ReviewCureus2025
Artificial Intelligence-Integrated Biosensors for Antimicrobial Resistance Detection and Surveillance: A Review and Future Perspectives for Global Biosecurity.
Review in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Artificial Intelligence and Bioengineering Approaches for Antimicrobial Resistance Prediction.Medicina (Kaunas, Lithuania) · 2026Review
- Mapping the Convergence of Frontier Technologies for Major Environmental Challenges: A Chemical and Molecular Perspective on the Use of AI for Climate Action and Antimicrobial Resistance.Molecules (Basel, Switzerland) · 2026Review
- A multipurpose 1DZnO nanoplatform to combat ampicillin-resistant E. coli.Scientific reports · 2026Article
- Role of artificial intelligence in bacterial diagnostics and surveillance of anti-microbial resistance.Digital healthReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antimicrobial resistance (AMR) poses a critical threat to global health, undermining the efficacy of modern medicine. The escalating global epidemic of AMR jeopardizes the efficacy of contemporary medicine and undermines health systems globally. The swift, precise, and scalable identification of resistance determinants is essential for containment and stewardship initiatives; yet, existing surveillance techniques are constrained by time, expense, and accessibility. Recent advancements in biosensor technology and artificial intelligence (AI) provide a revolutionary approach to decentralized, intelligent AMR monitoring. This review consolidates recent advancements in biosensor platforms-encompassing electrochemical, optical, piezoelectric, paper-based, and nanomaterial-based modalities-and their incorporation with AI and machine learning techniques for improved detection, signal interpretation, and predictive analytics. This study investigates the utilization of hybrid systems in clinical, veterinary, and environmental settings under the One Health surveillance framework. The research also examines the integration of AI-enabled biosensors within digital and Internet of Things (IoT) frameworks, emphasizing its capacity to produce real-time, data-intensive insights for public health decision-making. Critical analysis is conducted on key problems, including sensor repeatability, data scarcity, algorithmic transparency, and regulatory adaptation, in conjunction with socioeconomic and ethical considerations. The report delineates prospective avenues for research, policy, and implementation, highlighting open data standards, equitable access, and interdisciplinary collaboration. These breakthroughs collectively indicate the emergence of AI-driven biosensing networks, which provide predictive, adaptive, and globally coordinated AMR surveillance.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.