Data Modeling

1. Introduction

Antimicrobial resistance is, by now, a familiar phrase, perhaps too familiar. It has been called a silent pandemic so many times that the label risks losing its force, yet the underlying numbers have lost none of their weight. Bacterial AMR was associated with roughly 4.95 million deaths worldwide in 2019, of which 1.27 million were directly attributable to resistant infections (Murray et al., 2022; Hassan et al., 2026), and the trajectory, unfortunately, does not bend downward on its own. Projections from the Global Research on Antimicrobial Resistance (GRAM) initiative suggest that drug-resistant infections could claim a cumulative 39 million lives between 2025 and 2050 (Sardar et al., 2026). The economic shadow cast by this crisis is comparably stark: unmitigated resistance could add up to $1 trillion in healthcare expenditure by 2050 and shave $1 trillion to $3.4 trillion from global GDP by 2030 (Sardar et al., 2026; Scaglione et al., 2026; Bhattacharya et al., 2026). These are not abstractions confined to policy briefings; they translate, ward by ward, into treatment failures, prolonged hospital stays, and, in intensive care units especially, escalating rates of failed empirical therapy (Chines et al., 2026; Alatawi et al., 2025).

What makes this crisis so stubborn is not any single cause but a tangle of them. Bacteria adapt genetically with a speed that human institutions rarely match, and that biological agility is compounded, quite directly, by how antimicrobials are used, and overused, in clinical medicine, animal husbandry, and agriculture (Branda & Scarpa, 2024; Hassan et al., 2026). Meanwhile, the commercial pipeline for new antibiotics has, for lack of a better word, stalled (Nagre et al., 2025). Traditional drug-discovery models, built around slow, capital-intensive, largely trial-and-error experimentation, were never designed to keep pace with horizontal gene transfer and rapid mutation, and it shows (Sanapalli et al., 2026; Branda & Scarpa, 2024). The result is a widening gap between the rate at which resistance emerges and the rate at which new therapeutic options arrive, one that conventional approaches alone seem unlikely to close (Salama et al., 2026; Mohammed et al., 2025).

It is against this backdrop, somewhat unavoidably, that artificial intelligence and machine learning have entered the conversation, not as a silver bullet, but as a genuinely different way of working. Their arrival marks something closer to a paradigm shift than an incremental upgrade: microbiology is moving from a largely descriptive discipline toward one that is predictive and data-driven (Salama et al., 2026; Chines et al., 2026). By drawing on high-throughput multi-omics data, clinical metadata, and increasingly sophisticated computational architectures, AI-driven workflows now offer capabilities that would have seemed implausible a decade ago: early detection of resistance markers, optimization of prescribing decisions, and de novo design of candidate molecules (Branda & Scarpa, 2024; Scaglione et al., 2026; Sanapalli et al., 2026). When patient history, genomic data, and epidemiological patterns are woven together, AI-based diagnostics can, at least in principle, sidestep some of the longstanding limitations of conventional testing, false negatives, unacceptable delays, and can do so even in low-resource settings where molecular infrastructure is thin on the ground (Ahmed & Alghamdi, 2026; Hassan et al., 2026).

Two threads of progress deserve particular attention, and this review follows both. The first concerns diagnostics. Conventional culture-based antimicrobial susceptibility testing (AST) still requires 36 to 72 hours in most settings, a delay long enough to force clinicians into empirical, broad-spectrum prescribing that, ironically, intensifies the very selective pressures driving resistance in the first place (Aggarwal et al., 2026; Scaglione et al., 2026). AI-powered platforms are beginning to unsettle this status quo, offering scalable, rapid resistance prediction across both genotypic and phenotypic modalities (Khangarot et al., 2026). On the genomic side, whole-genome sequencing and metagenomic next-generation sequencing, paired with machine learning, now bypass slow culture steps almost entirely, capturing resistance determinants that classical alignment-based tools such as BLAST would likely miss (Sardar et al., 2026; Scaglione et al., 2026; Khangarot et al., 2026; Bhattacharya et al., 2026). On the phenotypic side, MALDI-TOF mass spectrometry and Raman spectroscopy, already embedded in many clinical laboratories, have been repurposed through machine learning to classify resistance phenotypes within minutes rather than days, and when coupled with microfluidics, some systems now deliver minimum inhibitory concentration estimates in under two to four hours (Aggarwal et al., 2026; Branda & Scarpa, 2024; Liao et al., 2025; M. S. Rahman & Wakeman, 2026).

The second thread concerns discovery. Where traditional screening of physical compound libraries is slow and expensive almost by design, AI-driven virtual screening explores vast in silico chemical spaces instead, predicting binding affinities, pharmacokinetic properties, and even resistance-development potential before a single molecule is synthesized (Sanapalli et al., 2026; Branda & Scarpa, 2024; Abo-Tbeak & AL-Hilali, 2026). The discovery of Halicin, identified through a deep neural network trained on roughly 2,500 molecules and later shown to disrupt bacterial electrochemical gradients through a genuinely novel mechanism, remains perhaps the most cited proof of concept (Stokes et al., 2020; Nagre et al., 2025; Sanapalli et al., 2026). Abaucin, a narrow-spectrum antibiotic targeting multidrug-resistant Acinetobacter baumannii, followed a similar computational path (Sanapalli et al., 2026; Chines et al., 2026). Generative frameworks, variational autoencoders, generative adversarial networks, diffusion models, have since extended this logic to antimicrobial peptides, using protein language models such as ESM-1b and ESM-2 to design candidates optimized for potency and low toxicity, several of which have shown validated efficacy in murine models (Sardar et al., 2026; Maldonado-Hernández et al., 2026; Sanapalli et al., 2026).

