# A Review on Medical Textual Question Answering Systems Based on Deep Learning Approaches
## 1 Introduction
The system can answer factoid and open-ended questions and provide answers from foreign resources using translation techniques. However, there are still several problems in this field that need solutions, such as how to answer the given health-related questions. There are different blogs or websites where people can ask questions and obtain answers, but it is still time-consuming to locate the information, which is similar to your own question [19]. For traditional IR systems, health workers are given different possible answers to the questions, then users go through the provided answers to find the correct one. This is time-consuming and delays the instant assistance required to solve the pressing needs of the patients. To address the challenges of accuracy in answering questions and reducing the response time for IR systems, QASs have been introduced to precisely provide timely, correct answers to health workers and their clients [20]. The research methods used in QAS can be categorized into three main categories: Semantic Parsing [21], IR [22] and Neural Networks [23]. A cue-word-based technique that categorizes semantic classes in a patient treatment scenario and analyzes their relationships is proposed in [24]. Besides this, the authors employed an automatic classification process to determine the divergence of the result. In automatic QAS, answering questions is performed in two ways: match and generation, both of which depend on the same QA corpus [25]. [PERSON], et al. [26] stated that closed-domain QA aiming at domain-specific information is expected to attain a reliable and effective performance in real-world applications. Likewise, [PERSON] and [PERSON] [27] asserted that closed-domain QA could improve the satisfaction of users by using the specialized information required by domain experts. Specifically, [PERSON] and [PERSON] [28] stated that the medical domain QA face several challenges, including complex domain-specific terminology, ontological and lexical resources. Although there has been significant progress in QAS, very few studies have focused on the medical field for various reasons, such as lack of label information for most medical texts, diversity of questions, and models which fail to understand the question/answer text [29]. Deep learning techniques have been found to be very successful in dealing with medical tasks [30]. However, most existing medical systems are not trained on the medical datasets, which causes poor accuracy, and sometimes errors in the retrieved answers [31]. There is a growing need to develop advanced medical QAS due to the shortage of medical practitioners and the difficulty some people in accessing hospitals [32]. Therefore, this article aims to review the recent deep learning approaches used for QAS in the medical domain. The hierarchical structure of this survey is illustrated in Figure 1.  
The main contributions of this paper are as follows:  
1. Provide a systematic overview of the development of MQA Systems;
2. Categorizing deep learning techniques applied to various MQA tasks;
3. Presenting Evaluation Metrics to measure the effectiveness of MQA Systems;
4. Identifying existing challenges to further research in the MQA field. There are some differences among this survey and the existing related surveys. For example, [PERSON], et al. [33] published a survey in 2015, which compares four biomedical systems over features such as corpus, architecture and user interface. The survey in [33] does not give out details of the methods used in these medical QAS. [PERSON], et al. [34] gave out a literature review in 2017, which is focused on the general QAS, but not the medical QAS. This paper is organized as follows. Section 2 reviews the overview of medical QASs. Deep learning methods used in various phases of medical QASs are reviewed in Section 3. Section 4 provides different medical datasets and evaluation metrics used for measuring the effectiveness of medical QASs. The existing challenges and possible future directions in the field of medical QAS are described in Section 5.