Artificial Intelligence for Antimicrobial Resistance Prediction: Challenges and Opportunities towards Practical Implementation

Abstract

Antimicrobial resistance (AMR) is emerging as a potential threat to many lives worldwide. It is very important to understand and apply effective strategies to counter the impact of AMR and its mutation from a medical treatment point of view. The intersection of artificial intelligence (AI), especially deep learning/machine learning, has led to a new direction in antimicrobial identification. Furthermore, presently, the availability of huge amounts of data from multiple sources has made it more effective to use these artificial intelligence techniques to identify interesting insights into AMR genes such as new genes, mutations, drug identification, conditions favorable to spread, and so on. Therefore, this paper presents a review of state-of-the-art challenges and opportunities. These include interesting input features posing challenges in use, state-of-the-art deep-learning/machine-learning models for robustness and high accuracy, challenges, and prospects to apply these techniques for practical purposes. The paper concludes with the encouragement to apply AI to the AMR sector with the intention of practical diagnosis and treatment, since presently most studies are at early stages with minimal application in the practice of diagnosis and treatment of disease.

Navigating Antibacterial Frontiers: A Panoramic Exploration of Antibacterial Landscapes, Resistance Mechanisms, and Emerging Therapeutic Strategies

“…This analysis also demonstrates potential areas of growth in this field through, for example, targeting other bacteria apart from the ones previously mentioned; analysis of potential antibacterials in the realm of biocompatibility, cytotoxicity, stability, and metabolites; increasing the use of AI in antibacterial resistance studies, screening, and development of a larger scope of potential antibiotics of different classes; application of AI in nanoparticle and nanomaterial construction, use, and screening as potential antibacterial agents; the application of certain statistical and AI models, such as regression analysis and random forest ML algorithms; and the drastic need for improving the processing of all the information generated by this new technology. Various reviews discussing the use of AI and its limitations in resistance studies and drug discovery have been published. ,,− …”

Combating antimicrobial resistance: the silent war

“…In addition, machine-learning models can be utilized as the surveillance of AMR by analyzing data on antimicrobial use and resistant micro-organism, they can aid public health authorities to make informed decisions, prepare and respond immediately in the outbreak of resistance health issue when these models identify and predict the identify emerging resistance patterns and potential population and areas ( Rabaan et al, 2022 ). These applications contribute to reducing the overall burden of AMR ( Ali et al, 2023 ).…”

Revolutionizing the fight against antimicrobial resistance with artificial intelligence

“…One promising solution is the use of artificial intelligence (AI). AI has the potential to revolutionize our approach to tackling antimicrobial resistance by facilitating rapid diagnosis, predicting antibiotic resistance patterns, and identifying new treatments [12,14]. AI can analyze large amounts of data from a wide range of sources, including electronic health records, clinical trials, and public health databases.…”

“…By analyzing clinical trial data and other sources, AI algorithms can spotlight promising drug candidates and forecast their efficacy with high accuracy. [9,12,14,15].…”

“…Artificial intelligence (AI) has emerged as a promising tool in the fight against AMR, with the potential to facilitate rapid diagnosis, predict antibiotic resistance patterns, and identify new treatments. AI in AMR research has shown promise in predicting which microorganisms are likely to develop resistance to certain drugs, identifying emerging resistance patterns, and optimizing antimicrobial use in healthcare settings [12]. However, the application of ML/DL models in the field of antimicrobials faces several challenges, including the availability of high-quality data, validation of results, and environmental variability [13].…”