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.
& improve efficiencies and human health outcomes.
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
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
Radiology & Imaging
information in real-time for
our algos to analyse
- 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 RGU to deliver an automated tool to diagnose fractures quickly and efficiently.
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.
WHO IS JIVA?
Our team with Big Ideas
Dr Chetan Kaher
Business & Growth
Chetan has a doctorate in dentistry and a BSc in Immunology & Oncology with publications in developing anti-cancer proteins. He is currently on the NHS Clinical Entrepreneurship Scheme, to implement Jiva.ai into healthcare systems.
Dr Manish Patel
Manish has a doctorate in mathematical modelling with an emphasis on dealing with large, complex datasets. He is the technical architect of Jiva.ai, a new machine learning algorithm that will form the basis of a new breed of AIs.
CIO & Operations
Sarah is an experienced management and banking professional with a background in law. As well as being a successful entrepreneur, Sarah joins the team taking control of project management, operations and all things legal & data security.
Dr Andrew J. Thompson
Andy heads funding strategy and is a professional grant writer. He has extensive experience developing business, R&D and fundraising strategy, and preparing and implementing winning business cases.