Treatment decisions in oncology are commonly informed by the visual assessment of immunohistochemistry biomarkers by pathologists. However, pathology services face mounting pressure as diagnostic demand increases and workforce decreases.
Digital pathology and artificial intelligence have the potential to streamline the diagnostic workflow thereby improving pathologists’ workload, accelerating turn-around-times and facilitating access to testing. Here, we will present use cases of how digital pathology and artificial intelligence are being applied within AstraZeneca to support efforts in precision medicine.
Is a Senior Scientist at AstraZeneca evaluating applications of digital pathology and AI to support companion diagnostics development and clinical decision making. He has a PhD in Computer Science from University Pierre and Marie Curie and a Doctorate in Pharmacy from University Paris Sud.