AMR represents one of the most pressing global public health crises of the 21st century. The burden of AMR is particularly severe for neglected and poverty-associated infectious diseases, where delayed diagnosis, limited access to susceptibility testing, and fragmented healthcare infrastructures exacerbate treatment failure and transmission. Recent estimates indicate that AMR was associated with approximately 4.95 million deaths worldwide, with 1.27 million deaths directly attributable to infections caused by drug-resistant pathogens (Murray et al., 2022). A major contributor to this crisis is the continued reliance on conventional diagnostic workflows that fail to deliver timely and actionable information for clinical decision-making. Traditional diagnostic approaches including culture-based identification, phenotypic antimicrobial susceptibility testing (AST), and centralized nucleic acid amplification tests (NAATs) remain the clinical gold standard but are intrinsically constrained by prolonged turnaround times, typically ranging from 24 to 96 hours. They also demand specialized infrastructure and trained personnel, making them impractical for urgent or remote clinical scenarios (Walker et al., 2025). As a result, clinicians frequently initiate empirical broad-spectrum antimicrobial therapy, which, while life-saving in some contexts, significantly accelerates selective pressure and the emergence of resistant strains. Diagnostic uncertainty thus drives inappropriate prescribing and further spreads resistance. The impact of these diagnostic delays is particularly evident for high-burden pathogens such as Mycobacterium tuberculosis, Klebsiella pneumoniae, Pseudomonas aeruginosa, Neisseria gonorrhoeae, and Plasmodium falciparum. These organisms are increasingly associated with multidrug-resistant (MDR), extensively drug-resistant (XDR), and, in rare but alarming cases, pan-drug-resistant phenotypes, complicating treatment and increasing morbidity and mortality, as illustrated in Figure 1 (Alatawi et al., 2025; Roy et al., 2025).

Point-of-care resistance diagnostics (POC-RD) fundamentally redefine how antimicrobial resistance is detected, interpreted, and acted upon by compressing the diagnostic therapeutic decision loop to the earliest stage of clinical contact. Unlike conventional laboratory workflows, which often generate resistance data only after empirical treatment has already been initiated, point-of-care resistance diagnostics (POC-RD) are designed to detect clinically actionable resistance determinants in near real time. These include rpoB and katG mutations in Mycobacterium tuberculosis; fluoroquinolone-associated gyrA variants; carbapenemase genes such as bla_NDM and bla_KPC; macrolide resistance mediated by erm genes; and molecular markers of antimalarial drug resistance. Depending on the diagnostic workflow and specimen type, results can be delivered within clinically relevant timeframes ranging from under one hour to several hours, enabling earlier optimization of therapy and infection control interventions. As illustrated in Figure 2, timely resistance-guided treatment facilitated by POC-RD contributes to reduced mortality and transmission of drug-resistant tuberculosis, HIV (antiretroviral resistance), and priority bacterial pathogens such as Escherichia coli, Klebsiella pneumoniae, and Staphylococcus aureus (Mani et al., 2001; Singh et al., 2021). By providing early resistance information, POC-RD allows clinicians to select the most effective therapy from the outset, reducing treatment failure and unnecessary drug exposure. The principal clinical value of POC-RD lies in its ability to support evidence-based prescribing decisions earlier in the care pathway. Early access to resistance information allows clinicians to select the most effective antimicrobial or direct-acting antiviral regimens from the outset, reducing treatment failure, shortening time to clinical stabilization, and limiting unnecessary drug exposure. In severe infections such as sepsis, drug-resistant tuberculosis, and invasive hospital-acquired infections, even short delays in appropriate therapy are associated with increased mortality and prolonged hospitalization (Oliveira et al., 1920). In severe infections such as sepsis, drug-resistant tuberculosis, and invasive hospital-acquired infections, delays in appropriate therapy are associated with increased mortality and prolonged hospitalization; earlier access to resistance information may therefore contribute to improved outcomes in selected high-risk contexts. By providing actionable resistance profiles in real time, POC-RD mitigates these risks and aligns clinical practice with evidence-based, pathogen-directed treatment principles (Kumar et al., 2024). At the population level, POC-RD plays a critical role in strengthening antimicrobial stewardship by curbing the routine use of broad-spectrum agents and preserving last-line therapies. By confirming resistance mechanisms on site, POC-RD enables rational de-escalation and limits the spread of resistant strains. This function is especially valuable in high-burden healthcare environments, where empirical prescribing is often driven by diagnostic uncertainty rather than microbiological evidence (Chigozie et al., 2025). Beyond individual patient management, resistance-aware POC diagnostics generate high-resolution, temporally relevant data that can bridge critical gaps in AMR surveillance, particularly in regions where laboratory reporting is sparse or delayed. POC-RD also offers substantial advantages for outbreak detection and containment. Portable and deployable resistance diagnostic systems enable rapid screening in hospitals, community settings, border regions, and field environments, facilitating early identification of resistant strains before widespread transmission occurs. Connected POC-RD devices can feed real-time data into decentralized surveillance networks, supporting local, regional, and national AMR monitoring (Kost, 2019). This capability is particularly important in low-resource and remote settings, where access to centralized laboratories is limited and resistance data are frequently underreported. Overall, the integration of POC-RD into clinical and public health workflows offers a promising pathway to earlier resistance awareness, improved stewardship practices, and enhanced surveillance, particularly for high-impact infectious diseases such as tuberculosis, HIV-associated infections, and bacterial sepsis (Heidt et al., 2020).