And yet, for all this technical momentum, a substantial gap persists between what these models can do in silico and what they have actually been shown to do at the bedside. This review, then, is organized around that tension. We synthesize the technical progress achieved in AI-driven diagnostics and drug discovery, examine the methodological and regulatory bottlenecks that continue to constrain clinical translation, retrospective validation biases, data scarcity and inequity, black-box interpretability concerns, and the absence, to date, of any regulatory clearance for an AI-based AMR tool, and propose a structured framework of research questions and objectives to guide the analysis that follows (Sardar et al., 2026; Alatawi et al., 2025).

2. AI-Driven Advances Against Antimicrobial Resistance

2.1 The Clinical Necessity for AI-Driven Diagnostic Stewardship

Consider the situation a clinician actually faces at the bedside when a patient presents with suspected sepsis: the decision to treat cannot wait for certainty, yet certainty is precisely what culture-based diagnostics are slow to provide. This mismatch, between clinical urgency and diagnostic latency, is not a minor inconvenience; it is arguably the central driver of inappropriate empirical antibiotic use, and the consequences compound quickly in critical care settings where delay can be fatal (Alatawi et al., 2025). Traditional microbiological stewardship tries to optimize antibiotic selection under this pressure, but it remains fundamentally hostage to culture-dependent processing times (Zakhour et al., 2023). Rapid molecular assays, multiplex PCR chief among them, can detect specific resistance genes such as blaKPC or blaNDM directly from positive blood cultures within a few hours, which helps, but only up to a point: these assays are restricted to known target panels and cannot anticipate novel resistance mutations, nor can they supply minimum inhibitory concentration values (Alatawi et al., 2025; Scaglione et al., 2026).

What AI-driven diagnostics offer, and this is worth stating plainly, is a way of decoupling resistance prediction from the slow biology of cell division itself (Liao et al., 2025). By training classifiers on complex, high-dimensional biophysical signatures, these systems can infer resistance phenotypes directly from raw or early-incubation clinical specimens, before conventional culture would even yield a readable result (Aggarwal et al., 2026). In effect, diagnostics shift from a retrospective, verification-oriented exercise to something closer to proactive forecasting (Ahmed & Alghamdi, 2026; Hudu et al., 2025). The clinical upside of this shift is a genuinely "precision-first" prescribing model, one in which the empirical broad-spectrum window is minimized, or in the best cases, bypassed altogether, protecting both the individual patient and the broader therapeutic arsenal on which everyone else still depends (Abdul Ghafur et al., 2026; Alatawi et al., 2025).

2.2 Accelerating Phenotypic Susceptibility Testing

Among the various AI applications surveyed here, the automated analysis of routinely generated hospital diagnostic data stands out as perhaps the most clinically mature. MALDI-TOF mass spectrometry has, for some years now, been a cornerstone of rapid bacterial identification in clinical laboratories, yet its potential for direct susceptibility testing was, until fairly recently, left largely untapped (Aggarwal et al., 2026; Alatawi et al., 2025), a progression summarized across omics categories in Table 4. That is changing. Machine learning models, spanning random forests, gradient-boosted trees, and deep convolutional neural networks, can now parse subtle peaks within high-dimensional protein mass spectra or lipid profiles to classify resistance phenotypes within minutes of colony isolation (Aggarwal et al., 2026). In one representative example, XGBoost and artificial neural network models successfully distinguished carbapenem-resistant from carbapenem-susceptible Klebsiella pneumoniae strains using MALDI-TOF spectral features, reaching AUROC values above 0.90 (Aggarwal et al., 2026; Scaglione et al., 2026).

A parallel, and arguably complementary, approach has emerged around single-cell Raman spectroscopy paired with deep learning, a genuinely culture-free phenotypic AST modality (Liao et al., 2025; Scaglione et al., 2026). Raman spectroscopy captures the vibrational fingerprint of individual bacterial cells, a fingerprint that shifts, measurably, within one to two hours of antibiotic exposure as metabolic states change (Liao et al., 2025). Deep convolutional networks trained to read these raw spectral signatures can flag drug-induced metabolic alterations and deliver susceptibility calls at over 90% accuracy (Liao et al., 2025; Scaglione et al., 2026). When such spectral classification pipelines are further integrated with microfluidics and automated single-cell imaging, something remarkable happens to the timeline: phenotypic AST, ordinarily a 24- to 48-hour affair, compresses to under two to four hours (Liao et al., 2025). These platforms track cell-division dynamics, morphological change, or laser speckle patterns in real time, calculating precise MIC values directly from clinical samples at the point of care (Liao et al., 2025; Rahman & Wakeman, 2026).

2.3 Metagenomics and Multi-Omics Machine Learning Pathways

Phenotypic testing optimizes the immediate clinical response, but it does not, by itself, explain why resistance occurred. That deeper mechanistic picture is where genotypic diagnostics, powered by next-generation sequencing and metagenomics, earn their keep (Sardar et al., 2026; Scaglione et al., 2026). For a long time, genotypic AMR prediction leaned almost entirely on rule-based homology matching against curated databases such as CARD and ResFinder (Sardar et al., 2026). These tools are accurate, admittedly, for known resistance markers, but they are essentially blind to novel mutations, promoter modifications, or the complex epistatic interactions that can quietly modulate resistance phenotypes in ways alignment alone cannot capture (Sardar et al., 2026; Chines et al., 2026).

