AI could improve cancer diagnosis – if we get these 5 things right – Cancer Research UK

When all that training’s done, what does it mean for patients?  

The experience of having a scan or biopsy shouldn’t change at all. But the image or sample will be looked at by an AI system alongside a human doctor. They can work together in a couple of different ways. The AI could instantly highlight parts of tests that it identifies as worrying so a human can spot them more quickly, or it could help offer a fast second opinion if a doctor wants a further check.  

Why would we want to use AI? 

The cancer diagnostic workforce is under intense pressure to keep up with patient need.  

There is currently a shortage of both radiologists and pathologists. Modelling suggests that without changes, these shortfalls will get worse over the next 15 years. 

This problem is made even worse by the fact that demand for diagnostic services is forecast to grow. Cancer Research UK’s modelling suggests that the average number of new cases diagnosed each year is projected to reach half a million by 2040, up from around 385,000 between 2017 and 2019.  

If there are too many cancer tests to analyse, and not enough specialists to analyse them, then there’s a risk of delaying diagnosis. That can have serious consequences for patients. The earlier a cancer is diagnosed, the more likely it is that doctors can treat it successfully. On average, treatments given earlier should also be cheaper for the NHS.  

The NHS is already missing cancer waiting time targets.  If AI can perform some diagnostic tasks, it could help ease workforce pressures and reduce backlogs. And crucially for patients, it could free up doctors’ time. This would let them do things only they can do, like looking at complex cases, spending more time talking with patients and improving the general experience of care.  

What does the NHS need to do to use AI for cancer diagnosis?

Although AI technology isn’t ready to be used everywhere today, it’s improving fast. Bringing it into cancer diagnosis could be the next big step. Here are the key things the NHS, the UK Government and AI developers need to do to prepare. 

1. Support the workforce 

Today, the NHS doesn’t have all the technical knowhow it needs to effectively introduce AI to cancer diagnosis. These tools are complicated: it will take full-time specialists to set them up, maintain them and troubleshoot them. 

Recent projections suggest that, based on current trends, the NHS will be short of about 17,800 digital and data specialists by 2030. Due to limitations in the current NHS Electronic Staff Record, there’s no way to accurately identify who works in digital roles. We need this information for effective workforce planning. 

The UK Government’s upcoming national digital workforce strategy needs lay out exactly how these missing technicians will be trained and recruited. Employers everywhere are looking to hire people with digital skills, so the NHS needs to make its digital jobs more attractive, with clear career pathways, to compete. 

Current NHS staff also need to feel confident using AI tools in their day-to-day work. That will take training. Estimates suggest 90% of NHS workers will need digital skills over the next 20 years. However, at present 43% of NHS staff feel that they can’t access the right learning and development opportunities when they need to, on any topic. 

NHS employers need to provide training opportunities to make sure staff are comfortable with AI tools and understand how to use them appropriately. 

2. Build the right infrastructure  

AI systems need digital data. Radiology in the UK is already fully digitised, which means it’s in a good position to embrace AI. Most pathologists, however, still look at samples directly under a microscope. In digital pathology, the glass slides are scanned and then viewed on a computer instead. Before AI can assist pathologists in diagnosing cancer from biopsy samples, the NHS needs to invest in a more extensive digital pathology system.  

That’s not all. AI tools often require data from multiple IT systems, like imaging databases or electronic patient records. We want these IT systems to be able to communicate with each other, so they can automatically access the data they need to do their job, rather than relying on people to transfer it across. Unless they can do this – be ‘interoperable’ – then they won’t save much time.

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