Cartridge-based nucleic acid amplification tests (NAATs) represent the most mature and widely deployed class of POC molecular diagnostics with integrated resistance detection. Among these, the Xpert MTB/XDR platform exemplifies how automated real-time PCR technology can be translated into robust, near-patient testing solutions for drug-resistant tuberculosis (TB) (Sahana et al., 2017). These systems employ fully enclosed, disposable cartridges that integrate sample preparation, nucleic acid extraction, amplification, and detection into a single workflow, minimizing hands-on time and biosafety risks. Following minimal sample processing, sputum specimens are loaded into cartridges that detect Mycobacterium tuberculosis complex DNA while simultaneously interrogating mutations in key resistance-associated genes, including rpoB (rifampicin resistance), katG and inhA (isoniazid resistance), and gyrA/gyrB (fluoroquinolone resistance) (Rozales et al., 2013; Park et al., 2018). The clinical impact of cartridge-based NAATs is largely driven by their rapid turnaround time, typically under two hours, which contrasts sharply with conventional culture-based diagnostics and phenotypic drug susceptibility testing (DST) that may require weeks to generate actionable results. Numerous multicenter evaluations have demonstrated high concordance between Xpert MTB/XDR results and gold-standard phenotypic DST or sequencing-based reference methods, with reported sensitivities and specificities frequently exceeding 90% for major resistance determinants (Anton-Vazquez et al., 2021). Early resistance detection facilitates earlier treatment optimization, supports transmission control efforts, and contributes to resistance surveillance. Beyond individual patient management, the public health value of these platforms lies in their capacity to rapidly identify drug-resistant TB at first presentation, thereby reducing delays to effective therapy, limiting onward transmission, and supporting surveillance of resistant TB strains (Liebenberg et al., 2022). However, limitations remain, including dependence on stable electricity, relatively high per-test costs, and restricted ability to detect novel or uncommon resistance mutations outside predefined assay targets. Nevertheless, cartridge-based NAATs continue to serve as a benchmark for POC resistance diagnostics and illustrate the feasibility of molecular DST outside centralized laboratories (Chakraborty, 2024).