AI-driven genomic modeling gets around this by reframing AMR prediction as a sequence-based machine learning task rather than a lookup problem. Architectures such as Bidirectional Long Short-Term Memory networks, transformers like DNABERT, and graph neural networks can process raw genomic sequences or k-mer frequency matrices to surface novel resistance-associated motifs and single-nucleotide polymorphisms, without needing prior gene assembly at all (Sardar et al., 2026; Chines et al., 2026). These models build high-dimensional, species-aware embeddings that capture long-range genomic dependencies, which in turn allows them to classify divergent ARG subfamilies that would otherwise show too little sequence homology to known databases to be caught (Sardar et al., 2026; Khangarot et al., 2026), a pattern consistent with the genomics-focused model comparisons summarized in Table 1.

Still, genomics alone tends to fall short of true precision, if only because the mere presence of a resistance gene says nothing definitive about whether it is actually being transcribed or translated (Rahman & Wakeman, 2026). This is prompting a gradual, but accelerating, shift toward multimodal models that fuse genomic features with transcriptomic, proteomic, metabolomic, and lipidomic data (Rahman & Wakeman, 2026; Chines et al., 2026). Deep neural networks equipped with cross-attention layers can, when fed this multi-scale data, trace the flow of biological information from genotype through to active phenotype (Sardar et al., 2026). Integrating whole-genome sequencing with transcriptomic RNA-seq expression data, for instance, has enabled markedly accurate MIC predictions in Acinetobacter baumannii and Pseudomonas aeruginosa, revealing active efflux pump overexpression and target-site modifications that genomic sequencing alone would have missed entirely (Chines et al., 2026; Rahman & Wakeman, 2026).

2.4 Clinical Decision Support Systems and Dynamic Prescribing

None of this predictive power matters much, of course, unless it reaches the clinician at the point of decision. That translation happens, largely, through Clinical Decision Support Systems embedded within institutional electronic health records (Abdul Ghafur et al., 2026; Alatawi et al., 2025). Rather than dumping raw microbiological data on already-stretched clinicians, AI-CDSS platforms synthesize heterogeneous inputs, demographics, comorbidities, prior antibiotic exposure, local antibiogram trends, real-time labs, into personalized, narrow-spectrum regimen recommendations (Abdul Ghafur et al., 2026; Mohammed et al., 2025). The "AIME" platform deployed in the Netherlands offers a concrete illustration: it continuously analyzes patient clinical streams and laboratory results to optimize antibiotic selection and encourage earlier de-escalation (Mohammed et al., 2025).

Natural language processing has quietly become a critical enabler of these systems, and it is worth pausing on why. Much of the clinical context that matters, allergy histories, evolving symptoms, radiology impressions, sits locked inside unstructured clinician notes rather than structured fields (Abdul Ghafur et al., 2026). Large language models and clinical chatbots can extract this narrative information and feed it into dynamically updated CDSS risk scores, summarizing guidelines and refining drug regimens along the way (Antonie et al., 2025; Abdul Ghafur et al., 2026; Mohammed et al., 2025). Separately, machine learning is increasingly applied to Model-Informed Precision Dosing for narrow-therapeutic-index agents such as vancomycin, aminoglycosides, and colistin (Branda & Scarpa, 2024; Rahman & Wakeman, 2026). By combining pharmacokinetic and pharmacodynamic principles with patient-specific biomarkers, renal function and inflammatory markers among them, AI-MIPD models can recommend dosing titrations that aim to maximize bacterial clearance while limiting systemic toxicity (Branda & Scarpa, 2024).

2.5 Upstream Infection Prevention, Control, and One Health Biosurveillance

Reducing the incidence of healthcare-associated infections in the first place is, in a sense, the most efficient form of stewardship there is, since it removes the selective pressure before it ever builds. This makes upstream Infection Prevention and Control a crucial, if sometimes underappreciated, coordinate of AI-augmented stewardship (Abdul Ghafur et al., 2026). In modern hospitals, computer vision systems now track hand-hygiene compliance among staff, functioning as nudge-based, continuous quality-improvement tools that measurably reduce cross-transmission of multidrug-resistant organisms (Abdul Ghafur et al., 2026; Bhattacharjee & Bhattacharya, 2025). At the genomic level, pairing whole-genome sequencing surveillance with machine learning analysis of EHR metadata allows near-real-time detection of healthcare-associated outbreaks, reconstructing transmission networks well enough to flag rising clusters and trace pathways across wards before clonal spread takes hold (Abdul Ghafur et al., 2026; Sundermann et al., 2022).

Beyond hospital walls, AI is nudging epidemiological surveillance away from passive, retrospective reporting toward something more anticipatory, active biosurveillance framed within a "One Health" model (Abdul Ghafur et al., 2026). By integrating wastewater metagenomic sequencing with clinical, agricultural, and environmental data streams, One Health AI platforms can monitor cross-sectoral ARG transmission across the human-animal-environment interface, tracing, for instance, how antibiotic use in livestock feeds back into human health outcomes (Bhattacharya et al., 2026; Abdul Ghafur et al., 2026; Alatawi et al., 2025). A broader scoping review of AI across ecosystem-level applications reinforces this potential, mapping how machine learning connects environmental and human health signals under the One Health paradigm (Kasse et al., 2025). Perhaps most strikingly, models trained on global sewage resistome profiles have been shown to identify geographic resistance hotspots and forecast localized surges up to six months before they appear in clinical isolates (Abdul Ghafur et al., 2026; Scaglione et al., 2026), a lead time that, if acted upon, would let public health authorities adjust prescribing guidelines and mount containment campaigns well ahead of the curve (Abdul Ghafur et al., 2026).

