Explainable AI (XAI), is essential in allowing us to understand, communicate and adapt how machine learning models reach their decisions. Part two of our blog explores some of the benefits of XAI and how it can be used by, and benefit, healthcare professionals.
In a world that relies heavily on information technology, the importance of AI that we can understand, interpret and trust is becoming a necessity. In the first of our two-part blog series, we explore the evolution of AI and the increasing need for explainable AI.
One of the most crucial developments in AI – and one which has enjoyed exponential growth recently – is its application for the advancement of medical research and predictive analytics. When applied to the analysis of large and complex data sets – for example databases comprising hundreds of thousands of medical images from multiple sources and locations – AI is a very useful assistive tool, supporting with triage and diagnoses that would otherwise require considerable time and expertise from highly experienced clinicians.
Zegami is a great tool for managing, sharing and getting insights from your data in an intuitive way that can
We are excited to introduce our new feature – Automated image and data clustering! Besides offering a fascinating representation of the data, it provides some immediate practical benefits such as efficient labelling and rapid outlier detection.
In this interview with David Smith, Developer Advocate at Microsoft, our CEO Roger Noble explains how Zegami collaborated with the University of Oxford to analyse a research database of lung X-rays, and develop a machine learning model to identify cases of COVID-19.
Join OIC Principal and CFO Tania Wilson interview our CEO Roger Noble and CSO Steve Taylor.
Zegami, the Oxford University data visualisation spin-out, has joined an international team of medical researchers to try and find the
13th May, 11:00-11:40 BST Zegami has built a tool that could help speed up diagnosis of COVID-19, using a combination
Zegami has developed a new machine learning model using x-rays of Covid-19 infected lungs, artificial intelligence techniques and data visualisation tools that could help medical professionals identify corona virus cases more effectively
Our new Colour by Column feature is a powerful new dimension to visual data exploration in Zegami, enabling richer views, which reveal more of the underlying patterns in your data.
Zegami has been collaborating with MRC Weatherall Institute for Molecular Medicines to help clean its data and assist with the training of its machine learning models, specifically around developing a better understanding of which proteins in genes bind, and where they do this in the genome.
