talk-data.com

This project develops an AI-driven prescription support tool aimed at enhancing the accuracy, safety, and efficiency of medication prescribing in clinical settings. Every day, healthcare providers face immense pressure managing high patient volumes and navigating complex, evolving medical information. Our tool leverages explainable AI to flag potential drug-to-disease interactions in real time, reducing cognitive burden and supporting safer decisions. Built on WHO disease classifications and aligned with FHIR standards, it is designed for seamless future integration with Electronic Health Records (EHRs), offering a scalable solution for smarter, more connected prescribing.

The aim of the project is predicting how biomolecules bind, like proteins and DNA, is crucial for breakthroughs in genetics, drug discovery, and disease research. Traditional methods are slow and costly. Our project uses AI to predict binding strength directly from 3D structures, drastically cutting time and costs. By training a deep learning model on a specific protein and many mutated DNA variants, we can quickly determine the strength of the interaction. This helps researchers scale, test ideas faster, improve models sooner and expedite scientific progress.

This project focuses on enhancing cyber defense capabilities through the deployment of intelligent AI agents that augment human expertise within Security Operations Centers (SOCs). Addressing key challenges such as alert fatigue, manual threat correlation, and high-volume event processing, the solution integrates seamlessly with existing infrastructure—including K3s clusters, Loki, Vector, and Security Onion. The core of the solution is a multi-agent architecture that automates tier-1 security analysis while escalating complex threats to human analysts with full contextual insight.