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Tag: ai

What Digital Pathology can learn from Radiology

Radiology is ahead of the curve because they’ve had CAD (Computer Aided Detection) for about 20 years. Radiology as a field has therefore had experience of introducing and integrating AI algorithms.

In my previous post, I talked about high-level cross imaging modalities. Here, I will discuss three challenges specific to pathology. I also work with Radiology imaging, and I think that comparisons between the two can help see how pathology might develop in the future.

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Four challenges in developing AI algorithms for medical imaging

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.

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The promise of AI in medical imaging: improving medical practice

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.

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PathLAKE: Benefitting Pathologists with exemplary projects

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).

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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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