When it comes to AI in healthcare, data bias can lead to significant, albeit unintentional, consequences. We’ve put together these 6 tips to help you better understand AI bias.
AI bias: 6 tips to better understand your data
Machine Learning
Why AI needs to be explainable: Part two
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.
Why AI needs to be explainable: Part one
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.
A vision for how the NHS could embrace accountable AI lifecycle management
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 SDK
Zegami is a great tool for managing, sharing and getting insights from your data in an intuitive way that can
Automated image and data clustering
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.
Stronger Together interview with Microsoft
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.
Roger Noble and Steve Taylor interviewed by Oxford Investment Consultants
Join OIC Principal and CFO Tania Wilson interview our CEO Roger Noble and CSO Steve Taylor.
Data firm Zegami joins project to find cure for ME
Zegami, the Oxford University data visualisation spin-out, has joined an international team of medical researchers to try and find the
Webinar: Analysing X-Rays For COVID-19 Using AI
13th May, 11:00-11:40 BST Zegami has built a tool that could help speed up diagnosis of COVID-19, using a combination
Using AI to speed up COVID-19 diagnosis
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
Colour By Column in Zegami
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.
