The top stories in endocrinology last week focused on how artificial intelligence, or AI, could be a game-changer in predicting nonalcoholic fatty liver disease and detecting thyroid nodules. AI tool for nonalcoholic fatty liver disease could'make biopsies history' The detection and diagnosis of thyroid nodules may be on the cusp of a technological overhaul as a growing body of research on AI and machine learning tools aims to bring about more efficiency and accuracy while decreasing cost and improving ease-of-use. The Dermatologic and Ophthalmic Drugs Advisory Committee of the FDA voted 12-0 last week in favor of recommending approval of a biologics license application for teprotumumab, an experimental human monoclonal antibody shown to dramatically reduce the most debilitating symptoms of Graves' orbitopathy. The Society for Endocrinology and the British Thyroid Association issued a joint statement urging caution when interpreting a recent study linking radioactive iodine therapy to cancer mortality among people with hyperthyroidism. Japanese adults with type 2 diabetes assigned a long-term low-dose aspirin regimen did not lower their risk for dementia vs. similar adults who did not routinely take aspirin, according to a post hoc analysis of the Japanese Primary Prevention of Atherosclerosis with Aspirin for Diabetes trial.

Recently, ontology stream reasoning has been introduced as a multidisciplinary approach, merging synergies from Artificial Intelligence, Database and World-Wide-Web to reason on semantics-augmented data streams, thus a way to answering questions on real time events. However existing approaches do not consider stream change diagnosis i.e., identification of the nature and cause of changes, where explaining the logical connection of knowledge and inferring insight on time changing events are the main challenges. We exploit the Description Logics (DL)-based semantics of streams to tackle these challenges. Based on an analysis of stream behavior through change and inconsistency over DL axioms, we tackled change diagnosis by determining and constructing a comprehensive view on potential causes of inconsistencies. We report a large-scale evaluation of our approach in the context of live stream data from Dublin City Council.