AI Driven Healthcare : Integrative Disease Prediction and Drug Recommendation

The integration of advanced language models (LLMs) into healthcare systems has revolutionised disease pre- diction and drug recommendation methodologies. This project explores the application of LLMs in predicting diseases and recommending suitable drugs, thereby enhancing patient care and treatment outcomes. Through extensive research and experimentation, this study develops a robust framework that leverages the capabilities of LLMs to analyse patient data, medical literature, and drug databases. The proposed model utilises state-of-the-art natural language processing techniques to extract valuable insights from diverse sources, including electronic health records, clinical notes, and biomedical literature. By harnessing the power of deep learning algorithms, our system can accurately predict the onset of various diseases based on individual patient profiles and medical histories. Furthermore, the model employs advanced recommendation algorithms to suggest personalised drug therapies tailored to each patient's unique condition, genetic makeup, and treatment preferences. The project aims to bridge the gap between data-driven insights and clinical decision-making, empowering healthcare providers with timely and actionable information to improve patient care. Through rigorous evaluation and validation, we demonstrate the efficacy and reliability of our approach in predicting diseases and recommending effective drug interventions. Overall, this research contributes to the advancement of predictive analytics in healthcare and lays the foundation for future innovations in personalised medicine.