As the UK’s first clinical professor of AI in radiotherapy, Professor Raj Jena talks to Helen Quinn about the impact of artificial intelligence and deep learning tools on radiotherapy and patient care, the opportunities and challenges he’s hoping to tackle in his new role, and the broader potential of AI in healthcare.
Put simply, artificial Intelligence (AI) has the potential to transform healthcare. Effective use of emerging machine learning techniques can improve patient care, complement clinicians’ work and address a range of challenges. If used well and in the proper context, AI can enhance diagnostic processes, personalise treatment plans and efficiently manage healthcare data, all while freeing up clinicians’ time to focus on the direct human aspect of healthcare.
Within this rapidly evolving technology landscape, the University of Cambridge has appointed the UK’s first clinical professor of AI in radiotherapy, signalling a need for, and a commitment to, utilising AI in the fight against cancer. Taking up this novel role is Professor Raj Jena, who is also a research scientist and consultant oncologist at Cambridge University Hospitals NHS Foundation Trust.
Professor Jena specialises in using advanced imaging techniques to improve outcomes for patients with central nervous system tumours. Through his research, he has helped to develop an AI tool called Osairis, which can enhance and accelerate tumour analysis.
Machine learning for radiotherapy is now routinely used throughout Cambridge University Hospitals NHS Foundation Trust. It has reduced the waiting time for patients between referral and commencing curative radiotherapy treatment, which can, in turn, improve survival rates in some patients.
Aligning AI research and clinical practice
The new AI clinical professorship reflects the progress in balancing clinical practice and domain expertise in radiotherapy whilst maintaining and leading an academic group delivering high-quality research, says Professor Jena.
‘We’re trying to link the latest and greatest thinking in data science, machine learning and AI to what we do in the clinic,’ he explains. ‘Most people think an oncology consultant who’s active in research would either be working in a wet lab or work in the area of clinical trials. So, it’s quite nice to identify the fact that there is another way an academic oncologist can contribute to research.’
In fact, over the past 20 years, Professor Jena has concentrated on using mathematics and computation to analyse medical images – something he says has been recognised in the new clinical professor of AI in radiotherapy role. ‘I’ve been interested in computational approaches for years, but nowadays it’s reached the mainstream, and it’s called AI. It’s great because we can ride the wave of interest in AI,’ he says.
Using AI in radiotherapy
The use of AI in medical imaging involves applying a deep learning model to perform clinically valuable tasks. This is particularly applicable to analysing radiotherapy images, making it a highly effective technique in this field.
‘If you look at clinically useful applications of AI across the whole of medicine, the reality is that we’re still at the start of that story. But in radiation therapy, we happen to have a problem that lends itself to a solution in deep learning,’ Professor Jena explains. ‘We’ve gone quite quickly, from these approaches being just research to actually being plumbed into the clinic and helping patients get started on potentially life-saving radiotherapy more quickly.’
The development of the Osairis tool stemmed from a chance meeting between Professor Jena and Dr Antonio Criminisi PhD, a machine learning engineer and the head of Microsoft’s AI research programme in the UK.
Dr Criminisi taught computers to analyse the movement of the human body from the outside, recognising specific positions so a person’s body could be used effectively as a controller, for example, in sports-related video games. Professor Jena was curious whether this approach could be applied inside the body, too, and invited Dr Criminisi to his hospital department to observe radiation oncologists marking up scans of patients waiting to start radiotherapy treatment.
The outcome was the development of an open-source deep learning tool for automatic segmentation of radiotherapy images and the first AI technology to be developed and deployed within the NHS.
‘It was a very prescient point, we could then take the tooling and actually build our own machine learning models from our own patients’ data, test them out, and then for the first time, within the hospital, build a medical device,’ Professor Jena explains.
Cambridge University Hospitals Trust invested in cloud computing across its sites, allowing Professor Jena’s team to implement deep learning tools throughout the Trust. Now, when a patient with a head, neck or brain tumour comes for a scan, the scan data is anonymised, encrypted and sent off for analysis using the AI technology. It has been found to accelerate clinicians’ radiotherapy planning by approximately two and a half times.
‘What the algorithm does is to mark out every healthy structure we need to be aware of when planning radiotherapy treatment. And that means that the oncologist can be much faster in creating a safe radiotherapy plan,’ Professor Jena says. ‘Something that used to take maybe an hour and 40 minutes can be done in half an hour, so you can see patients faster and free up clinicians and patients get started on radiotherapy more quickly, too.’
The challenges
Despite the myriad ways in which AI can support healthcare systems, challenges remain. Many machine learning models are built based on available data rather than in response to a particular patient need, and the data required to build specific models can be difficult to obtain.
Professor Jena says turning available data into necessary data requires considerable effort. He hopes his new professorship, which straddles both the research and clinical environments, will help him achieve this as he builds AI tooling to address specific patient needs and avoid bias in the system.
AI technology is also moving rapidly, and the journey has sometimes involved missteps, including breaches of data usage and sharing of data with industry. Professor Jena warns that robust governance needs to be in place to prevent further issues, particularly as AI models begin incorporating more sensitive data, such as genomic information.
‘I think we have to take those things and learn how to do it better. The biggest thing we can do is make examples where we do this right, that are highly shareable and highly applicable,’ Professor Jena says.
Enhancing future healthcare
Radiotherapy is an exemplar of the successful use of AI in healthcare, but Professor Jena hopes there will be a cross-fertilisation of technology, enabling AI to evolve and excel at interpreting non-image data as well. He believes AI can ‘make real inroads’ in diagnostics for the early detection of cancer. Early works suggest it could be used in tests that can look for cancer in urine, blood or even exhaled breath, for example.
The tools could also play a role in personalising treatments for cancer patients since AI can look for patterns and simplify very high-dimensional data. In complicated cancers such as brain tumours, where several medications might be marginally effective, an AI model could examine that information, align it with changes in the patient’s tumour and suggest a personalised medication plan.
‘I think this is where we really want to push,’ says Professor Jena. ‘Personalised medicine is very interesting to us because we now get so much information when a cancer is diagnosed, including genomics, which can highlight mutations and indicate a patient may benefit from some kind of targeted drug. I think the paradigm changes around AI in medicine will come within the areas of precision medicine or drug discovery.’
Ultimately, Professor Jena says that AI will complement and enhance much of what clinicians already do, freeing them from time-consuming, data-heavy tasks.
‘As you build these workflow acceleration tools, all staff will move towards a situation where they’re spending more time either listening to patients directly or making decisions. I think that will make a huge difference,’ he says. As well as awaiting the paradigm shift in AI, I’m a great believer in bringing together multiple AI tools where each one saves time or increases safety. Adding up all of these small increments can still make a huge impact on the delivery of human-centric care in the clinic.’
