Background: There are tremendous opportunities for health research propelled by the recent expansion of big data and artificial intelligence. However, this is an emergent area where recommendations are needed.

Objectives: The objective was to develop points to consider for the use of big data by computational modelling, machine learning and artificial intelligence in rheumatic and musculoskeletal disorders (RMDs).

Methods: Based on a literature review of the current status of big data in RMDs and in other fields of medicine, on individual interviews of selected experts, and on the opinion of experts in a face-to-face meeting, points to consider were formulated, discussed and finalised by an international group of 14 experts from a range of disciplines including computer science and artificial intelligence. Levels of evidence and strengths of recommendations were allocated.

Results: The document comprises 5 overarching principles and 9 points to consider. The overarching principles address the definition of big data and artificial intelligence, types of big data, and ethical and general principles for dealing with big data in RMDs. The points to consider cover aspects of data sources and data collection, discussing privacy by design, use of specific data platforms, and data sharing; data analyses in particular through artificial intelligence and machine-learning; they refer to big data as a moving field in need of correct reporting of methods used and of benchmarking; and data interpretation and implementation in clinical practice.

Conclusion: These EULAR points to consider provide a framework for the use of big data in RMDs.

Disclosure of Interests: None declared

DOI: 10.1136/annrheumdis-2019-eular.8513