2.6 AI-Enabled De Novo Antimicrobial Agent and Target Discovery

Diagnostics and stewardship, however well executed, are only as useful as the therapeutic options they are meant to guide, and that is precisely where the traditional pipeline has faltered. With small-molecule antibiotic discovery facing both scientific and economic stagnation, AI has stepped in as something of a disruptive accelerator for target identification and drug design (Abdulrazaq et al., 2025; Sanapalli et al., 2026). Where target discovery once meant painstaking, largely random phenotypic screening, next-generation frameworks now combine transcriptomics, structural biology, and deep learning to model host-pathogen-drug interactions at a systems level (Chines et al., 2026). AI-driven networks can analyze bacterial transcriptomic shifts under environmental stress to prioritize conserved, essential targets in ESKAPE pathogens, outer membrane proteins like OmpW in A. baumannii, regulatory proteins like AmpR in P. aeruginosa, that can then be pursued with novel compound scaffolds (Chines et al., 2026; Nagre et al., 2025; Rahman & Wakeman, 2026). Some models now span the entire pipeline, from raw data processing through to predictive modeling of microbial natural products (Abo-Tbeak & AL-Hilali, 2026).

In parallel, and arguably more strikingly, deep generative models are reshaping antimicrobial peptide design (Sanapalli et al., 2026; Wu-Mo et al., 2026). Natural-source peptide discovery is slow and often yields molecules with unacceptable hemolytic toxicity or poor proteolytic stability, problems that AI-driven discovery, structured around two complementary phases, attempts to sidestep (Sanapalli et al., 2026). In the discriminative phase, deep neural networks such as AMPs-Net act as classifiers, filtering millions of candidate sequences from genomic, metagenomic, and synthetic libraries down to a much smaller set with projected potency and low host toxicity (Sanapalli et al., 2026; Wu-Mo et al., 2026). In the generative phase, architectures including variational autoencoders, generative adversarial networks, and large language models learn the underlying "grammar" of functional peptides well enough to generate genuinely novel, non-natural architectures optimized for charge distribution and hydrophobicity, candidates later confirmed through in vitro membrane-disruption assays and in vivo murine models (Sardar et al., 2026; Sanapalli et al., 2026).

This computational momentum extends, too, into less conventional therapeutic territory, engineered phages, endolysins, antimicrobial polymers (Abdulrazaq et al., 2025; Sanapalli et al., 2026). By mining the genomic "dark matter" of uncharacterized bacteriophages, AI models can identify novel endolysin sequences, enzymes that digest bacterial cell walls, and optimize their stability and catalytic activity (Sanapalli et al., 2026). These AI-designed molecules bypass classical resistance mechanisms altogether, representing what may be one of the more promising frontiers in the fight against pan-resistant ESKAPE pathogens (Abdulrazaq et al., 2025; Chines et al., 2026).

2.7 Translational Obstacles, Model Decay, and the Explainable AI Imperative

For all this technical sophistication, a considerable gap remains between what these models can do in silico and what they have actually demonstrated in clinical use. The overwhelming majority of published AI-AMR models are retrospective proof-of-concept studies, and, as of this writing, no AI-driven AMR diagnostic or CDSS tool has secured formal regulatory clearance anywhere (Sardar et al., 2026). Three interlocking challenges seem to explain most of this delay: data heterogeneity, model drift, and interpretability (Sardar et al., 2026; Chines et al., 2026).

Data heterogeneity comes first, and it is not a subtle problem. Biological and pharmacological datasets used to train these models are heavily skewed toward high-income countries, which limits generalizability precisely where the burden of resistance is often highest, in low- and middle-income countries where baseline strain distributions, resistance patterns, and clinical guidelines can differ substantially (Sardar et al., 2026; Scaglione et al., 2026). Model drift follows close behind: pathogen populations evolve continuously under selective pressure, and static models pay a real price for that evolution over time (Sardar et al., 2026). Documented AUROC declines of 0.10 to 0.25 within eighteen months of deployment are not uncommon, a decay rate that all but demands continuous retraining pipelines and proactive drift-detection protocols if these tools are to remain trustworthy in practice (Sardar et al., 2026).

Interpretability is, arguably, the thorniest of the three. Clinicians remain ethically and legally accountable for patient outcomes, and it is entirely reasonable that they hesitate to follow prescribing or dosing recommendations generated by models offering no visible mechanistic reasoning (Sardar et al., 2026; Chines et al., 2026). Building genuine clinical trust, then, seems to require Explainable AI as something closer to a strategic necessity than an optional add-on (Chines et al., 2026; Sardar et al., 2026); Table 3 catalogues the principal interpretability methods, and their respective trade-offs, that have been applied to this problem. Feature attribution methods, SHAP and LIME chief among them, help decompose complex model predictions into intelligible, patient-specific contributions (Sardar et al., 2026). In genomic AMR prediction specifically, XAI can point directly to which mutations, amino acid substitutions in GyrA or ParC, say, or which promoter modifications are driving a predicted fluoroquinolone-resistance call (Sardar et al., 2026; Chines et al., 2026). That kind of transparency lets clinicians tie algorithmic output back to established biological mechanisms, which is, ultimately, what makes an AI-driven recommendation clinically auditable, logically defensible, and safely aligned with patient care (Sardar et al., 2026; Chines et al., 2026).

3. Methods

3.1 Study Design and Reporting Framework

This work was conducted as a narrative-systematic review, combining the breadth of a narrative synthesis with the transparency and reproducibility expected of a systematic search process. The review was structured, insofar as a narrative synthesis allows, around the reporting principles articulated in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) statement, so that a reader wishing to reproduce or extend the search could, in principle, do so from the description below. No formal protocol was pre-registered, given the review's hybrid narrative-analytical scope, but the search strategy, eligibility criteria, and data-extraction fields were fixed in advance of full-text screening to limit post hoc selection bias.

