M. Mateu-Sanz et al. Redefining biomaterial biocompatibility: challenges for artificial intelligence and text mining. Trends in Biotechnology

M. Mateu-Sanz, C.V. Fuenteslópez, J. Uribe-Gomez, H.J. Haugen, A. Pandit, M.P. Ginebra, O. Hakimi, M. Krallinger, A. Samara. Redefining biomaterial biocompatibility: challenges for artificial intelligence and text mining. Trends in Biotechnology, 2408. OPEN ACCESS.

doi: 10.1016/j.tibtech.2023.09.015

Abstract

The surge in ‘Big data’ has significantly influenced biomaterials research and development, with vast data volumes emerging from clinical trials, scientific literature, electronic health records, and other sources. Biocompatibility is essential in developing safe medical devices and biomaterials to perform as intended without provoking adverse reactions. Therefore, establishing an artificial intelligence (AI)-driven biocompatibility definition has become decisive for automating data extraction and profiling safety effectiveness. This definition should both reflect the attributes related to biocompatibility and be compatible with computational data-mining methods. Here, we discuss the need for a comprehensive and contemporary definition of biocompatibility and the challenges in developing one. We also identify the key elements that comprise biocompatibility, and propose an integrated biocompatibility definition that enables data-mining approaches.