AI aims to transform early arthritis detection and treatment

Arthritis is a condition that causes joint pain, inflammation, and long-term disability, and it affects millions of people in the UK. One of the major challenges with arthritis is being able to diagnose it early enough to prevent severe long-term effects.

Inflammatory arthritis (IA) is particularly difficult to detect because there is no single test that can diagnose it early. Statistically, in the UK alone, about 10 million people live with arthritis, and many of those suffer from significant pain, inflammation, and even disability when it is not caught in its early stages.

Without early detection, arthritis can have a dramatic impact on quality of life, with delays in diagnosis and treatment leading to worsening symptoms, such as more severe joint damage and long-term loss of mobility. People with untreated or late-diagnosed arthritis often face physical limitations that can affect their ability to work, and many report high levels of mental health problems, including anxiety and depression.

The ‘National Early Inflammatory Arthritis Audit (NEIAA) State of the Nation Summary Report 2024’ highlights that over 400,000 people are living with inflammatory arthritis in the UK, but only a fraction of those are being diagnosed in time to start early treatment. Those who do receive treatment within six weeks of referral have a much better chance of remission, but many face longer waiting times, particularly if they are not referred through a specialist arthritis pathway.

Using machine learning to detect arthritis early

Leading a new project aimed at transforming how arthritis is detected is Professor Weizi Li, an expert in Informatics and Digital Health at Henley Business School, who has been awarded £600,000 by UK Research and Innovation (UKRI) to develop machine learning (ML) methods that could revolutionise early detection of inflammatory arthritis. The project, which is part of a larger initiative to apply AI in healthcare, will use advanced AI techniques to help doctors identify the signs of arthritis before it progresses to a more damaging stage.

The goal of Professor Li’s project is to catch arthritis earlier, allowing patients to receive treatment sooner. By using AI to analyse large sets of referral data, the machine learning system, RMD-Health, will detect patterns that might not be immediately obvious to clinicians. This will enable doctors to make quicker, more accurate diagnoses, reducing the need for referrals to overstretched rheumatology specialists, which currently creates substantial delays in the diagnostic process.

Improving patient outcomes and reducing healthcare strain

Professor Li’s interdisciplinary team includes AI experts, clinicians from both primary and secondary care, data ethicists, and specialists in health inequality and bioanalytics, and together, they are working to create a system that not only detects arthritis earlier but also provides personalised treatment recommendations based on individual patient data. This level of customisation is crucial because arthritis affects people in different ways, so by tailoring treatment to a patient’s specific needs the outcome could improve significantly.

The project is set to last 18 months and involves collaboration with major institutions, including the Royal Berkshire NHS Foundation Trust, the University of Oxford, and the University of Birmingham where these partners will be testing the AI system in real-world clinical settings. In fact, hospitals like the Royal Berkshire Hospital will be among the first to pilot the RMD-Health system. The team also plans to ensure the system is ready for regulatory approval and commercialisation, with the ultimate goal of rolling it out across the NHS.

According to the NEIAA report, many patients with inflammatory arthritis wait too long for diagnosis, and those delays can drastically reduce their chances of remission, so by using AI to detect early warning signs, doctors will be able to refer patients for treatment much faster, which could help prevent long-term disability. And with more accurate and timely diagnoses, fewer patients will need to go through lengthy referral processes, and those who do receive treatment will be more likely to recover, which in turn reduces the strain on the healthcare system. What this should mean in the long run is a better quality of life for patients and less pressure on the NHS.