Abstract
A high-throughput artificial intelligence-powered image-based phenotyping platform, iBiopsy® (Median Technologies, Valbonne, France), which aims to improve precision medicine, is discussed in the presented review. The article introduces novel concepts, including high-throughput, fully automated imaging biomarker extraction; unsupervised predictive learning; large-scale content- based image-based similarity search; the use of large-scale clinical data registries; and cloud-based big data analytics to the problems of disease subtyping and treatment planning. Unlike electronic health record-based approaches, which lack the detailed radiological, pathological, genomic, and molecular data necessary for accurate prediction, iBiopsy generates unique signatures as fingerprints of disease and tumour subtypes from target images. These signatures are then merged with any additional omics data and matched against a large-scale reference registry of deeply phenotyped patients. Initial applications targeted include hepatocellular carcinoma and other chronic liver diseases, such as nonalcoholic steatohepatitis. This new disruptive technology is expected to lead to the identification of appropriate therapies targeting specific molecular pathways involved in the detected phenotypes to bring personalised treatment to patients, taking into account individual biological variability, which is the principal aim of precision medicine.
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