# A Review on Medical Textual Question Answering Systems Based on Deep Learning Approaches
## 5 Existing Challenges and Future Directions  
Although MQA has made considerable progress, there are still many uncertainties and limitations in the existing research. For example, the binary relationships still cannot represent all questions when the answer extraction is obtained by the comparison between the annotations of knowledge bases and user question. In addition, the acquisition of scientific knowledge is also a limitation of the system, because only experts in the field are able to add knowledge and increase system coverage [68; 79].  
During the review of MQASs based on deep learning approaches, we realized that researchers still face several challenges, and we suggested key directions that they should focus on in the future. Some of the following challenges remain either unsolved or answered to some extent.  
1. Retrieval of relevant and reliable answers  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **\#QA** & **QA Type** & **Source** \\ \hline MedQUAD & 47K & Ranking & Health articles \\ EmrQA & 400K & Extractive & Medical records \\ QA4 MRE & 600 & Ranking & Medical records \\ cMedQA & 101,7K & Extractive & Health articles \\ MASH-QA & 35K & Extractive & Health articles \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of different healthcare QA datasets.  
The existing medical search engines do not provide relevant answers on time. There is still a delay in patients obtaining the answer they need [80]. Researchers should build models that can provide relevant answers in few seconds or automatically. Document summarization still take a long time to generate the summary of the documents based on the question asked by the user. In addition, there is also a problem in the MQA system's ability to provide precise summaries from the original document. To reduce the response time of summary generation, more MQA corpus should be built for frequently asked questions that do not have answers [81].  
(2) Lack of large medical datasets  
The key issue with medical datasets, especially for clinical paraphrasing, consist of either short passages or web page title texts, both of which are not suitable to build a paraphrase generator for QA [82]. In addition, there is a lack of annotated data, and ambiguity in the clinical text which hinders the development of medical datasets. Therefore, there is a need to develop appropriate medical datasets to improve QA in the medical field [83].  
(3) Development of medical recommendation systems  
There is a need for medical recommendation systems that can provide treatment recommendations according to the description of the symptoms given by users. Specifically, there is an increasing demand for Q&A systems to successfully and efficiently assist diabetes patients [36; 84; 85]. The current diabetes management applications only provide general information management and search, but ignore a vital counselling service, which is critical for dealing with the health condition of patients living with diabetes. Therefore, by developing a QAS that can recommend the patients the types of medicines, they can take or give advice based on the symptoms provided by the patients.  
(4) Development of collaborative medical question answering systems  
In collaborative QAS (also known as community QAS), such as Wiki Answers and Yahoo Answers, answers are provided by users questions asked by other users, and the best answer is chosen manually by the questioner or by all participants through voting [74]. In the medical field, these systems allow physicians to interact with patients by effectively answering their questions.  
(5) Diversity of medical questions  
To answer questions in the medical field requires an in-depth understanding of the field. MQA passages from textbooks often do not directly answer questions, especially for case problems. One must discern relevant information scattered in passages and determine the relevance of each piece of text [86].