How Can we Develop Collaborative Standards for AI in Digital Pathology?
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.
Developing a pathology LIS for the digital age
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.
What does the future hold for AI in Digital Pathology?
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.
Utilising Machine Learning Models to transform Plant Genomics
Posted 25th February 2019 by Joshua Sewell
A lot of machine learning is used in technology such as Google Assistant, Amazon’s Alexa or Apple’s Siri, or to get rid of spam in your email inbox. Deep learning and rapid development of this technology enables us to solve image classification problems – e.g. “does this picture contain a dog or a cat?”. Also, artificial intelligence is set to soon replace many human jobs – in the darkest views, it might even pose an existential threat to the human race.
What repercussions do these developments have for genome-related research and in particular plant genomics?
How do we embed AI into Digital Pathology workflows?
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.
AI in digital pathology; increasing workflow efficiency and patient safety
Posted 11th February 2019 by Jane Williams
In the fifth of this six-part series, the experts considered the potential cross-border solutions and challenges for Digital Pathology, as well as the evidence for improved efficiency. In this final installment, they discuss the impact of AI on industry and medical practice.
If you weren’t able to make the panel discussion, you can watch the recording here.
Digital Revolution: Bring Your Cleverness and Wisdom
Posted 1st February 2019 by Joshua Sewell
We are lucky to be living in the middle of the digital revolution. Our access to information, products, and ability to change platforms and paradigms has never been better. Professionals shaping the digital revolution in pharmaceuticals and health care moved the leading edge further forward at the 2nd Global Pharma R&D Informatics and AI Congress.
Lab Data Automation, Optimizing R&D Workflows and Lead Discovery from Biologically Complex Bioassays
Posted 23rd January 2019 by Jane Williams
With an increasing amount of data being generated today, pharma and biotech companies need to develop new technologies and strategies in order to structure, analyse and integrate this big data. If you weren’t able to attend our Global Pharma R&D Informatics & AI Congress, we have made these slides from Ralph Haffner, Dana Caulder and Pierre Farmer available.