AI in healthcare: Is the NHS embracing the potential?
The complexity and rise of data in healthcare means that artificial intelligence (AI) will increasingly be applied within the field and has vast potential.
AI-driven tools can analyse large amounts of information, detecting patterns, and predicting outcomes. These tools have the potential to change and improve the way that healthcare is provided to people by improving quality and efficiency.
The NHS AI Lab was set up to make the most of the potential of AI technologies, and despite COVID-19, the past year has brought new opportunities and challenges for the use of AI in health and care. A survey was conducted, 2020-21: A year in the life of the NHS AI Lab, to explore the development and use of AI.
The survey was open to all and received over 368 responses in 2021, 197 of whom were developers of AI for health and care.
3 Key Findings
1. Diagnostics is the most popular area for AI in health and care
The deployment of AI-driven technologies is not yet evenly spread across the different areas of health and social care. The figure shows the responses from survey participants who were asked about the area of focus of their technology, they were allowed to select more than one area of focus. The survey clearly shows that most of the early adopters of AI technologies are in diagnostics. Similarly to 2019, the 2021 survey indicates the use of AI is dominant in the following 4 areas:
- Diagnostics - 57%
- Remote monitoring - 34%
- Triage - 32%
- Population health - 25%
2. AI products have some way to go before large scale use
Although the levels of readiness for the deployment of AI technologies in health and care have significantly increased from 2019 to 2021, it is still in the early stages. Around half of AI developers surveyed in the UK believe their product will be ready for deployment at scale in one year; up 24 percentage points from the last survey.
- Ready in 1 year: 54% developers
- Ready in 3 years: 79% developers
- Ready in 5 years: 87% developers
3. The COVID-19 pandemic has influenced progress
The pandemic has had a mixed effect on the development and adoption of AI technologies into health and care settings.
A third of AI developers in the survey indicated a negative impact, giving examples of problems with re-deployment of clinical staff, reduced data collection, and lack of engagement for non-COVID-19 activity. However, a similar number indicated positive impacts where healthcare pressures had resulted in a rapid uptake of AI tools and increased acceptability for digital technologies being used to deliver care.
What’s next for the NHS AI Lab?
The UK has the second-highest number of AI-driven healthcare technologies in development globally after the US, according to NIHR Innovation Observatory’s Mapping the Global Activity of AI Health Technologies, 2021. The aim of the NHS AI Lab is to continue creating an environment that enables both developers and adopters of AI technologies to thrive, and to bring the benefits of AI quickly and safely to the people who need it most.
Here is a selection of things NHS AI Lab is doing in 2021:
- Continuing to support the pandemic response through increasing use of the National COVID-19 Chest Imaging Database
- Work with a steady supplier of early stage AI technologies to get them tested and trialled within one to two years
- Educated and share knowledge with others who are developing and using AI in health and care so that progress is faster, quialty is imporved and costs reduced
- Help the most promising technologies get approved, commissioned and into widespread use in the NHS
The advantages and disadvantages of AI in cloud computing
Cloud computing offers businesses more flexibility, agility, and cost savings by hosting data and applications in the cloud. AI capabilities are now combining with cloud computing and helping companies manage their data, look for patterns and insights in information, deliver customer experiences, and optimise workflows.
We take a look at some of the benefits and drawbacks of AI in cloud computing.
The benefits of AI in cloud computing
A major advantage of cloud computing is that it eliminates costs related to on-site data centers, such as hardware and maintenance. Those upfront costs can be restrictive with AI projects, but with cloud enterprises you can access these tools for a monthly fee, making research and development related costs more manageable. AI tools can also gain insights from the data and analyse it without human intervention, reducing staff costs.
AI is able to identify patterns and trends in large data sets. Using historical data, AI compares it to the most recent data, which provides IT teams with well-informed, data-backed intelligence. AI tools can also perform data analysis fast so enterprises can rapidly and efficiently address customer queries and issues. The observations and valuable advice gained from AI capabilities result in quicker and more accurate results.
Improved data management
AI enables extensive data management, and cloud computing maximises information security, making it possible to deal with massive amounts of data in a programmed manner to analyse them properly, allowing the business to leverage information that has been “mined” and filtered to meet each need. AI can also be used to transfer data between on-premises and cloud environments.
Businesses use AI-driven cloud computing to be more efficient and insight-driven. AI can automate repetitive tasks to boost productivity, and also perform data analysis without any human intervention. IT teams can also use AI to manage and monitor core workflows. IT teams can focus more on strategic operations while AI performs the mundane tasks.
With businesses deploying more applications in the cloud, security is crucial in order to keep data safe. IT teams can use different AI-powered network security tools which can track network traffic, they can flag issues, such as finding an anomaly.
The drawbacks of AI in cloud computing
Enterprises need to create privacy policies and secure all data when using AI in cloud computing. AI applications require a large amount of data, which can include consumer and vendor information. While some data can be anonymous and can't be tied to personally identifiable information, knowing who the data belongs to makes it more valuable. When sensitive information is used, data protection and compliance is a major concern.
IT teams use the internet to send raw data to the cloud service and recover processed data. Poor internet access can hinder the advantages of cloud-based machine learning algorithms, as cloud-based machine learning systems need consistent internet connectivity.
While processing data in the cloud is quicker than conventional computing, there is a time lag between transmitting data to the cloud and receiving responses. This is a significant issue when using machine learning algorithms for cloud servers, where prediction speed is one of the primary concerns.