Is AI in the health and care industry hype or reality?
With global spending on AI in healthcare expected to reach $6.6 billion by 2021, expanding to $150 billion by 2026 there is little doubt that AI is here to stay.
The NHS alone has announced its intention to spend £250 million on AI to support a broad range of clinical decisions. These examples include how to speed up test results for cancer screening, brain scans and heart monitoring as well as other predictive models to drive efficiency in the system such as forecasting bed availability or identifying those patients that could be treated in community settings.
As we look to optimise resources across the UK health and care system and deliver best care outcomes, how can we further advance the application of AI to enable the emulation of human tasks through learning and automation?
The pandemic has been an opportunity to accelerate the application of AI. Whereas previously AI was viewed more as an advisory tool, the types of decisions required to manage COVID-19 have benefited from the predictive qualities of AI. Rather than advising, AI has been informing key decisions including those around national lockdowns, as well as serving as a backbone to Track & Trace systems. How will AI remain at the heart of our managerial decision-making for the future?
In clinical settings there also remains a lot of opportunity to be uncovered. The application of an AI-driven computer vision model to more accurately diagnose wet or dry AMD in the eye in itself is ground-breaking – but as a result of further experimentation, there is potential for AI to do much more. A new discovery has uncovered how AI can recognise patterns within the pupil to accurately identify the gender of the patient. This level of assessment and identification goes far beyond the capabilities of a clinician. If AI can do this, what else can it do? How can we further our understanding of patients through AI diagnosis?
What next for AI?
As the pandemic has shown us, the application of AI and analytics for forecasting is certain to accelerate our thinking about how to optimise this as a tool in other scenarios. But whilst there is much progress being made – and much still to do – AI is not a foolproof solution.
Due to the data inputs and assumptions made when developing the machine learning models, there is an inevitable bias in AI models. The role of clinical experts therefore remains as critical as ever. Their critique of the output of AI models, applying their experience and expertise is essential to decision-making. The future will be one where the partnership between AI and clinical expertise will shape better decision-making and better results for patients.
There are other limitations to the application of AI across health and care settings in respect to data privacy as well as data quality, as well as the continuing debate around ethics and use of AI. As we balance the opportunities with the challenges, we’ll need to shape an AI strategy that is fit for purpose.
To find out more about how AI has revolutionised the health and social care industry, view this SAS webinar and learn about:
- The ways AI has already revolutionised the health and social care industry
- How AI is at the forefront of tackling COVID-19
- How AI is helping to save countless lives
- What lies in the future for health and social care using AI
‘How AI has revolutionised the health and social care industry’
To access the full webinar for free, please complete the form below and click submit. You will then receive an email to the address you supplied containing a link to view the webinar.
Follow Together for Health: