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In a recent review published in the journal of Nature Medicine, researchers discussed the results of a two-year weekly effort to track and communicate significant developments in medical (artificial intelligence) AI. They included prospective studies as well as developments in medical image analysis that have narrowed the gap between research and implementation.
In a recent review published in the journal Nature Medicine, scientists discussed the results of a two-year weekly effort to track and communicate significant developments in medical AI. In particular, AI has been a game-changer in the crucial task of protein folding, which involves predicting the 3D structure of a protein from its chemical sequence. Improvements in protein structure prediction can reveal mechanistic information about a variety of events, including drug-protein interactions and mutation effects. With AI, non-invasive cancer screening, prognosis, and tumor origin identification are now possible. Moreover, deep learning has improved CRISPR-based gene editing by helping predict guide RNA activity and identifying anti-CRISPR protein families. According to one study, BioBERT, a model trained on a large corpus of medical literature, outperformed previous peers on natural language tasks such as answering biological questions. Â