CRISPR-based diagnostic technologies represent a rapidly evolving frontier in POC molecular testing, offering unprecedented specificity, adaptability, and speed. Platforms built on CRISPR-Cas12 and Cas13 systems exploit the collateral cleavage activity triggered upon sequence-specific recognition of target DNA or RNA. Once activated, Cas enzymes cleave labeled reporter molecules, generating detectable fluorescence, colorimetric, or electrochemical signals. These assays typically operate under isothermal conditions, eliminating the need for thermal cycling and enabling simplified instrumentation suitable for decentralized settings (Ghorbani et al., 2021). CRISPR-based POC assays have demonstrated the ability to detect pathogen-specific nucleic acids as well as single-nucleotide polymorphisms (SNPs) associated with antimicrobial resistance. Proof-of-concept studies have validated their capacity to discriminate resistance-conferring mutations such as gyrA substitutions associated with fluoroquinolone resistance and carbapenemase genes including bla_NDM and bla_KPC with high analytical specificity (Yang et al., 2023). SNP-level detection is critical for accurate resistance profiling, and programmable guide RNAs allow rapid adaptation to emerging resistance mechanisms in neglected diseases such as malaria or MDR bacterial infections (Kohabir et al., 2025; Zhuang et al., 2025). Moreover, assay times typically range from 20 to 40 minutes, supporting near-real-time clinical decision-making as some CRISPR assays are performed on benchtop instruments in high-complexity laboratories, others are adapted for decentralized near-patient testing with minimal equipment, while true point-of-care systems integrate lateral flow or microfluidic cartridges for rapid, on-site results without specialized infrastructure. Time-to-result and operational requirements vary substantially across these formats. These assays hold high potential for next-generation POC diagnostics but require further clinical validation and regulatory approval. Regulatory approval pathways for CRISPR diagnostics are still evolving; for example, the FDA provides Emergency Use Authorizations (EUA) for rapid deployment, the EU IVDR framework establishes risk-based validation requirements, and the WHO prequalification program guides global adoption of high-priority diagnostics. Real-world performance data across diverse healthcare settings also remain limited (Wang et al., 2025; Zhou et al., 2026).

Biosensor-integrated POC systems represent a complementary approach to nucleic acid amplification–based diagnostics, leveraging advances in nanotechnology, surface chemistry, and signal transduction. These platforms utilize molecular recognition elements—such as oligonucleotide probes, aptamers, or antibodies—coupled with nanomaterials including graphene, gold nanoparticles, and quantum dots to detect nucleic acids or resistance-associated proteins with exceptional sensitivity. Several research prototypes have reported detection limits in the femtomolar to picomolar range, enabling reliable analysis from very small sample volumes such as fingerstick blood, urine, or swab lysates (Chen et al., 2025). Electrochemical biosensors measure changes in current, voltage, or impedance upon target binding, while optical biosensors exploit fluorescence, plasmon resonance, or photonic signal changes. Nanomaterials enhance signal amplification, surface area, and binding kinetics, contributing to rapid assay times—often under 15 minutes—and low power requirements. In principle, these characteristics suggest suitability for decentralized testing and outbreak response; however, translating laboratory performance into consistent field-level reliability remains a major challenge (Grieshaber et al., 2008). These platforms are well-suited for field deployment in remote or resource-limited settings, crucial for neglected infectious diseases. In the context of AMR, biosensor-based systems have been explored for detecting resistance genes, enzymatic activity (e.g., β-lactamase function), and specific resistance-associated proteins. Their rapid turnaround and portability offer significant advantages; however, the translation of biosensor prototypes into clinically validated products is hindered by substantial challenges, including batch-to-batch reproducibility, matrix effects in complex clinical samples, long-term stability, limited multiplexing capacity, and a lack of standardized validation frameworks. At present, biosensor-integrated systems are best viewed as experimental or early-stage translational technologies, with strong long-term potential for POC resistance diagnostics but insufficient evidence to support routine clinical adoption at scale (Kao and Alocilja, 2025; Ait Lahcen et al., 2026).

Multiplex POC molecular platforms represent a significant advance in syndromic diagnostics by enabling simultaneous detection of multiple pathogens and resistance determinants from a single sample. These systems employ multiplex PCR or microarray-based technologies to identify a broad spectrum of viruses, bacteria, and fungi alongside key antimicrobial resistance genes such as mecA, bla_NDM, bla_KPC, and vanA/B. This comprehensive diagnostic approach is particularly valuable in clinical syndromes such as sepsis, respiratory tract infections, and sexually transmitted infections, where clinical presentation alone is insufficient to guide targeted therapy (Serapide et al., 2025). These panels provide pathogen ID and resistance profiles in 45–90 minutes, enabling rapid, evidence-based therapy and minimizing unnecessary antimicrobial use (Candel et al., 2024). Limitations include predefined panels and assay complexity, but multiplex POC systems remain powerful tools for high-priority neglected pathogens such as MDR TB, drug-resistant N. gonorrhoeae, and resistant malaria. Nonetheless, for high-priority pathogens such as methicillin-resistant Staphylococcus aureus (MRSA), carbapenem-resistant Enterobacterales, drug-resistant Neisseria gonorrhoeae, and multidrug-resistant TB, multiplex POC platforms offer a powerful diagnostic solution that bridges clinical microbiology and real-time therapeutic decision-making (Yamin et al., 2023).

