1.5 Terminology

An overview of the terminology used in Chapter 1.

This report uses the terms ‘Artificial Intelligence (AI)’ and ‘AI technologies’ to describe the use of digital technologies to create systems capable of performing tasks commonly thought to require intelligence. These can include algorithms using statistical techniques that find patterns in large amounts of data, or to perform repetitive cognitive tasks with data without the need for constant human oversight.

This definition of AI is intentionally broad and could encompass algorithms not commonly considered as AI. While parts of this report refer only to more complex machine-learning algorithms, many of the factors that influence confidence described in the report could apply to any data-driven technology or algorithm used in healthcare or clinical practice.

AI technologies have the potential to support existing clinical capabilities in diagnosis and screening, drug discovery, digital epidemiology, and personalised medicine,¹ as well as optimising organisatioal resources, system efficiencies and clinical workflows.  

'Clinicians', as referred to in this report, include healthcare workers making a patient-specific decision that affects patient care, and may include Nurses, Paramedics, Allied Health Professionals, Doctors, and other specialist healthcare staff groups.

'Industry innovators' refer to private sector developers and providers of AI technologies.

References

¹ Joshi I, Morley J. Artificial Intelligence: How to get it right. Putting policy into practice for safe data-driven innovation in health and care. 2019:1-55. https://www.nhsx.nhs.uk/ai-lab/explore-all-resources/understand-ai/artificial-intelligence-how-get-it-right.