Wilson Bautista-Molano University Hospital Fundación Santa Fe de Bogotá, School of Medicine, Universidad El Bosque, Colombia
Citation: EMJ Rheumatol. 2026; https://doi.org/10.33590/emjrheumatol/56N26YS0
Looking back on the European Alliance of Associations for Rheumatology (EULAR) 2026 Congress, what were the most significant scientific advances or discussions that stood out to you, and why do you believe they are important for clinical practice?
One of the most relevant aspects of EULAR 2026 was the continued move towards more personalised, data-driven rheumatology for inflammatory conditions. Perhaps the most notable development was the combination of advances in risk stratification, imaging, and the integration of AI into research and clinical decision-making. These developments are relevant because they are helping us move from a purely disease-centred approach to a more patient-centred model, where early identification, prognostic assessment, risk evaluation, and treatment profile stratification are increasingly feasible in daily clinical practice.
You co-chaired the ‘Mind the Spine: Imaging in Spondyloarthritis’ oral abstract session. What were the key takeaways from the research presented, and how do you see imaging evolving in the management of axial spondyloarthritis?
The session highlighted how imaging is becoming more advanced in both the diagnosis and longitudinal assessment for axial spondyloarthritis. A key message was that imaging is no longer limited to confirming inflammation; it is also increasingly important for understanding structural damage, treatment response, and even disease progression over time. I strongly believe that imaging will continue to evolve towards more standardised, reproducible, and quantitative tools that may support earlier diagnosis and more precise disease monitoring over time.
Several presentations in that session explored structural progression and emerging imaging technologies. Which developments do you think have the greatest potential to improve diagnosis or monitoring in the coming years?
This is a very interesting question. Among the most promising developments are quantitative MRI approaches, improved protocols for structural assessment, and the application of machine learning to image acquisition and interpretation. These innovations have the potential to reduce variability, improve sensitivity to change, and support earlier identification of clinically relevant progression. In the coming years, I expect imaging to become not only more accurate, but also more integrated into predictive models that combine clinical, laboratory, and imaging data, especially in axial spondyloarthritis.
You have been involved in international initiatives focused on risk stratification in rheumatoid arthritis. How might more personalised approaches change the way we manage patients in the future?
Personalised risk stratification has the potential to transform rheumatoid arthritis management by allowing us to identify which patients are more likely to develop severe disease, structural damage, or sub-optimal treatment response. This would support earlier and more targeted intervention rather than a one-size-fits-all approach, which is the current clinical practice. Over time, it should help improve outcomes while also reducing unnecessary treatment escalation in lower-risk patients. I have been involved in a global collaborative study aimed at defining criteria for the assessment of preclinical phases in rheumatoid arthritis, which may allow us to reach an even earlier stage, before the onset of clinical manifestations.
Your research has contributed significantly to our understanding of spondyloarthritis in Latin America. What unique insights can global registries and multinational datasets provide that single-country studies cannot?
Global registries and multinational datasets provide a broader and more diverse perspective than single-country studies, particularly for diseases such as spondyloarthritis where phenotype, access to care, and treatment patterns may vary across regions. They allow us to identify shared disease characteristics as well as regional and geographic differences that may otherwise be overlooked and unnoticed. For instance, in Latin America, these collaborative efforts are especially valuable because they help generate evidence that is both locally relevant and globally comparable. This is the case of the Registro de Espondiloartritis Axial de America–Pan-American League of Associations for Rheumatology (ESPALDA-PANLAR)registry, which is currently collecting clinical, imaging, and therapeutic information of patients with axial spondyloarthritis from all countries in Latin America.
There has been growing interest in identifying patients earlier in the disease course. How do you think our understanding of early axial spondyloarthritis has changed over the past decade?
I think our understanding of early axial spondyloarthritis has changed substantially over the past decade. We now recognise that clinically significant disease may exist well before classical radiographic damage becomes evident, for example in radiographs, which has improved our ability to identify and classify patients earlier. This change has reinforced the importance of combining clinical assessment, imaging, and biomarker research to better define disease at its earliest, and probably pre-clinical, stages. This is highly relevant considering the different clinical phenotypes of this heterogenous condition, such as the peripheral manifestations (e.g., arthritis, enthesitis, and dactylitis).
AI and advanced data analytics featured prominently at this year’s Congress. How do you foresee these tools influencing rheumatology research and patient care over the next 5–10 years?
AI and advanced data analytics are likely to have a major impact on rheumatology over the next 5–10 years, particularly in disease pattern recognition, risk prediction tools, and the analysis of large datasets or registries. In research, these tools can help us identify disease sub-groups, predict clinical outcomes, and generate more efficient study designs. In clinical care, they may support earlier diagnosis, decision support (e.g., treatment), and more individualised monitoring, although their implementation will need careful validation and ethical considerations. In the end, I can say that there are several opportunities to use AI, but there are also many challenges when considering implementation in routine clinical practice in the context of a real-world setting.
Finally, what are your key priorities for the next year, both in your own research programme and within the wider rheumatology community?
My priorities for the next year, in line with the priorities of our group, are to maintain working on and strengthening international collaborative research in the field of spondyloarthritis and rheumatoid arthritis, especially in the generation of real-world data. We also want to keep building and expanding international partnerships and helping to increase the visibility of Latin American rheumatology research.
