The Potential of Biomedical Text Analysis in Healthcare

Biomedical text mining plays a crucial role in modern healthcare by extracting valuable insights from vast amounts of medical literature and patient data. With the increasing volume of unstructured medical information, artificial intelligence (AI) and machine learning (ML) have become essential tools for automating diagnosis explanations, treatment recommendations, and drug information retrieval. Traditional AI chatbots have been employed to generate medical report summaries and provide drug-related details, but they often suffer from issues related to accuracy, interpretability, and user trust. This research explores the transition from AI-integrated chatbots to a pre-trained ML model based on BioBERT, a high-accuracy model for medical diagnosis and treatment recommendations. The study highlights the challenges faced in processing medical text, including contextual understanding, data privacy concerns, and regulatory compliance. By leveraging BioBERT, the proposed system improves diagnostic accuracy and enhances the interpretability of medical recommendations while reducing the limitations associated with AI chatbots. Our methodology involves integrating BioBERT into a web-based healthcare application that allows users to manage health records, access diagnostic insights, and track system performance through analytics. The study demonstrates that the ML-based approach significantly enhances decision-making efficiency, providing more reliable and explainable medical recommendations. The findings contribute to the advancement of AI-driven medical support systems, paving the way for more accurate and user-friendly healthcare applications.