The challenge of diagnostic delays and the limited ability of conventional strategies to integrate complex clinical and microbiological data

The gap

Evidence profile

Sourced from the stated challenges and future work of the source papers, classified as general, spanning 3 journals.

Supporting evidence — 4 representative gaps

• Artificial Intelligence in the Fight Against Antimicrobial Resistance: Applications in Diagnosis, Susceptibility Prediction, Precision Dosing, and Antibiotic Discovery (2026) · Antibiotics · doi

  The paper identifies the challenge of diagnostic delays and the limited ability of conventional strategies to integrate complex clinical and microbiological data. The paper also identifies the challenge of developing new antibiotics, with the rapid evolution of antimicrobial resistance outpacing the introduction of new treatments. The challenge of external and temporal validation of AI-based systems is also identified, with substantially poorer performance frequently observed in these settings.

  generalstated challenges
  Keywords: paper identifies challenge diagnostic delays limited ability conventional
• Epidemiological patterns, risk factors, and prevention gaps of surgical site infections in operating rooms: a hospital-based surveillance study (2026) · Frontiers in Public Health · doi

  • The research aims to conduct multicenter studies, which will track patient health outcomes while testing predictive models to mea- sure SSIs across different patient groups. • The project aims to create risk prediction tools that will use elec- tronic health records to identify patients who are most likely to develop health complications. • The project will study the effectiveness of bundled interventions that combine various methods to improve patient care and control antibiotic usage and environmental practices. • The researchers will conduct studies over an extended period to measure how SSIs impact patient health outcomes and their eco- nomic consequences. • The researchers will utilize machine learning and predictive modeling technologies to develop advanced analytical meth- ods that will enhance SSI surveillance operations while improving resource distribution and helping create new policies. • The researchers will examine different methods to decrease anti- microbial resistance in surgical site infection pathogens, which include implementing targeted stewardship programs.

  generalfuture workevidence 5/5
  Keywords: patient health researchers aims conduct outcomes predictive ssis different project create develop multicenter track testing
• Antimicrobial Resistance: Global Threat and Solutions (2026) · International Journal For Multidisciplinary Research · doi

  Research Priorities: Future research must focus on advancing rapid diagnostic tools, developing new antimicrobials, and exploring alternative therapies. Point-of-care diagnostics are particularly important, as they enable clinicians to distinguish bacterial infections from viral ones, thereby reducing unnecessary antibiotic prescriptions [100]. Continued investment in antimicrobial discovery pipelines, especially those targeting Gram-negative pathogens, remains a critical priority for global health [101]. One Health Approach: The One Health framework demonstrates the interdependence of animal, human, and environmental health in tackling AMR. Coordinated surveillance across these domains is essential for tracking resistance determinants and designing effective interventions [102]. Incorporating veterinary and environmental data into public health systems will enhance preparedness and strengthen global capacity to respond to emerging resistance threats [103]. Sustainable Innovation Models: Long-term innovation in combating antimicrobial resistance requires both economic incentives and collaborative frameworks. Approaches such as public-private partnerships, market entry rewards, and subscription-based payment systems are currently being explored to encourage the development of new antibiotics [104]. Global actions, including the Global Antibiotic Research and Development Partnership (GARDP), serve as strong examples of cooperative models that drive progress in this field [105]. Ensuring that new therapies are distributed equitably while upholding stewardship principles will be essential for achieving sustainable outcomes in the fight against AMR [106,107]. Table 8 shows research priorities and innovation models. Table 8. Research priorities and innovation models

  generalfuture workevidence 5/5
  Keywords: health global innovation models priorities resistance therapies antibiotic antimicrobial environmental essential public systems sustainable development
• Interventions to Reduce Antibiotic Prescribing in Small Animal Veterinary Practice: Evidence and Clinical Impact (2026) · Antibiotics · doi

  Future antimicrobial stewardship programs are likely to increasingly integrate tech- nological innovation, behavioral strategies, and organizational approaches to support evidence-based prescribing. Key future directions include the use of artificial intelligence, electronic prescribing systems, rapid molecular diagnostics, point-of-care testing, integrated surveillance, and implementation strategies adapted to routine companion animal practice. The future development of antimicrobial stewardship in companion animal medicine will likely depend on the successful integration of technological innovation with behavioral and organizational strategies. Although digital tools have considerable potential to improve prescribing decisions, technology alone is unlikely to achieve sustained improvements without addressing the human factors that influence antimicrobial use. Future steward- ship models will therefore need to combine advanced analytics with clinician education, communication strategies, workflow optimization, and institutional support [99]. Continued advances in artificial intelligence may facilitate predictive clinical decision- support systems capable of estimating the probability of bacterial infection, recommending individualized antimicrobial therapy, identifying patients suitable for antimicrobial de- escalation, and automatically detecting prescribing patterns requiring stewardship review. As these technologies mature, careful clinical validation and appropriate regulatory over- sight will remain essential to ensure safe implementation in veterinary medicine [99]. Artificial intelligence-based stewardship systems may eventually support real-time clinical decision-making by integrating multiple sources of information, including elec- tronic health records, laboratory findings, antimicrobial susceptibility data, patient history, and local resistance patterns. Predictive models may assist clinicians in identifying pa- tients at increased risk of resistant infections, selecting appropriate empirical therapy, and recognizing opportunities for antimicrobial discontinuation or de-escalation. However, im- plementation should be accompanied by rigorous validation, transparency of algorithmic decision-making, and continuous monitoring to prevent inappropriate recommendations or unintended clinical consequences [100]. An important future challenge will be ensuring that artificial intelligence systems are developed using representative veterinary datasets. Many current machine-learning approaches are limited by incomplete data, inconsistent electronic records, and underrepre- sentation of diverse patient populations. Collaborative efforts to create large, high-quality veterinary datasets will be essential for developing reliable and generalizable AI-based stewardship tools [101]. The increasing availability of electronic health records offers opportunities to develop real-time antimicrobial surveillance systems capable of monitoring prescribing

  generalfuture workevidence 5/5
  Keywords: antimicrobial future stewardship prescribing systems strategies support artificial intelligence clinical based electronic decision veterinary records

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