International Journal of Science, Engineering and TechnologyAuthors: Dr.Vinod Kumar, Vineet Salar, Monti Saini
Abstract: Medical records contain many messy details, making it difficult for patients or general health workers to follow them. Regular NLP tools fail when they jump across fields because each part requires its own know-how, rather than a single model handling everything. To use multiple smart agents at once, use fast LLaMA technology that is sped up by Groq chips, is an idea. The focus of one is on heart issues, another is on lung function, and the third is on mental health – all working together. A team lead agent gathers their insights into a clear wrap-up, giving a comprehensive diagnosis that anyone can grasp once they're done. The setup is powered by Flask and allows users to send in files, check results from smart modules, or retrieve the condensed version later. Testing demonstrates that it is more accurate, generates fewer false details, and runs faster than basic one-model tools. Findings suggest that splitting tasks among focused agents leads to deeper, smarter handling of tricky health records [1], [2]. Remote care settings benefit greatly from this approach, which aids patients in comprehending information, sorting diagnostic steps, and streamlining clinic operations.
DOI: http://doi.org/10.5281/zenodo.20339099
