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Redefining biomaterial biocompatibility: challenges for artificial intelligence and text mining: challenges for artificial intelligence and text mining

  • Miguel Mateu-Sanz
  • , Carla V. Fuenteslópez
  • , Juan Uribe-Gomez
  • , Håvard Jostein Haugen
  • , Abhay Pandit
  • , Maria-Pau Ginebra
  • , Osnat Hakimi
  • , Martin Krallinger
  • , Athina Samara

Research output: Contribution to a Journal (Peer & Non Peer)Articlepeer-review

75 Citations (Scopus)

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.

Original languageUndefined/Unknown
Pages (from-to)402-417
Number of pages16
JournalTrends in Biotechnology
Volume42
Issue number4
DOIs
Publication statusPublished - Apr 2024

Keywords

  • artificial intelligence
  • biocompatibility
  • biomaterial
  • data mining
  • databases
  • international organization for standardization (ISO)
  • natural language processing

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