Artificial intelligence applications in biofilm research: computational approaches for biofilm analysis and antimicrobial prediction
Abstract
Biofilms are organized groups of microbes surrounded by an extracellular polymeric substance (EPS) matrix. This structure helps microbes survive, creates metabolic differences, and makes them less sensitive to antimicrobial treatments. Because biofilms can block antimicrobials and help microbes adapt, they often cause chronic and recurring infections that are hard to treat with standard methods. Most current diagnostic and antimicrobial testing methods focus on free-floating (planktonic) microbes and do not reflect the complex structure and behavior of mature biofilms. This gap often leads to ongoing treatment failures and poor predictions of treatment outcomes. Recently, artificial intelligence (AI) and computational modeling have shown promise for improving biofilm research. These tools can help with automated detection, structural analysis, computational phenotyping, and predicting how biofilms will respond to treatments. This review examines current and emerging AI-based methods in biofilm biology, focusing on computational analysis, prediction of antimicrobial responses, and AI-supported antibiofilm therapies. It also discusses challenges such as dataset differences, limited real-world testing, difficulty understanding models, and a lack of models for clinically important mixed-species biofilms. Overall, this review shows how AI could help improve the accuracy, integration, and tailoring of biofilm research and antimicrobial management.
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Evidence Hub reads this as a review, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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