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.

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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.

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Car hire accelerates with one in five planning to rent

More than a third renting in the 12 months will be first timers Zegami joint venture with Ebbon Group pioneers

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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.

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Zegami SDK

Zegami is a great tool for managing, sharing and getting insights from your data in an intuitive way that can

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Feature Preview: Multiple Image Sources

We’re currently working on a new capability for Zegami which adds a new dimension to image analysis, and we’re really excited to be able

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Image only collections

We’ve added the ability to create Zegami collections with only images. It’s never been easier to create a Zegami collection and should take only minutes to start gaining insights into a set of image files.

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.

Case Studies Hero

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

Data Visualisation to Protect Great White Sharks

SEA TECHNOLOGY MAGAZINE Great white sharks enjoy what can only be described as a complex relationship with humankind. On one