AI Assurance
Our Values: AI that is validated for people, by people, will be trusted and therefore have an affect more quickly.
As long as people want to use AI systems, the health sector will need to keep assuring them. Our work in the area has involved close collaborations with:
As long as people want to use AI systems, the health sector will need to keep assuring them. Our work in the area has involved close collaborations with:
This work has involved focusing resources in support of AI developments and has capitalised on our unique position:
- Technical experience of developing AI and ML that is scalable across multiple user cases, geographies and cultures
- Experience of research, design and development of systems for care environments
- Managing technical and commercial due dilligence in the private and public sector
- Leading the development and use of standards to validate AI in healthcare
- AI product development within the NHS that delivers a tenfold return on investment
It was this latter experience that led to the development of PRIDAR assessments. These assessments enable boards and management teams to understand the risks associated with compliance with law and good practice, and enable teams to prioritise the funding, management, launch and monitoring of AI in a care environment.
We also enable customers to embody AI in the customs and practice of healthcare organisations, and have been instrumental in the movement of power away from IT providers, into the hands of clinicians and users. This approach has subsequently been described in the 2022+ AI Framework for change strategy.
We make it very clear that unlike historical software and systems, AI and Ml based care systems can be adapted more quickly to user needs. We lead the way in terms of interoperability by the publishing of model labels (see Motives and Models), and promoting the active use of Model Feedback so that AI systems can continually be assured as safe and fit for purpose.
- Technical experience of developing AI and ML that is scalable across multiple user cases, geographies and cultures
- Experience of research, design and development of systems for care environments
- Managing technical and commercial due dilligence in the private and public sector
- Leading the development and use of standards to validate AI in healthcare
- AI product development within the NHS that delivers a tenfold return on investment
It was this latter experience that led to the development of PRIDAR assessments. These assessments enable boards and management teams to understand the risks associated with compliance with law and good practice, and enable teams to prioritise the funding, management, launch and monitoring of AI in a care environment.
We also enable customers to embody AI in the customs and practice of healthcare organisations, and have been instrumental in the movement of power away from IT providers, into the hands of clinicians and users. This approach has subsequently been described in the 2022+ AI Framework for change strategy.
We make it very clear that unlike historical software and systems, AI and Ml based care systems can be adapted more quickly to user needs. We lead the way in terms of interoperability by the publishing of model labels (see Motives and Models), and promoting the active use of Model Feedback so that AI systems can continually be assured as safe and fit for purpose.
