Imagine a future where your medical history could help predict what health conditions you might face in the next two decades. Researchers have developed a generative AI model that uses large-scale health records to estimate how human health may change over time. It can forecast the risk and timing of over 1,000 diseases and predict health outcomes over a decade in advance.
A project at Lund University in Sweden has trained an AI model to identify breast cancer patients who could be spared from axillary surgery. The model analyses previously unutilised information in mammograms and pinpoints with high accuracy the individual risk of metastasis in the armpit. A newly completed study shows that the model indicates that just over 40 per cent of today’s axillary surgery procedures could be avoided.
Stanford Medicine researchers have developed an artificial intelligence (AI) tool to help scientists better plan gene-editing experiments. The technology, CRISPR-GPT, acts as a gene-editing “copilot” supported by AI to help researchers - even those unfamiliar with gene editing - generate designs, analyze data and troubleshoot design flaws.
Whether it’s an infection by a novel virus or memory-stealing plaques forming in the brains of Alzheimer’s patients, visualizing disease processes in the body is the first step toward alleviating human suffering.
Depression is one of the most common mental health challenges, but its early signs are often overlooked. It is often linked to reduced facial expressivity. However, whether mild depression or subthreshold depression (StD) (a mild state of depressive symptoms that does not meet the criteria for diagnosis but is a risk factor for developing depression) is associated with changes in facial expressions remains unknown.
Altuna Akalin and his team at the Max Delbrück Center have developed a new tool to more precisely guide cancer treatment. Described in a paper published in Nature Communications, the tool, called Flexynesis, uses deep neural networks and evaluates multi modal data.
Implementing artificial intelligence (AI) into NHS hospitals is far harder than initially anticipated, with complications around governance, contracts, data collection, harmonisation with old IT systems, finding the right AI tools and staff training, finds a major new UK study led by UCL researchers.