3.2 Information Sources and Search Strategy

We searched PubMed/MEDLINE, Scopus, Web of Science, and the Cochrane Library for records published, largely, within a recent multi-year window through mid-2026, supplemented by manual citation-chasing of key reviews and hand-searching of high-impact venues (Antibiotics, Briefings in Bioinformatics, Frontiers in Public Health, Frontiers in Microbiology, and Cell). Search strings combined controlled vocabulary (MeSH terms such as "Drug Resistance, Microbial" and "Artificial Intelligence") with free-text keywords using Boolean operators, structured broadly as: ("antimicrobial resistance" OR "antibiotic resistance" OR "drug resistance, bacterial") AND ("artificial intelligence" OR "machine learning" OR "deep learning" OR "neural network*") AND (diagnos* OR "drug discovery" OR "susceptibility testing" OR genomic* OR "clinical decision support"). Filters were applied for English-language records where feasible, and reference lists of eligible articles were screened for additional relevant studies not captured by the primary search.

3.3 Eligibility Criteria

Studies were considered eligible if they (a) applied a machine learning or deep learning method to any stage of AMR diagnostics, resistome prediction, antimicrobial or antimicrobial peptide discovery, or AI-enabled clinical decision support; (b) reported at least one quantifiable performance metric (e.g., accuracy, AUROC, sensitivity, specificity, precision, or minimum inhibitory concentration accuracy) or a clearly described discovery outcome; and (c) were published as peer-reviewed articles, systematic or narrative reviews, or credible preprints with sufficient methodological detail to permit critical appraisal. Studies were excluded if they addressed AI applications unrelated to antimicrobial resistance, lacked any empirical or quantitative outcome, or were editorials, conference abstracts without full data, or non-English publications for which translation could not be reliably verified.

3.4 Study Selection and Data Extraction

Titles and abstracts were screened independently against the eligibility criteria described above, with full-text review conducted for any record where relevance could not be determined from the abstract alone. For each included study, we extracted, where reported: pathogen species or genus studied, diagnostic or discovery modality (e.g., whole-genome sequencing, MALDI-TOF, Raman spectroscopy, virtual screening), underlying AI/ML architecture, feature representation strategy, dataset or sample size, validation scheme (internal cross-validation, external holdout, or prospective clinical validation), and reported performance metrics. These extracted fields form the basis of the synthesis tables presented in the Results section.

3.5 Data Synthesis Approach

Given the substantial heterogeneity in outcome metrics, pathogen types, and validation designs across the included literature, a formal meta-analysis was judged inappropriate; a narrative, thematically organized synthesis was used instead. Findings were grouped into four analytical domains that emerged, fairly naturally, from the literature itself: rapid phenotypic diagnostics, genomic and metagenomic resistome prediction, explainable AI and model interpretability, and de novo antimicrobial discovery. Within each domain, we prioritized studies reporting external or prospective validation over those relying solely on internal cross-validation, and we explicitly noted, wherever apparent, indicators of retrospective bias, dataset imbalance, or limited generalizability, consistent with recommended practice for critically appraising AI-based diagnostic and prognostic research.

3.6 Quality and Risk-of-Bias Considerations

Because included studies spanned diagnostic accuracy research, computational drug-discovery pipelines, and health-services implementation reports, no single standardized risk-of-bias instrument was uniformly applicable. Instead, we assessed each study qualitatively against domains adapted from the PROBAST (Prediction model Risk Of Bias ASsessment Tool) framework, participant/sample selection, predictor and outcome definition, and analysis methodology, flagging studies reliant exclusively on internal resampling, small single-center cohorts, or unreported external validation as carrying a higher risk of overestimated performance. This appraisal directly informed the translational-gap analysis presented in the Discussion.

4. Results

4.0 Overview of the Empirical Landscape

Taken as a whole, the convergence of high-throughput biotechnology, digital pathology, and machine learning has, over a relatively short span of years, established a genuinely robust empirical baseline for transforming AMR management (Sardar et al., 2026). Moving away from classical, observation-based approaches, next-generation AI pipelines show clear, reproducible progress across four fronts: compressing diagnostic timelines, predicting genomic resistance phenotypes, offering biological explainability, and executing de novo molecular discovery (Chines et al., 2026; Liao et al., 2025). Tables 1 through 4 summarize the underlying study-level evidence referenced throughout this section, spanning model performance for resistance detection (Table 1), computational drug-discovery platforms (Table 2), explainable AI methods (Table 3), and multi-omics diagnostic paradigms (Table 4). What follows synthesizes the empirical evidence documented across the reviewed literature, organized around these same four dimensions of clinical translation.

4.1 AI-Driven Rapid Phenotypic Diagnostics and Digital Microbiology

The clinical utility of any diagnostic stewardship program is, ultimately, bounded by its turnaround time, and in critical care, that bound has real mortality consequences: delayed appropriate antibiotic therapy against ESKAPE pathogens raises mortality risk by approximately 7.6% per hour (Sardar et al., 2026). AI is chipping away at this constraint by bypassing the slow biological process of overnight cell division altogether (Liao et al., 2025), and Figure 1 illustrates just how far that compression now extends relative to conventional culture-based testing (Figure 1); Table 4 situates these phenotypic gains within the broader multi-omics diagnostic landscape.

