Artificial Intelligence and Deep Learning for Antimicrobial Resistance Prediction: A Scoping Review of Architectures and Explainability

Antimicrobial resistance (AMR) poses a critical global challenge, driven by the widespread misuse of antibiotics across human, animal, and environmental domains. Conventional laboratory-based antimicrobial susceptibility testing is time-consuming and resource-intensive, limiting its scalability and clinical responsiveness. Artificial intelligence (AI) and deep learning (DL) have emerged as effective computational tools for predicting AMR from heterogeneous data sources. This scoping review systematically maps AI- and DL-based methodologies for AMR prediction across 20 peer-reviewed studies published between 2021 and 2025. Following PRISMA-ScR guidelines, we analyze methodological trends, strengths, and limitations across three thematic clusters: genomic and spectrometric prediction, clinical and EHR-based prediction, and proteomic antibiotic resistance gene detection. A three-generation architectural progression is observed, from classical machine learning and CNN-based models, through XAI-integrated CNNs functioning as biological discovery tools, to large language model and foundation model approaches enabling few-shot adaptation to emerging antibiotics. Seven distinct explainability methods are identified, of which four achieve formally validated attributions and five demonstrate biologically grounded explanations, with partial overlap between categories. Key challenges include limited data standardization, absence of temporal and geographic external validation, heterogeneous and unvalidated explainability practices, and real-world deployment constraints. This review provides a structured technical synthesis of AI-driven AMR prediction methodologies and outlines future research priorities toward multi-species evaluation, temporally validated models, standardized explainability reporting, and clinically deployable systems aligned with the One Health paradigm.