Artificial intelligence (AI) holds the potential to address various challenges in environmental rheumatology. These challenges include the need for better understanding complex relationships within environmental data, fast prediction of environment-related parameters, risk assessment, and environmental decision-making.
AI can assist in analyzing large datasets to identify genetic risk factors for adverse reactions to toxic exposures, as well as in detecting gene-environment interactions and epigenetic alterations. Furthermore, AI can play a crucial role in toxicity prediction by utilizing deep learning algorithms to assess complicated bioactivity data, offering a solution where traditional methods fall short. Despite the promise of AI, challenges such as insufficient or poor-quality data, model interpretation difficulties, and concerns about bias in outcomes need to be addressed for successful integration of AI in environmental rheumatology.