One notable line of empirical progress concerns the automation of Gram-stain interpretation and colony morphology tracking, tasks that, performed manually, are highly operator-dependent and prone to fatigue-related error (Hassan & Zughaier, 2026). Deep convolutional neural networks trained on roughly 5,000 images achieved bacterial classification accuracies of up to 94.0% (Khangarot et al., 2026), and Vision Transformers applied to the same data pushed performance further still, reaching an AUC of 0.952, a gain attributable, it seems, to their capacity for capturing global contextual patterns that local convolutional kernels tend to miss (Khangarot et al., 2026). Swin Transformers applied to more complex colony-morphology datasets (4,200 images spanning over ten morphotypes) produced an 8.0% to 10.0% accuracy improvement over standard CNN baselines under strict external validation, a result the authors attribute to more robust shape- and edge-texture feature extraction (Khangarot et al., 2026).

Beyond image-based analysis, AI has repurposed routinely generated diagnostic data streams, MALDI-TOF mass spectrometry and single-cell Raman spectroscopy chief among them, into genuinely fast resistance-classification systems (Aggarwal et al., 2026). MALDI-TOF peak-intensity classifiers, paired with artificial neural networks or gradient-boosted trees, discriminate carbapenem-resistant from carbapenem-susceptible Klebsiella pneumoniae isolates with AUROC values exceeding 0.85 to 0.90, within minutes of colony isolation (Liao et al., 2025; Scaglione et al., 2026). Single-cell Raman spectroscopy, meanwhile, captures dynamic vibrational shifts within one to two hours of antibiotic exposure, reflecting metabolic perturbations well before macroscopic growth becomes visible (Liao et al., 2025). Deep CNNs processing these high-dimensional spectral signatures predict phenotypic susceptibility directly from pre-incubation or flagged blood cultures at accuracies exceeding 90.0%, compressing what was once a 48- to 72-hour susceptibility profile into a point-of-care result available in under two to four hours (Liao et al., 2025; Scaglione et al., 2026), a maturity level classified as an active-influence deployment stage in Table 4.

4.2 Genomic and Metagenomic Predictions of the Resistome

Phenotypic assays remain, in most laboratories, the gold standard for susceptibility testing, but genotypic modeling through next-generation sequencing offers something phenotypic testing cannot: a comprehensive survey of the molecular resistome itself (Sardar et al., 2026). The shift from rule-based alignment (BLAST-style homology matching against CARD or ResFinder) toward deep sequence representation learning has, empirically, expanded this predictive capacity considerably (Khangarot et al., 2026), as reflected across the genomics-based classifiers summarized in Table 1.

Classical supervised models, Random Forests and Support Vector Machines trained on manually engineered k-mer frequency matrices, performed acceptably but proved fairly vulnerable to sequence-level noise, assembly errors, and poor generalization across divergent strain lineages (Sardar et al., 2026). Species-aware nucleotide language models such as DNABERT have since addressed much of this fragility. Through self-supervised pretraining on large unannotated genomic corpora, DNABERT captures long-range genomic dependencies and epistatic interactions that k-mer-based models simply cannot see (Khangarot et al., 2026). In head-to-head comparisons, DNABERT outperformed Random Forest models by 15.0% in species-level differentiation and achieved a 12.0% to 18.0% absolute improvement in classification precision and recall over BiLSTM networks across 500 diverse bacterial genome datasets (Khangarot et al., 2026), a gap wide enough to suggest a genuine architectural advantage rather than incidental variation (Figure 2), a pattern broadly consistent with the pan-genome and k-mer-based classifiers reported in Table 1.

Metagenomic ARG detection presents its own distinct bottleneck: identifying highly divergent resistance genes directly from fragmented clinical or environmental reads, where standard best-hit homology matching tends to produce a troubling rate of false negatives for novel variants (Sardar et al., 2026). Treating ARG detection as a multi-label classification problem, DeepARG, trained on over 50,000 reference sequences using deep convolutional architectures, achieved classification precisions exceeding 0.97 across 30 major antibiotic classes, though its performance dropped noticeably when predicting rare ARG categories with sparse database representation (Khangarot et al., 2026). The HMD-ARG framework, a hierarchical convolutional network paired with attention mechanisms, was built specifically to address this class-imbalance weakness, and on a dataset of 60,000 genomic and metagenomic contigs under rigorous external validation, it delivered a 10.0% to 15.0% precision-and-recall improvement over DeepARG, successfully separating closely overlapping subclass designations that DeepARG tended to conflate (Khangarot et al., 2026).

Genotype alone, however, is rarely the whole story, since gene presence does not guarantee functional expression (Rahman & Wakeman, 2026). Multimodal neural networks integrating genomic markers with transcriptomic RNA-seq data and clinical EHR metadata have begun to close that gap (Chines et al., 2026; Khangarot et al., 2026): modeling host-pathogen-drug dynamics at a systems level across 10,000 patients, multimodal transformers achieved AUROC values of 0.90 to 0.92 in predicting multidrug-resistant clinical outcomes, a meaningful improvement over genomics-only classifiers (Khangarot et al., 2026).

4.3 Explainable AI and Model Interpretability in Clinical Workflows

The adoption of high-capacity deep learning models in infectious disease medicine remains, in practice, severely constrained by their opacity (Sardar et al., 2026). Clinicians, who carry legal and ethical accountability for prescribing decisions, are understandably reluctant to act on recommendations derived from computational steps they cannot inspect (Sardar et al., 2026). Explainable AI, in this context, functions less as an optional enhancement than as a strategic bridge between raw model accuracy and trustworthy, actionable stewardship (Chines et al., 2026), and Table 3 summarizes the mathematical basis, clinical application, and inherent limitations of each method discussed below.

