ABOUT US
A model integration-first AI platform,
that will enable automation, recognize hidden patterns and capture key insights.
Jiva.ai empowers organizations with the tools and computation capacity to create Machine Learning models so that they can make the most out of their data.
Predict
Solve
& improve efficiencies and human health outcomes.
Enhance
FOCUS
Machine Learning
Healthcare systems must operate under the strain of ever-increasing patient demand whilst maintaining an acceptable level of care.
AI and machine learning can help reduce that burden by saving both time and money in diagnosis, automation and proactive prediction.
Jiva.ai can provide quantifiable value to the healthcare sector
Smart diagnosis
Proactive detection
Live Monitoring
Custom solutions
Let’s talk. Jiva.ai is extremely versatile and can be shaped to specific requirements
We currently work with the following core healthcare and life sciences segments
SOLUTION
Jiva.ai

information in real-time for
our algos to analyse
anti-patterns
- USE CASE 1
- USE CASE 2
- USE CASE 3
Prostate cancer is set to become the most common cancer in men with approximately 50,000 new cases every year. Subjectivity in diagnosis is a known issue with sensitivity recorded as low as 57%. A high time and economic cost of post-biopsy complications means that radiologists are under increasing pressure to improve efficiency.
Jiva.ai is set to become the first AI-based solution trained on 1000s of MRI scans from a variety of sources. The trained kernel is due to go to clinical trial in 2020.
Jiva has teamed up with Manchester University to create early diagnostics for liver disease, fibrosis and HCC. The data spans from patient demographics to pathology and imaging.
Doctors in A&E are overworked, tired and pressured – they need every bit of help they can get. Moreover junior staff can have an error rate of up to 30% in identifying fractures from scans. Jiva.ai has teamed up with Robert Gordon University to deliver an automated tool to diagnose fractures quickly and efficiently.
TECHNOLOGY
Under the Hood
For example, you may want to integrate kernels (predictors) between the following markers:
(a) Genetic markers for diabetes
(b) Genetic markers for heart disease
(c) Socio-demographic factors
age, income, house prices, environmental factors, etc
These data verticals can be learned separately and integrated later. This allows us to have an AI algorithm more suitable for real world problems that change and grow. The idea is to improve your machine learning capability over time.