Posted 10th May 2019 by Joshua Sewell
Unsurprisingly, there is a lot of hype surrounding AI. Available deep learning packages make it so easy to create models and so we can expect lots of them to emerge. Anyone able to access sufficiently labelled data can start building models.
Posted 3rd May 2019 by Joshua Sewell
In our lab of Quantitative Imaging and Artificial Intelligence, we’re developing AI applications in a variety of areas, such as radiology and pathology. The goal is to develop applications that meet unmet medical needs, particularly in relation to precision medicine and clinical prediction.
Posted 17th April 2019 by Joshua Sewell
David Snead is Consultant Histopathologist and Clinical lead for Coventry and Warwickshire Pathology Services (CWPS), a network of labs hosted by University Hospitals of Coventry and Warwickshire NHS Trust. As head of the UHCW Digital Pathology Centre of Excellence, he is now heavily involved in the Pathology image data Lake for Analytics, Knowledge, and Education (PathLAKE).
Posted 5th April 2019 by Joshua Sewell
Aiforia Technologies is a medical AI software company seeking to transform clinical pathology and medical research by bringing deep learning AI to assist and augment human experts in medical image analysis.
We had the chance to ask CEO Kaisa Helminen about AI in healthcare and Aiforia’s newest platform.
Posted 22nd March 2019 by Joshua Sewell
As laboratories transform their workflows into the digital environment, a tremendous opportunity presents itself: to transition the field of pathology from a qualitative to quantitative discipline. Quantitation brings measures of accuracy, reproducibility, and statistical stringency that allow computational algorithms (including AI) to perform complex tasks and measure their success. The evolution of Pathology will not be dictated by any single organization but rather will take an entire community of experts.
Posted 15th March 2019 by Joshua Sewell
For over a decade, Memorial Sloan Kettering has implemented digital pathology enterprise system for clinical scanning. Over the years that has evolved significantly. Currently, a lot of our efforts are spent on archive scanning to be available for prospective clinical cases.
Efforts to enable pathologists the ability for primary diagnosis are being explored, and we’re currently validating available systems. There’s always a certain flux in terms of vendor communication and networking, meaning that we’re validating systems for our internal use whether that’s for clinical, education, or research.
Posted 1st March 2019 by Joshua Sewell
This is the second of a two-part blog post. In his first post, Liron wrote on embedding AI in Digital Pathology workflows.
Digital Pathology AI apps are certainly feasible, but exactly when they will be ready for clinical use is less clear.
There are potentially hundreds or thousands of algorithms that will need to be developed. Currently, there are only a handful of algorithms that are approved by regulatory bodies for clinical practice, so we’ve got a long way to go.
Posted 22nd February 2019 by Joshua Sewell
This is an exciting time in pathology: now that digital pathology is mature, we have noticed an uptake in a lot of AI start-up companies. Most of the algorithms have been developed on a research basis or in a test environment, and only recently applied. What follows is a summary of the work we are doing at the University of Pittsburgh Medical Center testing these AI apps, and some of the questions arising therefrom.