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Tag: pathologists

Guidelines for Validating Whole Slide Imaging for Diagnostic Purposes

Andrew Evans, speaking at the Digital Pathology & AI Congress USA, described new guidelines he helped to draft for validating whole-slide-imaging for diagnostic purposes. First published in 2013 the guidelines were designed to address the fundamental question, “what needs to be done to validate a whole slide imaging for diagnostic use?”. The review producing the new guidelines was published in May 2021.

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Training AI to predict outcomes for cancer patients

Predicting the outcome of cancer can help the clinical decision-making process related to a patient’s treatment. The potential for Artificial Intelligence (AI) to support this was a key facet of the final keynote speech to the online 7th Digital Pathology and AI Congress: Europe, by Johan Lundin, Research Director at the Institute for Molecular Medicine Finland (FIMM) at the University of Helsinki and Professor of Medical Technology at Karolinska Institutet.

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Digital Pathology: Lagging or Leading?

When compared to traditional methods, digital pathology may seem totally superfluous, or interesting but not all that essential. And since the costs for the elegance of digital pathology solutions are not trivial in comparison to what the peddlers traditionally have offered, it is not surprising to see the adoption curve lagging. Where then is the ‘burning platform’ that can truly drive greater adoption of digital pathology? 

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Unleashing the power of digital pathology and AI for precision medicine

We’re looking back at the highlights from the Digital Pathology and AI meeting in 2018 as we anticipate this year’s Digital Pathology and AI Congress in December. This second post in our mini-series reviews Marylin Bui’s keynote presentation where she explained how the combination of Digital Pathology and Artificial Intelligence (AI) holds huge potential for patient care.

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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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