Feature attribution methods remain the primary tool for taking these complex models apart in an interpretable way (Sardar et al., 2026). LIME, a post-hoc local explanation technique, approximates the decision boundary of a deep network locally using a simple, interpretable linear surrogate (Sardar et al., 2026); it is genuinely useful for explaining patient-specific recommendations but suffers from instability and offers little global, dataset-wide insight (Sardar et al., 2026). SHAP, grounded mathematically in cooperative game theory, tends to fare better on both counts, providing consistent, globally stable feature-importance attributions by calculating each genomic marker's, taxon's, or spectral peak's marginal contribution to the final prediction (Sardar et al., 2026).

These methods have, encouragingly, connected algorithmic output to established microbiological mechanisms rather than producing explanations that merely sound plausible. In genomic resistance prediction, SHAP-guided regularization frameworks reliably highlight biologically confirmed mutations, single-nucleotide polymorphisms within the quinolone-resistance-determining regions of gyrA or parC, as the dominant drivers of predicted fluoroquinolone resistance (Khangarot et al., 2026). In high-throughput single-cell spectral assays, attention maps visualize the exact mass-to-charge ratios or vibrational wave numbers associated with cell-wall peptidoglycan remodeling, letting clinicians confirm that the model is responding to authentic drug-induced physiological change rather than background noise or batch artifacts (Sardar et al., 2026; Khangarot et al., 2026), consistent with the attention-based and gradient-based methods detailed in Table 3.

4.4 AI-Enabled De Novo Drug Discovery and Target Prioritization

The value of even the best diagnostic and genomic stewardship is ultimately capped by the therapeutic options available to act on it. To counter the stagnation of traditional small-molecule discovery, deep generative AI and structural biology models have become disruptive accelerators for both compound design and target identification (Abdulrazaq et al., 2025; Sanapalli et al., 2026).

At the molecular level, AI-driven virtual screening has already produced landmark results, several of which are catalogued alongside their computational architectures in Table 2.

Halicin, discovered using deep neural network activity-prediction classifiers trained on a library of FDA-approved and natural compounds, exhibits a genuinely novel mechanism of action, disrupting transmembrane proton motive forces, and has shown potent in vivo broad-spectrum efficacy in murine models against pan-resistant Acinetobacter baumannii and Clostridioides difficile (Sanapalli et al., 2026). Machine learning-guided virtual screening combined with molecular docking separately enabled the discovery of Abaucin, a highly selective, narrow-spectrum therapeutic that targets A. baumannii while sparing host commensal microbiota (Sanapalli et al., 2026).

In target discovery specifically, next-generation pipelines now draw on transformer-based structural prediction tools such as AlphaFold2 and RoseTTAFold to model high-resolution protein-ligand and host-pathogen interactions (Chines et al., 2026; Khangarot et al., 2026). Incorporating these high-accuracy 3D protein structures into molecular docking algorithms produced an 18.0% improvement in virtual hit-rates relative to traditional template-free approaches (Khangarot et al., 2026), a gain substantial enough to meaningfully change the economics of early-stage screening.

Deep generative architectures, variational autoencoders, generative adversarial networks, and conditional GANs, are simultaneously being deployed for the rational design of antimicrobial peptides targeting ESKAPE pathogens and Candida auris (Sanapalli et al., 2026; Wu-Mo et al., 2026). Where natural-source AMP discovery is hindered by high hemolytic toxicity and poor proteolytic stability (Sanapalli et al., 2026), generative models learn the latent grammar of peptide-membrane interactions directly, optimizing charge distribution, hydrophobicity, and amphipathicity in silico. The AMPGen platform illustrates this well: an evolutionary information-preserved generator pre-trained on UniClust30, paired with an XGBoost discriminator and an LSTM scorer, optimizes activity against specific multidrug-resistant strains (Wu-Mo et al., 2026). Across these generative pipelines, resulting peptides have achieved minimal inhibitory concentrations as low as 2 to 8 µg/mL in vitro, reduced bacterial burdens by 2.3 to 3.0 log10, and measurably reduced murine mortality in experimental sepsis models (Chines et al., 2026), findings that, taken together, suggest generative AMP design has moved well past a purely theoretical exercise, consistent with the discovery pipelines and outcomes summarized in Table 2.

5. Discussion

5.0 Bridging the Translational Chasm

Reading across the results assembled here, a somewhat paradoxical picture emerges. On one hand, the technical achievements are difficult to dismiss: diagnostic timelines compressed from days to hours (Figure 1), resistome prediction accuracies rivaling or exceeding classical alignment-based methods (Table 1), and generative pipelines that have produced antimicrobial peptides with confirmed in vivo activity (Table 2; Sanapalli et al., 2026; Chines et al., 2026). On the other hand, and this is the tension the field has yet to resolve, almost none of this evidence has been generated, or validated, under conditions that would satisfy a regulator, or, for that matter, a skeptical clinician standing at the bedside (Sardar et al., 2026). It is worth sitting with that gap for a moment rather than rushing past it.

5.1 The Persistence of Retrospective, Single-Center Validation

Much of the literature synthesized in this review, and this is a limitation the field appears to acknowledge openly, relies on internal cross-validation within homogeneous, single-center datasets (Aggarwal et al., 2026; Sardar et al., 2026). This is not a trivial methodological quibble; it inflates performance metrics in ways that dataset shift, temporal drift, and regional epidemiological variation can quietly unravel once a model is deployed elsewhere (Aggarwal et al., 2026; Chines et al., 2026), and the validation-scheme column in Table 1 makes plain just how few of the reviewed genomic classifiers were tested on truly external cohorts. The documented AUROC decline of 0.10 to 0.25 within eighteen months of deployment (Sardar et al., 2026) is, frankly, a sobering reminder that a model's reported accuracy at publication time is closer to a snapshot than a guarantee. Continuous retraining pipelines and formal drift-detection protocols (Figure 2) are not luxuries here; they seem closer to prerequisites for any AI-AMR tool intended for sustained clinical use.

