We developed an AI-based diagnostic model that did well in detecting lymph node metastases, particularly micrometastases. The LNMDM showed substantial potential for clinical applications in improving the accuracy and efficiency of pathologists' work.

Recent research has demonstrated that large language models can evolve with scale, moving beyond simple pattern matching to do higher-level reasoning and produce realistic visuals and text. There has been some research on language models trained on protein sequences, but when they are scaled up, little is known about what they learn about biology. Researchers at Meta AI have developed one of the most significant language models of protein to date, ESMFold, that can predict protein structure from a gene sequence. With an order-of-magnitude faster inference time, ESMFold, based on a 15B parameter Transformer model, delivers accuracy comparable to other state-of-the-art models. The paper describing the model and several tests carried out as part of this study have also been published on bioRxiv.

They call it artificial intelligence--not because the intelligence is somehow fake. It's real intelligence, but it's still made by humans. That means AI--a power tool that can add speed, efficiency, insight and accuracy to a researcher's work--has many limitations. It's only as good as the methods and data it has been given. On its own, it doesn't know if information is missing, how much weight to give differing kinds of information or whether the data it draws on is incorrect or corrupted.