OCC AI

We work with our clients to strengthen their AI solutions and to solve the challenges around security, accessibility, application infrastructure and regulatory compliance.

Artificial Intelligence at OCC

Artificial intelligence and machine learning are fields of significant interest at OCC. We deliver AI solutions to clients through well-designed, robust applications, using the latest technology to help solve a range of different commercial problems. As part of our commitment to be involved at the forefront of emerging research in this area, OCC sponsors the University of Oxford Reuben College Fellowship in Artificial Intelligence and Machine Learning. This enables us to stay up to date with the latest thinking and gives us the opportunity to apply this knowledge to the development of our commercial services and products.

Our Directors with OCCs Reuben College Fellow; David Clifton, .

David Clifton has been appointed as the first Oxford Computer Consultants AI and Machine Learning Fellow. His research is primarily centred on AI in Healthcare, or Clinical AI. The partnership facilitates intellectual exchange by giving us the opportunity to participate in Reuben’s workshops, lectures, and other initiatives.

AI and David Clifton’s research

An introduction to the Oxford Computer Consultants Fellow for AI and Machine Learning – An overview of AI and Professor David Clifton’s research.
 

AI Challenges and Opportunities

Kaz Librowski and David Clifton discuss the importance of large data sets; the challenges ahead for AI and the privacy problems around handling personal data.

AI in health and social care

John Boyle and Professor David Clifton discuss the impact of AI and machine learning in health and social care. They consider the specific challenges around soft outcomes and the use of synthetic data.

Transforming AI into commercial applications

OCC’s John Boyle and Reynold Greenlaw discuss turning AI research into commercial applications. They review how OCC has helped emerging and established companies harness AI techniques.

Some of our work in the application of AI

Our custom software innovation delivery group helps transform new ideas and early-stage software into commercial applications. Its project teams bring together data experts, numerical engineers, UX designers and front-end developers to deliver complex solutions as accessible applications backed by implementation and support professionals.

Parkinson’s app

The Parkinson’s mobile app showing a reaction test.

The Park AI app uses indigenous smartphone sensors to accurately capture exercise performance of Parkinson’s patients. Performance data are sent to the cloud and analysed by AI code. The AI provides performance scorings enabling accurate symptom monitoring which is used by clinicians to help maintain a patient’s quality of life.

Determining the gestational age of the foetus

An allied health professional capturing a sonographic video of a foetus.

Quality videos of foetus ultrasound scans are used by AI to determine the gestational age of the unborn child. A desktop app enables non-expert sonographers in remote locations to capture these videos. After quality checking the videos the desktop software transfers them to the cloud where AI determines the gestational age. This is then used by the on-site healthcare professionals to plan the mother’s maternity care.

Stress echocardiogram analysis

A graphical representation of an echocardiogram. Displayed are two sets of images showing cross sections of the heart wall at rest and under stress. These cross sections are colour coded to show areas of the heart wall where there is concern.

Algorithms trained by AI are used to analyse echocardiograms in much more detail than is possible by a human therefore helping improve the diagnosis of coronary heart disease. This early-stage project helped establish the foundations of and funding for the Ultromics’ software which was subsequently developed and achieved FDA 510(K) certification.

Mining social media data for use by the emergency services

A rescue worker wading through a city street

This European funded research project further developed our data mining tools and techniques to analyse social media data streams. Using machine learning bayesian techniques the AI processed terabytes of real-time data of low-quality text in many languages, separating real incidents from fake news, to provide reliable intelligence on crisis situations.

See more of our AI case studies and AI articles