# A Review on Medical Textual Question Answering Systems Based on Deep Learning Approaches
## 1 Introduction  
Progress in the field of Question Answering (QA) is leading the world to new technology heights, especially in the medical field, to assist health workers in finding solutions to medical queries. In the late 1980s, [PERSON] developed Unix Consultant at U.C. Berkeley [1]. This system was able to answer questions relating to the Unix operating system. In addition, the LILOG project, a text-understanding system, was developed in the field of tourism information in a German city [2]. The systems developed in the UC and LILOG helped to advance the reasoning and computational linguistics. In 1999, the QA Track of the Text Retrieval Conference (TREC) started the research into QA from the perspective of Information Retrieval (IR) [3]. In this regard, QA systems answer any question by retrieving short text extracts or phrases from lots of documents, including the answer itself. QA is viewed as a combination of information extraction and IR. Generally, QA can be defined as a computer science discipline in the fields of IR and natural language processing (NLP), which focuses on developing a system that can automatically answer human questions in a natural language [4]. Usually, QA is a computer program, which can obtain answers by querying a structured database of information or knowledge. More commonly, QASs can extract answers from unstructured natural language document collections [5]. Some examples of the document collections used for QASs are as follows: compiled news-wire reports, a local collection of reference texts, internal organization web pages and documents, a subset of World Wide Web (WWW) pages, and a set of Wikipedia pages. With the significant progress made by academic research, QA can deal with a wide range of question types, including fact, definition, list, hypothetical, How, Why, crosslingual, and semantically constrained questions. Based on the QAS' domain coverage, many researchers classify them into closed-domain and open-domain [6, 7]. Closed-domain QAS use questions under a definite domain and can exploit domain-specific knowledge that is normally formalized into ontologies. In closed-domain QAS, only a few types of question are allowed, such as giving short answer only to inquiries that are related to Hyderabad city [6]. On the contrary, Open-domain QASs are rely on general ontologies and world knowledge, so they deal with nearly any kind of question. In addition, these systems usually have more data that can be used to extract answers [7]. Based on the modality of data, QAS can use text, images, or both. Multimodal QASs use multiple input modalities from users to answer questions, such as text and images [8]. The recent developments in the fields of biomedicine, electronic publishing, and computing technology have contributed to the rapid growth in the biomedical literature available online to medical practitioners [9, 10]. The medical practitioners can assess biomedical literature using the PUBMED database, which consists of more than 32 million citations from life science journals, online books, etc. [11, 12]. Question answering is one of the most important and difficult NLP tasks, which focuses on interactions between computers and human language. NLP deals with the problem of how computers are programmed to process and analyze large amounts of natural language data. The result is a computer that can understand the content of a document, including the contextual nuances of the language in the document [13]. The main challenges in NLP for QA include speech recognition, sentiment analysis [14], information extraction [15], text summarization [16], natural-language generation [17], etc. Knowledge bases and the heterogeneous corpora are among the major sources that QAS use to interpret the natural language question, and return a concise and correct answer to the user. For example, [PERSON], et al. [18] developed a QA framework that can answer student questions in natural language, by providing answers to a multi-document summary.