Researchers from IIIT-Delhi have developed AI-powered tools that analyze antibiotic resistance patterns in real time, enabling rapid responses from health agencies.

One such tool, named AMRSense, integrates routine hospital data to provide insights on antimicrobial resistance at global, national, and local levels.

The researchers advocate for the use of AI to enhance antimicrobial stewardship and surveillance by analyzing routine culture sensitivity testing reports from hospitals.

The ideal scenario for hospitals is to maintain low baseline resistance and low rates of change, which the AI tool can help monitor and improve.

Clinicians can utilize the OMROrbit visualizations to make informed treatment decisions and compare AMR rates across different healthcare facilities.

The AMROrbit Scorecard, developed by the team, won an award at the 2024 AMR Surveillance Data Challenge and visualizes resistance trajectories of hospitals in relation to global benchmarks.

A recent study published in The Lancet analyzed six years of data from 21 tertiary care centers in India, revealing connections between antibiotic pairs and resistance trends in infections.

Dr. Sethi explained that their method is cost-effective compared to traditional genomic approaches, enabling the identification of rising resistance patterns using existing hospital data.

While the accuracy of the AI models has been validated against historical data, future trends may vary due to unforeseen events like the COVID-19 pandemic.

However, limitations of the AI model arise in regions lacking consistent surveillance data, particularly where data is not digitally accessible.

Additionally, the researchers hope to explore potential links between antibiotic use in agriculture and environmental factors contributing to antimicrobial resistance.