Saxer, Franziska, Sita, Bierma-Zeinstra, Jansen, Gunther, Demanse, David, Holzhauer, Bjoern, Mesenbrink, Peter, Melnick, Justin, Rall, Thorsten and Schieker, Matthias (2025) Why is there no treatment for OA - Opportunity for AI based big data analytics to advance the field. Osteoarthritis and cartilage.

Abstract

Background: Osteoarthritis (OA) has been researched for decades but insights have not translated into a treatment for OA. One reason may be the heterogeneity of patients suffering from OA. Machine-learning (ML) could be leveraged to detect patterns of patient characteristics that allow stratification and the development of surrogate endpoints to improve trial designs and translate scientific advances to tangible benefits for patients.
Opportunity: The article describes the vision of collaborative inter-professional, inter-institutional as well as public-private activity leveraging the wealth of data to achieve this goal. We summarize the underlying assumptions, challenges and potential applications of a ML-based approach.
Use cases: Employing federated approach training algorithms locally has the advantage of preserving privacy. The application of novel ML techniques to divers sets of multidimensional health care data such as registries, real-world evidence, trial data etc. allows not only prognostic and predictive inferences but can also overcome issues with incompleteness of variables, heterogeneity in database structures and multidimensionality of variables. This exploration of data can form the foundation for the development of covariates, digital twins, synthetic control groups and form a potential basis for trial emulation. In addition, the approach will enable the development of novel (surrogate) endpoints and inform enrichment strategies.
Conclusion: Leveraging ML, the richness of data on OA and the expertise from various areas including patients, providers, ethicists and regulators has the potential to revolutionize trial designs in OA and finally meet the needs of patients suffering from OA.

Item Type:	Article
Keywords:	Machine learning, artificial intelligence, transformers, transfer learning, digital twin, covariates, methodology, outcome research
Date Deposited:	17 Jan 2026 00:46
Last Modified:	17 Jan 2026 00:46
URI:	https://oak.novartis.com/id/eprint/57981