Question Answering in Medical Domain Using Natural Language Processing: A Review

Medical Question Answering is a significant undertaking, as it could result in more rapid and precise diagnoses and treatment decisions. Question Answering models have made significant advances in accurately answering medical questions and supporting healthcare professionals in finding relevant information from vast amounts of medical literature and patient records. This paper critically evaluates the most advanced question answering technology in the field of medical, which uses natural language processing to understand and answer questions. The review covers a different technology related to QA in the medical domain, including datasets, models, evaluation metrics, and applications. Additionally, this paper discusses how large language models (LLMs) like ChatGPT are used to answer medical questions. Furthermore, the review dissects the different metrics and methodologies that are used to assess the performance of medical QA systems, with a particular focus on metrics that capture the medical relevance and accuracy of the answers. Finally, the review explores the future directions and research challenges in medical QA using NLP. Overall, this review thoroughly and completely surveys the most advanced and up-to-date research on using NLP to answer questions in the medical field, highlighting the accomplishments, challenges, and potential future advances. It is a valuable and powerful resource for researchers, practitioners, and healthcare professionals who are curious about using NLP techniques to improve healthcare delivery by answering medical questions.