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Tag: ai

Controlling the fate of cancer cells with SF3B1 modulators

Andrew Cook has worked in the pharmaceutical industry for over 20 years. Throughout his career, he has endeavoured to do things differently, a little out of the ordinary, leading him to his work at H3 Biomedicine on splice modulators. “Part of the reason it attracted me” he says, “is it’s so different. It’s a natural product-based drug discovery effort, and I had never done that before”.

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Going back to biology and the hype of AI in pharma

With a background in pharmacology, toxicology, molecular cell biology and genetics, Rangaprasad Sarangarajan joined BERG Health in 2010 as head of R&D.

In the initial years of the company, he aided in the conception, design, and implementation of the paradigm of looking at human biology, use of technology to generate molecular signatures, and the use of artificial intelligence-based analytics for understanding the complexity of biology, identifying of targets, and developing of drugs, and its utility in clinical development.

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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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