AI tool spots blood cell abnormalities missed by doctors
Peer-Reviewed Publication
Updates every hour. Last Updated: 21-Dec-2025 00:11 ET (21-Dec-2025 05:11 GMT/UTC)
An AI tool that can analyse abnormalities in the shape and form of blood cells, and with greater accuracy and reliability than human experts, could change the way conditions such as leukaemia are diagnosed.
Complex digital images of tissue samples that can take an experienced pathologist up to 20 minutes to annotate could be analysed in just one minute using a new AI tool developed by researchers at the University of Cambridge. SMMILe, a machine learning algorithm, is able not only to correctly detect the presence of cancer cells on slides taken from biopsies and surgical sections, but it can predict where the tumour lesions are located and even the proportion of regions with different levels of aggressiveness.
Gliomas represent more than a quarter of all CNS tumors. Limited treatment options together with high mortality and disability rates constitute a significant burden on healthcare and the economy, underscoring the need to identify transformative therapeutic targets to combat this malignancy.
Gliomas represent more than a quarter of all CNS tumors. Limited treatment options together with high mortality and disability rates constitute a significant burden on healthcare and the economy, underscoring the need to identify transformative therapeutic targets to combat this malignancy.
Gene therapy—the process of modifying, replacing, or regulating genes to treat disease—has emerged as one of the most transformative innovations in modern medicine. While ex vivo strategies, such as engineered immune or hematopoietic cells, have achieved clinical success, the next frontier lies in in vivo gene therapy—directly delivering therapeutic genetic material into target tissues within the body.