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Tag: pathologists

Transforming medical image analysis with deep learning AI

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.

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How Can we Develop Collaborative Standards for AI in Digital Pathology?

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.

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Developing a pathology LIS for the digital age

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.

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What does the future hold for AI in Digital Pathology?

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.

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How do we embed AI into Digital Pathology workflows?

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.

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AI in digital pathology; increasing workflow efficiency and patient safety

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.

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The benefits and challenges of offsite working in Digital Pathology

In the fourth of this six-part series, the experts discussed the biggest drivers in the transition to going 100% digital. Here, the panel contemplated the potential cross-border solutions and challenges to the field, as well as the evidence for improved efficiency in digital pathology.

If you weren’t able to make the panel discussion, you can watch the recording here.

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The biggest drivers for 100 percent adoption of digital pathology

In the third of this six-part series, the experts looked at issues surrounding the transition to digital pathology. Here, the panel considered the perceived hurdles involved in making this transition, and the possible benefits of digital pathology to patient safety.

If you weren’t able to make the panel discussion, you can watch the recording here.

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