Novel Applications of Artificial Intelligence in the Detection, Prediction, and Overcoming of Antibiotic Resistance: A Narrative Review

Antimicrobial resistance (AMR) is one of the biggest threats to public health in the world, killing more than one million people every year. The emergence of resistant bacteria outpaces the development of new antibiotics, and traditional methods of diagnosing and treating these infections are often slow, expensive, and ineffective. Meanwhile, artificial intelligence, as a transformative technology, has high potential to change the approach to dealing with AMR. The purpose of this narrative review is to comprehensively examine the various applications of artificial intelligence in discovering, predicting, and overcoming antibiotic resistance. This study was conducted by the Narrative Review method and by searching the latest authoritative articles published in databases such as PubMed, Scopus, and Web of Science between January ۲۰۱۸ and December ۲۰۲۵. Keywords used included artificial intelligence, machine learning, antibiotic resistance, drug discovery, and rapid diagnosis. The findings show that artificial intelligence is used in five key areas: (۱) predicting the resistance phenotype from the bacterial genome sequence with over ۹۵% accuracy, (۲) discovering new antibiotics with innovative molecular structures such as the drug halicin, (۳) reassigning existing drugs for antibacterial effect, (۴) optimizing combined treatment regimens tailored to each patient, and (۵) rapid detection of resistant strains using computer vision and spectroscopy in less than ۳۰ minutes. However, there are important challenges, including the lack of standardized data, the uninterpretability of black-box models, and the lack of appropriate monitoring frameworks. Artificial intelligence is a powerful complementary tool in the fight against antibiotic resistance, capable of revolutionizing diagnosis and treatment. But realizing this potential requires interdisciplinary collaboration, development of high-quality data, and design of interpretable algorithms for safe use at the patient's bedside.

کلیدواژه ها:

Artificial intelligence ، antibiotic resistance ، machine learning ، drug discovery ، rapid diagnosis