Collectively, POC molecular diagnostic platforms with integrated resistance detection have transitioned from experimental tools to realistic clinical options for managing high-priority infectious threats. Compared with purely phenotypic or antigen-based rapid tests, these molecular systems provide mechanistic insight into resistance, enabling earlier and more precise antimicrobial interventions (Alatawi et al., 2025). Deployment reduces diagnostic delays, informs therapy selection, strengthens antimicrobial stewardship, improves AMR surveillance, and opens avenues for future therapeutic strategies in neglected diseases. As summarized in Table 1 and illustrated in Figure 3, each platform class offers distinct strengths and limitations, suggesting that no single technology will meet all diagnostic needs. However, inappropriate reliance on limited resistance panels, false-negative results, or misinterpretation of molecular resistance markers at the point of care may lead to suboptimal therapy selection and unintended antimicrobial exposure.

These risks underscore the need for careful clinical integration, confirmatory testing pathways, and stewardship oversight to ensure that POC resistance diagnostics augment rather than undermine rational antimicrobial use. A tiered strategy combining cartridge-based NAATs, CRISPR assays, biosensors, and multiplex panels will likely define future POC resistance testing (Ghouneimy et al., 2022). Continued investment in clinical validation, regulatory harmonization, and integration with digital health infrastructure will be essential to fully realize the transformative potential of these technologies in combating antimicrobial resistance.

Biosensor-based genotypic resistance diagnostics are gaining increasing attention as a promising alternative to amplification-dependent molecular assays. These systems integrate highly specific molecular probes such as oligonucleotides, peptide nucleic acids, or CRISPR-derived recognition element with ultra-sensitive nanomaterial-based transducers, including graphene field-effect transistors (GFETs), plasmonic gold nanoparticles, carbon nanotubes, and electrochemical electrodes (Bakht et al., 2026). Signal changes upon target binding enable direct detection of resistance genes with exceptional sensitivity, sometimes at femtomolar or single-molecule levels (Huang et al., 2021). A key advantage of biosensor-based approaches lies in their exceptional analytical sensitivity, with some platforms capable of detecting target molecules at femtomolar or even single-molecule levels without extensive sample amplification. This capability opens the possibility of near-instantaneous AMR profiling at the point of care, significantly shortening diagnostic turnaround times. Low power requirements, miniaturization, and compatibility with portable devices make these platforms well suited for deployment in low-resource and outbreak-prone settings (Zhang et al., 2024). Furthermore, the low power requirements, miniaturization potential, and compatibility with portable readout devices make biosensor platforms well suited for deployment in low-resource, field-based, or outbreak-response settings. As fabrication costs decrease and multiplexing improves, biosensor-based diagnostics may support decentralized AMR surveillance and precision-guided therapy in neglected infectious diseases (Diouf and Savane, 2026).

Isothermal nucleic acid amplification technologies are playing an increasingly central role in the evolution of portable POC resistance diagnostics. Techniques such as loop-mediated isothermal amplification (LAMP), recombinase polymerase amplification (RPA), and rolling-circle amplification enable rapid target amplification at a constant temperature, eliminating the need for bulky and energy-intensive thermocyclers. These methods are now being integrated into compact, hand-held, or battery-operated devices that incorporate preloaded, lyophilized reagents and simplified sample processing workflows (Panno et al., 2020). They allow rapid detection of resistance genes in neglected infectious diseases, enabling informed therapy decisions in settings where empirical treatment has dominated. From an AMR perspective, these systems can be designed to rapidly screen for resistance-associated genes, allowing clinicians to make informed treatment decisions in settings where empirical therapy has traditionally dominated. Ongoing improvements in assay specificity, contamination control, and multiplexing capacity are further enhancing the clinical reliability of isothermal POC diagnostics. Their maturation will expand access to precision antimicrobial therapy and support timely interventions in high-burden, low-resource environments (Srivastava and Prasad, 2023).

