ReviewArchives of Razi Institute2025
Artificial intelligence in Combating Antimicrobial Resistance.
Review in Archives of Razi Institute, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- ASP-COMPLEX: redefining antimicrobial stewardship to improve outcomes in high-risk patients with severe infections.Revista espanola de quimioterapia : publicacion oficial de la Sociedad Espanola de Quimioterapia · 2026Review
- Leveraging artificial intelligence for One Health: opportunities and challenges in tackling antimicrobial resistance - scoping review.One health outlook · 2025Article
- Artificial intelligence in Combating Antimicrobial Resistance.Archives of Razi Institute · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antibiotic resistance (AR) has become a significant worldwide public health concern in the twenty-first century. Antimicrobial resistance (AMR) occurs when microorganisms, such as bacteria, fungi, parasites, and viruses acquire genetic changes that make them resistant to antimicrobial drugs, including antibiotics. AMR, often known as the "Silent Pandemic," requires prompt and persistent intervention rather than postponement. Failure to take preventative measures will result in AMR becoming the primary cause of mortality worldwide. In the fight against multidrug-resistant bacteria to halt antibiotic resistance, conventional techniques for developing drugs are expensive and time-consuming. However, AI systems can rapidly scan extensive chemical libraries and forecast possible antibacterial agents. Considering the slow progress of ongoing antibiotic research, it is essential to accelerate the development of novel antibiotics and supplementary treatments. The acceleration is essential to effectively address the increasing health risk posed by antibiotic-resistant bacteria and to ensure that we maintain an advantage in combating these emerging threats. The use of AI in medical research holds significant promise, particularly in addressing multidrug-resistant (MDR) infections to battle AMR. This study focuses on the effective applications of AI in addressing AMR and its potential benefits for humanity. It covers fundamental concepts of AI, current available resources for AI, its uses and scope, as well as its benefits and limitations.AI algorithms consistently observe antibiotic usage, diseases occurrences, and resistance trends. This review explores how AI is used to identify AMR markers, diagnose AMR, develop smallmolecule antibiotic and also emphasizes emerging research domains, such as AMR detection and novel medication development, which contribute to managing AMR.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.