The startup has developed a diagnostic software platform that utilizes artificial intelligence to identify antimicrobial resistance in infectious pathogens within hours. This technology enables healthcare providers to prescribe effective drug combinations for drug-resistant infections promptly, improving patient outcomes.
Funding
$855K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
EFFIIERLFounders
Product
Problem
The rise of antimicrobial resistance (AMR) poses a significant threat to global health, as traditional diagnostic methods for identifying drug-resistant infections can be slow and cumbersome. Delayed or inaccurate diagnoses can lead to ineffective treatments, prolonged illness, increased healthcare costs, and the further spread of resistant pathogens.
Solution
AarogyaAI offers a diagnostic software platform that leverages artificial intelligence and machine learning to rapidly identify antimicrobial resistance in infectious pathogens. The platform analyzes genomic data to predict drug resistance, enabling clinicians to prescribe effective, targeted therapies for patients with drug-resistant infections. AarogyaAI's solutions are pathogen and sequencing hardware agnostic, and can be accessed across devices, providing clinicians with data-driven insights to improve patient outcomes and combat the spread of AMR. The AI algorithms are trained on global and local databases, as well as extensive literature, to detect antimicrobial resistance and search for novel mutations contributing to drug resistance.
Target Audience
The primary target audience includes healthcare providers, hospitals, and diagnostic laboratories seeking to improve the speed and accuracy of antimicrobial resistance detection for better patient management.
Features
- AI-powered analysis of genomic data for rapid identification of antimicrobial resistance
- Prediction of drug resistance based on pathogen genome
- Access across devices including desktops, mobiles, and tablets
- Algorithms trained on global and local databases and literature research
- Detection of antimicrobial resistance and novel mutations contributing to drug resistance
- Pathogen and sequencing hardware agnostic