Genotypic diagnostics are limited to known resistance mechanisms. Rapid phenotypic antimicrobial susceptibility testing (AST) at the POC provides culture-free evaluation of drug response, complementing genotypic assays. Advances in microfluidics, nanofluidics, and droplet-based technologies now enable the isolation and interrogation of individual bacterial cells within highly controlled microenvironments (Hattab et al., 2024). These platforms expose bacteria to defined concentrations of antimicrobial agents and monitor growth dynamics, metabolic activity, or cellular responses using optical, electrical, or biochemical readouts. Experimental POC phenotypic AST systems have demonstrated the ability to generate minimum inhibitory concentration (MIC)-equivalent results within 30 to 120 minutes, orders of magnitude faster than conventional culture-based susceptibility testing (Reszetnik et al., 2024). This approach is especially valuable for emerging resistance phenotypes, combination therapies, and infections where resistance mechanisms are heterogeneous or continuously evolving.

The expanding global infrastructure for genomic surveillance of infectious pathogens provides a powerful foundation for the next wave of adaptive POC resistance diagnostics. Large-scale genotypic databases capturing resistance mutations, mobile genetic elements, and strain-level epidemiology can inform the dynamic design of diagnostic target panels tailored to regional and temporal AMR patterns. Cloud-connected POC platforms can be updated in near real-time, ensuring relevance as resistance landscapes evolve (Baker et al., 2023). By integrating genotypic precision, phenotypic relevance, and digital intelligence, next-generation POC diagnostics can bridge the gap between rapid testing and clinically actionable therapy. These emerging technologies signal a shift toward more comprehensive, flexible, and future-proof POC resistance diagnostics. By integrating genotypic precision, phenotypic relevance, and digital intelligence, next-generation POC systems have the potential to close the long-standing gap between rapid testing and clinically actionable susceptibility information. Successful translation will depend on rigorous clinical validation, regulatory harmonization, and thoughtful integration into existing healthcare and surveillance infrastructures. These emerging technologies promise to guide precision antimicrobial therapy, strengthen stewardship, and mitigate AMR in neglected infectious diseases.

POC Diagnostic testing provides detection of antibiotic resistance in real-time, allowing clinicians to initiate targeted therapy during their first patient encounter, reducing the use of broad-spectrum antibiotics as empirical therapy and decreasing toxicity and reduction of the development of antibiotic-resistant infections (Alatawi et al., 2025).

By incorporating POC diagnostics with cloud-based monitoring and AI based analytics, there is a capability for real time tracking of resistance patterns, prediction of outbreaks, and implementing data-driven antimicrobial stewardship interventions. This could significantly curb the spread of MDR and XDR strains, especially in high-burden regions (Pennisi et al., 2025).

The compact, battery-powered, and instrument-free system delivers relevant diagnostic and antibiotic resistance results in isolated, rural or conflict-stricken areas, enabling timely treatment and reduced mortality and transmission rates (Alatawi et al., 2025).

Rapid adaptation to newly emerging or re-emerging pathogens will be achieved easily with the modular design features of CRISPR, isothermal, and biosensor-based systems, which provide the ability to quickly reconfigure. Such agility is critical in pandemic preparedness and response (Zhou et al., 2025).

The use of new antimicrobial modalities such as phage therapy, CRISPR-based antimicrobials, and targeted small molecules is increasing and POC resistance diagnostics would provide information that would help guide the rational use of these antimicrobial modalities, the monitoring of their therapeutic efficacy over time and the minimization of the development of resistance against these new modalities of treatment (Olawade et al., 2024).

Future POC systems may integrate information related to host responses including inflammatory and immune responses as well as pathogen detection and resistance profiling to enable the ability to develop a personalised management approach for the infection developed based on the specific genotype of the pathogen, combined with an assessment of the host’s clinical condition (Lakshmanan and Liu, 2025). Overall, the integration of the above advances will lead us away from reactive management of infections to a more precise, data-intensive, and anticipation-based management of infections. This will benefit individual patients as well as the public health system overall.