Requirement for Academic Collaboration on Artificial Intelligence

To develop risk-based approaches to the classification of technologies that incorporate artificial intelligence for use in health care settings.

Academic Collaboration on Artificial Intelligence

Contract summary

Industry

Research and development services and related consultancy services – 73000000
Miscellaneous health services – 85140000

Location of contract

United Kingdom

Value of contract

£100,000

Procurement reference

NICE AIAC

Published date

5 May 2021

Closing date

4 June 2021

Closing time

4pm

Contract start date

5 July 2021

Contract end date

30 June 2022

Contract type

Service contract

Procedure type

Open (non-OJEU)

Contract is suitable for SMEs?

Yes

Contract is suitable for VCSEs?

Description

Overview Description for an Academic Collaboration on Artificial Intelligence
NICE is looking for an academic partner (or joint bid, or consortium) to work on the delivery of an NHSx AI Lab commissioned project to develop NICE’s Evidence Standards Framework for Digital Health Technologies to fully incorporate data driven technologies that incorporate artificial intelligence, including those that use adaptive algorithms. The Framework will describe an approach to the classification of AI technologies that triages technologies for health technology evaluation and will describe the levels and types of clinical evidence that should be available, or developed, for these technologies to demonstrate their clinical and economic value in the UK health and care system. This includes evidence of clinical effectiveness relevant to the intended use(s) of the technology.

The standards framework will be used to support innovators and technology developers, including commercial organisations, of appropriate evidence-generation plans; research funders and investors who are considering funding the development of data driven technologies as well as evaluators and commissioners.

To support this work NICE is looking for an academic partner with
experience of developing:

• risk-based approaches to the classification of technologies that incorporate artificial intelligence for use in health care settings
• methods for the evaluation of data driven technologies (including those that incorporate artificial intelligence) in terms of their clinical effectiveness and value

Full details in the attached documents