AI in health and care

What do we mean by AI in health and care?

AI describes a set of advanced technologies that enable machines to carry out highly complex tasks effectively – tasks that would require intelligence if a person were to perform them.

There is no single, universally agreed definition of AI, nor indeed of ‘intelligence’. Broadly speaking, intelligence can be defined as ‘problem-solving’, and ‘an intelligent system’ as one which takes the best possible action in a given situation.

The ‘A’ of AI generally refers to one of the following:

Artificial (Intelligence)

This makes it possible for ‘machines’ to learn from new experiences, adjust outputs and perform human-like tasks. It can be thought of as the simulation of human intelligence and could include voice and visual recognition systems.

Augmented (Intelligence)

These are outputs that complement human intelligence, emphasising AI’s supplementary role. Examples include tools that support radiologists in reviewing large numbers of scans, or that support financial advisors to better understand clients’ current and potential future financial needs.

Ambient (Intelligence)

The application of several technologies (including Artificial or Augmented Intelligence, but also sensor networks, user interfaces, home automation systems, etc) to create proactive ‘smart’ environments.

AI is generally classified into the following types:

Narrow AI

This typically focuses on a narrow task, or works within a narrow set of parameters such as reading radiology scans, or optimising hospital workflows.

Strong or general AI

This is a hypothetical concept which can refer to an AI that can learn to perform several different types of task, or to a sentient machine with consciousness and mind.

Thanks to advances in AI and Big Data research, narrow AI technologies have the potential for wide application in health and social care, bringing benefits to individuals, families, communities, and society as a whole. While early examples from our survey illustrate that much of this work is at an early stage, current technologies support a more general shift away from reactive care models to models that are more personalised and proactive.

But this is not without its challenges in health and social care and more widely – ensuring these technologies are fit for purpose, ensuring outputs are transparent and explainable, and ensuring people are trained in the use of these new technologies.