Artificial intelligence in healthcare: medical named entity recognition based audio prescription generator

Artificial Intelligence has made significant contributions in various aspects and domains of human life, permeating fields such as Education, Healthcare, Economy, Socialization, Security, Agriculture, and Privacy. This research specifically delves into the promising possibilities and applications of AI in healthcare and medical domains, focusing on developing a Medical Named Entity Recognition (NER) model. The proposed approach involves using different pre-trained NER models and an in-house-generated corpora to train a personalized Spacy model. The research then conducts a thorough comparison of these models to understand their strengths and shortcomings. Widening the scope of the proposed research, we introduce an inventive mechanism to extract medical information from doctors' audio in real-time saved on buffer memory, deleted after use, which is then used to create patient medical prescriptions. The potential benefits of this mechanism are vast, expanding beyond reducing patient wait times, enhancing the clarity of prescriptions, maintains comprehensive patient histories, and finally introducing automation into the prescription drug ordering chain. Additionally, the proposed model eases the scheduling and management of medical tests and procedures based on doctor and patient availability. In jist, the proposed paper aims to present an innovative human-centric approach to automating interactions between patients and doctors and optimizing the delivery of healthcare services. Through the integration of modern-day AI services, The proposed mechanism has the potential to bring about a potential change in medical practices, providing a more efficient and effective breakthrough to patient care.