5.2 Data Equity and the Geography of Resistance

A related, and arguably more troubling, concern is geographic. Global AMR databases skew heavily toward high-income, Western settings, leaving precisely the regions carrying the greatest resistance burden, sub-Saharan Africa and parts of Latin America among them, severely underrepresented in training data (Aggarwal et al., 2026; Sardar et al., 2026). This is not merely a fairness issue in the abstract; it has direct clinical consequences, since a model trained predominantly on Western strain distributions may perform poorly, or worse, confidently and incorrectly, when applied to pathogen populations it has effectively never seen (Sardar et al., 2026; Scaglione et al., 2026). Inconsistent breakpoint definitions across EUCAST and CLSI standards, along with a general lack of standardized wet-lab annotation, compound this problem further, introducing noise that even the most sophisticated architecture cannot fully compensate for (Sardar et al., 2026; Liao et al., 2025; Branda & Scarpa, 2024; Alatawi et al., 2025).

5.3 The Interpretability Imperative

The black-box nature of deep learning architectures constitutes, in our reading of this literature, the single most consequential barrier to clinical adoption (Sardar et al., 2026; Branda & Scarpa, 2024). Given that delayed or incorrect antibiotic administration can raise septic shock mortality by approximately 7.6% per hour (Sardar et al., 2026; Alatawi et al., 2025), it is difficult to fault clinicians for hesitating to trust recommendations they cannot interrogate. The XAI methods reviewed here, SHAP, attention maps, feature-weighting approaches, and summarized comparatively in Table 3, offer a genuinely promising path forward, having already demonstrated the ability to map model predictions onto biologically confirmed mechanisms such as gyrA and parC mutations in fluoroquinolone resistance (Khangarot et al., 2026; Sardar et al., 2026; Chines et al., 2026). What remains missing, though, is standardization: no consensus yet exists on which XAI method, or combination of methods, should be required for regulatory submission, and this ambiguity likely contributes to the field's current stall at the pre-clearance stage (Sardar et al., 2026).

5.4 Regulatory and Infrastructural Bottlenecks

Perhaps the starkest fact to emerge from this synthesis is this: despite everything described above, no AI-based AMR tool has, to date, received regulatory clearance from a body such as the FDA (Sardar et al., 2026). Existing regulatory pathways were built for static software, and they are not well suited to evaluating continuously adaptive machine learning models, tools whose entire value proposition rests on their capacity to keep learning as resistance patterns shift (Sardar et al., 2026; Alatawi et al., 2025). Integrating these tools into existing hospital IT infrastructure and electronic health record systems introduces further friction, interoperability challenges, compatibility issues, and data-privacy obligations under frameworks like GDPR and HIPAA that were, again, not designed with adaptive AI in mind (Alatawi et al., 2025).

5.5 Toward a Standardized Roadmap for Clinical Translation

None of this, it should be said, amounts to a reason for pessimism, so much as a reasonably clear list of what needs to happen next. Federated learning offers one plausible route around the data-scarcity and privacy problems simultaneously, allowing multi-institutional model training without centralizing sensitive patient data, an approach that could, in principle, address both the geographic representation gap and HIPAA/GDPR compliance concerns in a single stroke. One Health-linked surveillance networks, similarly, could extend the six-month early-warning capacity already demonstrated in wastewater resistome monitoring (Abdul Ghafur et al., 2026; Scaglione et al., 2026), and classified as an active-influence surveillance modality in Table 4, into a genuinely global, continuously updated resistance forecasting system. And standardized XAI reporting requirements, akin to what CONSORT-AI and related frameworks have begun to establish for other clinical AI domains, would give regulators a consistent basis for evaluating whether a model's reasoning is auditable enough to trust. Taken together, these three pillars, federated data-sharing, One Health surveillance integration, and standardized interpretability reporting, sketch out what a realistic path from retrospective proof-of-concept to prospective clinical deployment might actually look like.

5.6 Limitations of This Review

This synthesis is, admittedly, not without its own limitations. As a narrative-systematic review rather than a formal meta-analysis, it cannot statistically pool effect sizes across the heterogeneous outcome metrics reported in the underlying literature, and the reliance on published, largely English-language sources may itself introduce a degree of publication and language bias, an irony not lost on us, given that data inequity is one of the central concerns raised throughout this discussion. Future prospective, multi-site validation studies, ideally registered and reported according to emerging AI-specific reporting standards, will be needed to confirm whether the performance gains documented here in fact translate to real-world, external clinical settings.

6. Conclusion

Taken together, the evidence assembled here suggests that AI has already done what it was first asked to do in the AMR space: it has shown, repeatedly and across modalities, that resistance can be predicted, diagnosed, and in some cases outrun computationally. What it has not yet done is prove itself at the bedside, across populations, or under regulatory scrutiny. Closing that gap will likely depend less on more powerful architectures than on unglamorous work, prospective multi-site trials, harmonized data standards, federated learning across institutions, and explainability frameworks that clinicians can actually act on. If those pieces come together, and there is reason for cautious optimism that they will, AI-AMR tools may finally cross from promising literature into the diagnostic workflows and formularies where they are needed most.

References