Aspiring ML engineer with knick of LLMs

github

Apoorv Sadhale  • Peerlist

Our project aims to revolutionize clinical trials through AI-powered advancements, leveraging ML algorithms. We utilize AI models to develop clinical decision support tools that enhance safety profiles, optimize dosing regimens, model drug interactions, identify biomarkers for treatment response, and adapt trial designs based on real-time data. Our innovative approach spans four phases: Safety and Dosing (Phase 1), Efficacy and Side Effects (Phase 2), Large-Scale Testing (Phase 3), and Post-Market Surveillance (Phase 4).

The AI-enhanced drug discovery market is projected to grow at a CAGR of 40.02% from 2023 to 2028, indicating a significant opportunity to leverage Machine Learning and Neural Networks to bridge gaps in the drug development pipeline, reduce costs, and expedite the process. The use of AI and ML in clinical trials can accelerate the timeline of clinical trials, reduce the number of expensive trials needed to bring a drug to market, and improve patient matching.

Technical challenges, such as data quality, technical expertise, and regulatory considerations, must be addressed to fully realize the potential of AI in drug discovery and development. Our project is supported by cutting-edge technologies and strategic partnerships, positioning us to transform the landscape of clinical trials and contribute to the evolution of healthcare.