NHS AI (artificial intelligence)

by | Nov 8, 2019 | Artificial Intelligence (AI), Blog, News

Artificial intelligence (AI) in the NHS an investment for the future

Our dermatology AI AUTODERM® was featured as one of the case example in the NHSX report “Artificial Intelligence: How to get it right. Putting policy into practice for safe data-driven innovation in health and care” (page 46). The report is intended to provide a cohesive overview of the current state of play of data-driven technologies within the health and care system. The objective is to make it clear where in the system AI technologies can be utilised and the policy work that is, and will need to be done, to ensure this utilisation is done in a safe, effective and ethically acceptable manner in a data driven ecosystem across healthcare.

In August NHS announced a £250 million investment in Artificial Intelligence (AI) applications for health and care through the creation of the NHS AI Lab.  This report highlights why there is a need for this investment and how over time this investment will enable health and care providers to benefit from the very best data-driven technology and help achieve goals for technology use in the NHS and in the care system.

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About NHSx

NHSx is responsible for delivering the Health Secretary’s Tech Vision, building on the NHS Long Term Plan by focusing on five missions:

  • Reducing the burden on clinicians and staff, so they can focus on patients
  • Giving people the tools to access information and services directly
  • Ensuring clinical information can be safely accessed, wherever it is needed
  • Improving patient safety across the NHS
  • Improving NHS productivity with digital technology

 

NHSx Report Summary

Artificial Intelligence (AI) has the potential to make a significant difference to health and care. A broad range of techniques can be used to create Artificially Intelligent Systems (AIS) to carry out or augment health and care tasks that have until now been completed by humans, or have not been possible previously; these techniques include inductive logic programming, robotic process automation, natural language processing, computer vision, neural networks and distributed artificial intelligence. These technologies present significant opportunities for keeping people healthy, improving care, saving lives and saving money for the pilot digital technologies. It could help personalised NHS screening and treatments for cancer, eye disease and a range of other conditions, for example.

Furthermore, it’s not just patients who can benefit. AI can also support clinicians, enabling them to make the best use of their expertise, informing their decisions and saving them time. This report gives a considered and cohesive overview of the current state of play of data-driven technologies within the health and care system, covering everything from the local research environment to international frameworks in development. Informed by research conducted by NHSX and other partners over the past year, it outlines where in the system AI technologies can be utilised and the policy work that is, and will need to be done, to ensure this utilisation is done in a safe, effective and ethically acceptable manner.

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Chapters 1 and 2 set the scene

They provide an overview of what AI is (and importantly is not), why we believe it is important, and a detailed look at what is currently being developed by the AI ecosystem by evaluating the results of a horizon scanning exercise and our second ‘State of the Nation’ survey. This analysis reveals that diagnosis and screening are the most common uses of AI, with 132 different AI products identified being designed for diagnosis or screening purposes covering 70 different conditions.

 

Chapter 3 is an in-depth look at the Governance of AI

Building on the Code of Conduct for data-driven technologies, it explores the development of a novel governance framework that emphasises both the softer ethical considerations of the “should vs should not” in the development of AI solutions as well as the more legislative regulations of “could vs could not”. In particular it covers key areas such as the explainability of an algorithm, the evidence generation for efficacy of fixed algorithms, the importance of patient safety and what to consider in commercial strategies.

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Chapter 4 is all about the data that fuels AI

When engaging with innovators, regulators, commissioners and citizens on AI the one topic that is guaranteed to come up is Information Governance (IG). Protecting patient data is of the utmost importance, which is why IG is crucial, but it should not be seen as a blocker to the use of data for purposes that can deliver genuine benefits to patients, clinicians and the system. This Chapter highlights how we are working collaboratively with key partners across the system (e.g. the Accelerated Access Collaborative, the Office of Life Sciences, Health Data Research UK, Genomics England, Academic Health Science Network) to clarify the rules of IG and streamline access to data for good through specific programmes such as the Digital Innovation Hubs.

 

Chapter 5 covers adoption and spread

Considering the sometimes negative impact the complexity of the NHS as a sociotechnical system has on the spread of important innovation, it covers the actions being taken to encourage adoption. However, given the challenges involved in the practical implementation of AI we do not want to encourage adoption for the sake of adoption, so it also covers ‘what good looks like’ and how we can monitor the impact of the introduction of AI over time so that good stays good further downstream.

 

Chapter 6 comes back to the people of the NHS

Building on the work of Health Education England and the Topol Review, it highlights the challenges faced by the workforce in the development, deployment and use of AI and what needs to be done in order to ensure they have the skills that they need to feel confident in using AI in clinical practice safely and effectively. Crucially it highlights how again we cannot do this alone and must work closely with national centres of data science training such as the Alan Turing Institute.

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Chapter 7 goes international

Health data is not only generated in England and the AI technologies that are trained and tested on
it are not developed only in England. Instead the AI ecosystem is truly international and there is, therefore, a need for international collaboration and agreement of standards, frameworks and guidance. For this reason, this chapter highlights the ongoing work of the Global Digital Health Partnership, the World Health Organisation and the EQUATOR network in developing these with us as a key partner.

 

Chapter 8 concludes with the NHS AI Lab

It brings together all the information included in the previous chapters to highlight why we know that the Lab is needed and why we think it will be crucial in helping us achieve our aims of:

  • Promoting the UK as the best place in the world to invest in healthtech.
  • Providing evidence of what good practice looks like to industry and commissioners.
  • Reassuring the public, patients and clinicians that data-driven technology is safe, effective and protects privacy.
  • Allowing the government to work with suppliers to guide the development of new technology so products are suitable for the health and care system in the future.
  • Building capability within the system with In-house expertise to prototype and develop ideas.
  • Making sure the NHS gets a fair deal from the commercialisation of its data resources and expertise.

 

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About the report

Although this report has named editors, it results from the collective effort of a great number of individuals who kindly gave up their time to contribute their thoughts, ideas and research. A full list of acknowledgements is provided at the end of the report. There are, however, several key organisations, and individuals who provided input without which this report would not have been possible. With this in mind, we would like to thank: Tina Woods, Collider Health Melissa Ream, Marie-Anne Demestihas and Sile Hertz, AHSN Network Anna Steere, NHSX Dr. Sam Roberts, Accelerated Access Collaborative

 

Sources:

Artificial Intelligence: How to get it right. Putting policy into practice for safe data-driven innovation in health and care https://www.nhsx.nhs.uk/assets/NHSX_AI_report.pdf 

Health Secretary announces £250 million investment in artificial intelligence. A new National Artificial Intelligence Lab will use the power of artificial intelligence (AI) to improve the health and lives of patients. https://www.gov.uk/government/news/health-secretary-announces-250-million-investment-in-artificial-intelligence

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