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AI tools are being developed to help clinicians interpret MRI scans for axial spondyloarthritis, the disease spectrum that includes ankylosing spondylitis (AS). But current studies show that these tools can identify patterns in study datasets—not that patients are getting diagnosed sooner in routine care. The distinction matters because diagnosis still depends on symptoms, clinical assessment, imaging and, sometimes, blood tests.
Why can ankylosing spondylitis be hard to diagnose?
AS is the radiographic form of axial spondyloarthritis (axSpA), a group of inflammatory conditions affecting the spine and joints between the spine and pelvis. In radiographic axSpA, X-rays show characteristic changes in the sacroiliac joints. In non-radiographic axSpA, those changes are not visible on X-ray, even though a person may have symptoms and other evidence of disease.
Early inflammatory back pain can resemble common mechanical back pain, and symptoms do not always follow one pattern. Inflammation can also affect places beyond the spine, including tendons and joints. Uveitis (inflammation in the eye), psoriasis and inflammatory bowel disease may be relevant clues, as can family history. These features are reasons for a clinician to consider the wider picture; none confirms axSpA on its own.
That is why a normal X-ray does not necessarily settle the question, and why a single symptom or test result should not be used to rule the condition in or out. NICE’s recommendation 1.1.1 in Spondyloarthritis in over 16s: diagnosis and management says: “Do not rule out the possibility of spondyloarthritis solely on the presence or absence of any individual sign, symptom or test result.”
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What does the diagnostic process involve?
A clinician will usually consider the history and duration of symptoms, examine the patient, and decide whether blood tests or imaging are appropriate. The NHS describes assessment that may include tests for inflammation and HLA-B27, as well as X-ray or MRI and a rheumatology assessment. The tests contribute evidence; they are not standalone verdicts.
- HLA-B27: A positive result can support suspicion in the right clinical context, but it does not prove axSpA. A negative result does not exclude it; axSpA can occur in people who are HLA-B27 negative.
- Inflammation blood tests: These may add information, but a result on its own cannot establish or rule out the diagnosis.
- X-ray: It can show sacroiliac-joint changes associated with radiographic disease. Earlier disease may not yet be visible.
- MRI: It can show inflammation when an X-ray is not diagnostic. The scan still needs to be interpreted alongside symptoms and other clinical findings.
NICE referral guidance includes people whose back pain began before age 45 and has lasted more than three months, together with combinations of additional features. Whether assessment or referral is appropriate depends on the individual situation; this is not a self-diagnosis score. Discuss persistent symptoms and relevant features with a healthcare professional.
How long is the delay, and what do the figures measure?
“Delay” can mean different things: time from first symptoms to a specialist appointment, time to a confirmed diagnosis, or time to treatment. Those intervals should not be treated as interchangeable.
A 2022 analysis of the National Early Inflammatory Arthritis Audit reported that 79.7% of 784 patients with axSpA had experienced symptoms for more than six months before their initial rheumatology assessment. The cohort was recruited in England and Wales from May 2018 to March 2020. This measures symptoms before the first rheumatology assessment—not the total time from symptoms to diagnosis.
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An older ASAS referral recommendation page has cited a five-to-eight-year gap between symptom onset and diagnosis. That estimate comes from an earlier consensus-era source and should not be presented as a current, universal average. Separately, a 2024 ASAS consensus definition calls axSpA “early” for research purposes when axial symptoms have lasted two years or less. That research definition applies to people with an axSpA diagnosis; it is not a diagnostic cutoff for an individual.
What is AI being tested to do?
The clearest examples in axSpA research involve analyzing MRI images of the sacroiliac joints. A model can be trained to detect image patterns associated with active inflammation or structural changes. Other approaches combine MRI findings with clinical risk factors. In principle, these tools could help clinicians spot or characterize findings more consistently, including in scans that require expert interpretation.
That is clinical decision support, not an autonomous diagnosis. The clinician still needs to judge whether scan findings fit the person’s symptoms and other evidence, and to consider alternative explanations. The studies described below evaluate model performance, not whether an AI system can replace a rheumatologist.
| Study | Patients and design | What the AI evaluated | What the result establishes |
|---|---|---|---|
| Radiology retrospective study | 593 patients with suspected axSpA; sacroiliac MRI was centrally evaluated. | A deep-learning model detected active inflammatory and structural changes indicative of axSpA. | Evidence of image-analysis capability in a study dataset. The authors called for prospective work to establish clinical value and effects on therapy. |
| 2025 multicentre study | 1,294 patients; the model was tested on internal, external and prospective-validation datasets. | An MRI model combined with clinical factors. | Validation across datasets, with performance varying by dataset. The abstract reported an AUC of 0.812 for prospective validation; that is a model-performance measure, not a measure of time saved or diagnoses made sooner. |
The two studies are not a head-to-head comparison: they used different patient groups, designs and validation approaches. A single performance metric cannot show which approach would improve care more in ordinary practice.
Has AI been shown to get patients answers faster?
Not in the evidence described here. A 2024 review of AI and machine-learning work in axSpA covers radiography, CT, MRI, predictive modelling and monitoring. It describes potential, while noting limitations such as variable study designs, sample sizes and the prevalence of retrospective, single-centre studies. The imaging studies demonstrate model development or validation, not a verified reduction in real-world time to diagnosis or improved patient outcomes.
To show that AI actually shortens delays, research would need to evaluate its use in clinical care and measure outcomes such as time to assessment or diagnosis, as well as whether decisions and patient care improve. The imaging researchers specifically call for prospective studies of clinical value. Until such benefits are demonstrated, “AI is helping patients get answers faster” is a possibility under study, not an established result.
What can you do if symptoms persist?
A concise record can make a clinical conversation more useful. It is not a checklist for deciding whether you have axSpA.
- Note when the back pain began, how long it has persisted, and whether its pattern has changed.
- Record other relevant symptoms, including painful or swollen joints, tendon-area pain, eye inflammation, psoriasis or inflammatory bowel disease.
- Include relevant family history and any tests or imaging you have already had, along with when they were done.
- Ask whether the pattern warrants further assessment or rheumatology referral, even if a previous result—such as an X-ray or HLA-B27 test—was negative.
Persistent back pain has many possible causes. A healthcare professional can assess the pattern and decide whether further evaluation is appropriate.
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