Zegami has created a reliable, insightful AI-enabled platform for the analysis of scientific and medical imaging which places us at the forefront of this technology. The Zegami platform uses AI to facilitate the analysis, evaluation and curation of large data sets, with far-reaching implications for the scientific and medical communities. Zegami’s image visualisation platform allows you to easily find patterns, outliers and trends in large data sets, including images and visual data. Zegami helps to uncover bias and misclassifications in machine learning models to better understand how they work.

Curating data is a complicated and time-consuming process, particularly when working with large datasets containing unstructured data. AI and machine learning are key to the advancement of medical and scientific research and practise: when applied to the analysis of large and complex data sets, for example databases of medical images, machine learning can accurately identify patterns, trends and health outcomes which would otherwise be beyond the scope of human observation.

Zegami’s AI-enabled image analysis platform enables scientists and clinicians to extract meaningful insights from large datasets, based on patterns and outliers in the data. With Zegami, you can rapidly visualise related images and see precisely which ones an AI model is focusing on.

Our software offers an interactive solution to machine-learning: working with teams to create machine-learning models which help identify and understand potential problems or biases in training data, so that they can be eliminated to avoid misclassification, reduce error and improve outcomes.

Zegami provides scientists and clinicians with the accurate data visualisation tools needed to understand AI software and use it to its best advantage. Our platform demystifies machine learning, making way for constant improvement by consistently validating and testing new algorithms so that AI can be safely and reliably adopted in healthcare settings.

• Reducing bias by avoiding overfitting across demographics or data sources
• Identifying outliers and removing them from training data
• Accurately benchmarking key data to minimise false positives and negatives

• Demystifying data and machine learning algorithms for regulatory purposes
• Improving the effectiveness and accuracy of existing models so you can make reliable predictions

Developing highly accurate, robust and unbiased AI is essential for future development in the worlds of science, business and medicine. Zegami’s leading-edge imaging software makes it possible to extract meaningful insights from big data and use those insights to inform and improve solutions. Featuring embedded intelligence, Zegami enables users to visually identify data quality issues with ease, and ensure that machine learning models are free from error and bias.

Zegami’s AI-enabled image analysis provides insights which:

• Are clinically safe and validated in both clinical and research settings
• Use machine learning to constantly update and improve upon training models
• Manage AI bias, performance and surveillance over time
• Maintain quality assurance through professional monitoring and validation
• Satisfy regulatory compliance and certification requirements

Thanks to its leading-edge technology Zegami has numerous applications in the worlds of scientific research, engineering, healthcare and commercial data integration. Our current partnerships include:

University of Adelaide plant accelerator project
Zegami solves food supply issues in large automated greenhouses in Europe, Australia and the US, enabling researchers to view thousands of images simultaneously and identify patterns and anomalies in physiological and chemical traits.

Data scientists training machine learning models
Cancer Research UK is currently using Zegami’s imaging platform to develop a real-time cancer detection system which identifies cancerous lesions whilst a patient is being examined.

Data integration across silos
Commercial partners including Siemens and Honeywell have used the Zegami platform to extract meaningful insights from disparate data sources, cleaning data and simplifying the data management process to make it more efficient.