Introduction

In a significant stride against antimicrobial resistance (AMR), researchers from IIIT-Delhi and Inria Saclay, France, have developed a cutting-edge AI system to help clinicians identify effective antibiotic combinations against drug-resistant bacteria.

The Growing Challenge of Antimicrobial Resistance

AMR poses a major threat to global health. In several low- and middle-income countries, over 70% of hospital-acquired infections are resistant to one or more antibiotics, often rendering standard treatments ineffective and escalating patient risk.

Collaborative Effort and Research Team

This AI initiative is a part of the India-France research partnership. It was led by Prof. Angshul Majumdar (IIIT-Delhi) and Dr. Emilie Chouzenoux (Inria Saclay), with contributions from engineer Stuti Jain and graduate students Kriti Kumar and Sayantika Chatterjee.

Innovative Approach of the AI System

The AI model uses clinical data from Indian hospitals, antibiotic chemical structures, and bacterial genome information. It generates precise treatment suggestions tailored to infection profiles, going beyond the static nature of traditional drug charts.

Testing and Validation

Researchers tested the system against bacteria like Klebsiella pneumoniae, Neisseria gonorrhoeae, and Mycobacterium tuberculosis. The tool reliably identified antibiotic pairs with high predicted efficacy, confirmed by resistance data and expert review.

Real-World Impact and Future Applications

A real case at AIIMS Kalyani illustrates the tool’s potential: a resistant hip implant infection left doctors with few options. The AI tool could have offered alternate combinations based on the bacterial genome. Future plans include adaptation for viral and lifestyle diseases.

Conclusion

This AI-powered innovation provides a scalable, data-driven solution to combat AMR, especially in under-resourced settings. The project showcases how international collaboration and machine learning can directly impact global health outcomes.