Evolution of AI and Digitization in Precision Pathology and Image Analysis ,

Discover a groundbreaking webinar delving into the advancements in AI and digitization in precision pathology and image analysis applications in the pharmaceutical, chemical and biotech industries. Although the pioneers started more than 20 years ago, digital and quantitative precision pathology is now coming to age and is especially maturing in diagnostic human pathology, including artificial intelligence (AI)-assisted diagnosis. In development in the pharma sector — and less so in early research, the field was lagging behind, due to a combination of factors, one of them being the very rigorous regulatory framework in place for safety studies and the other being the high throughput with usually low input data of highly demanding large study format.

However, in recent years, the COVID-19 pandemic has elicited big strides in the progress of executing safety, biodistribution and pharmacology studies digitally locally or remotely, and the sector is evolving with unprecedented speed. In parallel, computer capabilities and AI sectors have steeply expanded to reach practical empowerment.

This webinar will review the various milestones in AI and digitization in precision pathology in the industry and show the audience the rapid adoption in the contract research organization arena and where the challenges currently are. It will help newcomers as well as experts better understand where the chemistry, pharma and biotech industries are and want to go.

The featured speakers will review where and in what sector of experimental/investigative and toxicologic pathology digitization equals and often surpasses current analogical glass — and microscope-based pathology, but also where some gaps remain. They will touch base on the future of diagnosing common lesions in toxicology studies, use AI to direct the pathologist to potential differences of treated animals versus controls and use lesion quantification to reproducibly score tissue lesions and morphological changes. They will also review current image management systems and present a few of the many available AI for image analysis (IA) systems, with a few pertinent examples in the pharma/biotech R&D arena.