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Thought Leadership | 31st March 2022

Is artificial intelligence (AI) the future of healthcare?

Read Time: 5 minutes


Artificial intelligence (AI) has experienced a post-pandemic boom. But what does this mean for its future use in healthcare? Will we be talking to robot doctors and nurses in the years to come? The evidence suggests there is a lack of public trust and an unwillingness to engage in AI healthcare technology despite the benefits. So, how will AI fit into the future of healthcare. 

The use of artificial intelligence (AI) in healthcare is increasingly important in a post-pandemic world. While this isn’t a new phenomenon, it is one of many pre-existing healthcare trends accelerated by the pandemic. 

The growing use of AI represents a long-term structural shift in how services and treatments are delivered, which will have lasting implications for the future of the health service.  

In recognition of the momentum behind this issue, Health Education England (HEE) has published the first roadmap on using AI in the National Health Service (NHS). The report looks at AI-driven technologies in the health service and their potential to drive future innovation across the NHS over the next 15-20 years1 

It found that imaging, pathology and endoscopy was the most common use of AI in healthcare with a 34% share1. Moreover, of the 56 technologies scheduled for large-scale deployment in the next year, 77% will be used in secondary care and 23% in primary care1 

Digital transformation is also one of the key areas of focus identified in the NHS’s Long Terms Plan2 and will involve AI acting as a catalyst for change across the healthcare service. Part of this transformation will involve greater use of technology like chatbots and virtual assistants to change the way services are delivered.  

Chatbots and digital dialogue  

Healthcare AI chatbots can fulfil various traditional healthcare functions, including booking appointments, ordering prescriptions, checking symptoms, and providing basic medical information and advice. Their use is set to increase markedly over the coming decade3 

Babylon Health, who provide patients with an AI chatbot based consultation service using their medical history and standardised medical knowledge, is a striking example of this4. Patients can initially report symptoms on an app, which is checked against an official medical database, using speech recognition technology4. They will then receive a recommended course of action based on the information provided. The success of their work led them to partner with the NHS to develop a digital triage system for referrals and dispensing health advice during the COVID-19 crisis4.   

There is also evidence to suggest that AI chatbots could be used to remove the stigma associated with certain health conditions. Research from the University of Westminster suggests that AI chatbots could help patients discuss sensitive health conditions in a more open and frank manner5. The research findings published in the SAGE Digital Health Journal found that for highly stigmatising health conditions like STIs, participants would rather speak to an AI chatbot than a GP5. In addition to service delivery, there is also evidence to suggest that AI is having an increasing use in NHS diagnostics. 

Making a difference in diagnosis 

The British Heart Foundation has recently reported that an AI tool is now being used to detect heart disease more quickly and accurately to improve patients’ care6. The imaging technology works by analysing heart MRI scans whilst the patient is still in the scanner6. It takes just 20 seconds for the scan results to be analysed, compared to 13 mins for a doctor to manually assess the images6. The new tool can also identify heart structure and function issues with 40% more accuracy than the human eye6. AI technology like this can have a particularly important role when clinicians are increasingly time poor, and the healthcare service is trying to recover from the critical care backlog caused by the pandemic6 

Furthermore, evidence from NHS Shared Business Services suggests that AI can reduce the decision-making time for thrombolysis and thrombectomies7. AI-powered imaging technology can interpret MRI and CT scan results within seconds, rather than the 30 minutes it would take a doctor to assess the results manually7 

AI technology is already a central part of the National Optimal Stroke Imaging Pathway (NOSIP)7. AI algorithms can be used to accelerate the clinical decision-making process by providing a real-time interpretation of scan images more quickly and accurately7. Diagnosis and treatment interventions are very time-sensitive for stroke patients, and technology like this could help save lives7. One of the key milestones for future stroke care will involve scaling this technology to drive the expansion of life-changing treatments7 

The use of AI technology is not limited to diagnostic and service delivery, it is also being used in the health service to address some of the underlying healthcare inequalities exposed by the pandemic.  

Tackling healthcare inequalities  

AI-led innovation is also being used to address some of the structural system issues associated with access to healthcare. For instance, NHSX AI Lab and the Health Foundation are currently undertaking joint project work using AI to address the racial and ethical healthcare inequalities in the NHS8 

There are several fascinating technological innovations in the pipeline, such as I-SIRch, which uses AI technology to make the clinical factors that lead to adverse maternity incidents in mothers from ethnic minority backgrounds easier to identify8. Black women are five times more likely to die in the UK due to pregnancy complications than white women9. I-SIRch will assess how different factors combine to cause these maternity issues and enable better forms of intervention to be designed8. Part of this will also include new AI development training for midwives and nurses.  

AI diagnostic technology for ethnic minorities is also under development for diabetic retinopathy screening8. Recent studies have shown that people of Indian, Pakistani, Bangladeshi and Caribbean ethnic groups are at increased risk of developing diabetic retinopathy compared to white people10.

The accuracy of AI retinal image analysis systems (ARIAS) to detect diabetic retinopathy varies between different ethnic minority groups due to their higher levels of retinal pigmentation8. New technology has the potential to increase the speed and accuracy of diagnosis and subsequent treatment for ethnic minority groups by accounting for genetic differences in retinal composition8. Such pioneering technology is currently under trial in North London to provide evidence of efficacy prior to potential commissioning and wider rollout8. 

Whatever the future may hold for the adoption of AI in the NHS, it will clearly have an enhanced role in the post-pandemic healthcare system. The latest AI technology has the potential to transform the way healthcare and diagnostic services are delivered and address some of the underlying healthcare inequalities exposed by the pandemic.



1. Health Education England. AI Roadmap report. Accessed on March 2022. 2. NHS England. NHS Long Term Plan. Accessed March 2022. 3. Vantage Market Research. Healthcare Chatbots Market to Reach 431.47 USD Million by 2028 Accessed March 2022. 4. Engati. How are intelligent healthcare chatbots being used?,generation%2C%20result%20analysis%2C%20etc. Accessed March 2022. 5. Medical Futurist. The Top 12 Health Chatbots Accessed March 2022. 6. Digital Health. AI chatbots could help patients discuss sensitive health conditions Accessed March 2022 7. British Heart Foundation. Superhuman 20 second AI heart tool begins NHS roll-out Accessed March 2022. 8. Computer Weekly. NHS AI framework offers decision support for stroke diagnosis Accessed March 2022. 9. The Health Foundation. Artificial Intelligence and Racial and Ethnic Inequalities Accessed March 2022. 10. The Guardian. AI projects to tackle racial inequality in UK healthcare. Accessed March 2022. 11. Nugawela, M.D, Gurudas, S, Prevost, A.T. et al. Ethnic Disparities in the Development of Sight-Threatening Diabetic Retinopathy in a UK Multi-Ethnic Population with Diabetes: An Observational Cohort Study. J. Pers. Med. 2021, 11, 740.
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