Why we need artificial intelligence in environmental rheumatology

Author information

CER3
2024 Vol.1, N°1
PI 0010, PF 0014
Reviews

Received: 24/05/2024
Accepted : 27/05/2024
In Press: 27/05/2024
Published: 10/07/2024

Abstract

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 analysing 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 utilising 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.