpose: Assessing the status of metastasis in sentinel lymph nodes (SLNs) by pathologists is an essential task for the accurate staging of breast cancer. However, histopathological evaluation of sentinel lymph nodes by a pathologist is not easy and is a tedious and time-consuming task. The purpose of this study is to review a challenge competition (HeLP 2018) to develop automated solutions for the classification of metastases in hematoxylin and eosin–stained frozen tissue sections of SLNs in breast cancer patients. Materials and Methods: A total of 297 digital slides were obtained from frozen SLN sections, which include post–neoadjuvant cases (n = 144, 48.5%) in Asan Medical Center, South Korea. The slides were divided into training, development, and validation sets. All of the imaging datasets have been manually segmented by expert pathologists. A total of 10 participants were allowed to use the Kakao challenge platform for six weeks with two P40 GPUs. The algorithms were assessed in terms of the AUC (area under receiver operating characteristic curve). Results: The top three teams showed 0.986, 0.985, and 0.945 AUCs for the development set and 0.805, 0.776, and 0.765 AUCs for the validation set. Micrometastatic tumors, neoadjuvant systemic therapy, invasive lobular carcinoma, and histologic grade 3 were associated with lower diagnostic accuracy. Conclusion In a challenge competition, accurate deep learning algorithms have been developed, which can be helpful in making frozen diagnosis of intraoperative sentinel lymph node biopsy. Whether this approach has clinical utility will require evaluation in a clinical setting