## 1 Introduction  
With the emerging trends of technology, virtual assistants help users complete their daily routine tasks efficiently. Most of the virtual assistants use artificial intelligence and provide personalizedassistance to the users in the form of managing calendars, controlling smart environments, navigation, making an appointment, providing wake-up calls, and many more things [1]. Many applications from different domains currently have their own built-in virtual assistants such as televisions [2], mobile devices [3], vehicles [4], and the Internet of things [5,6]. The virtual assistant is also known as a chatbot, dialogue manager, virtual agent, interactive assistant, or conversational agent. Many well-known companies including Apple (Siri), Google (Assistant), Samsung (Bixby) and Amazon (Alexa) introduced their own virtual assistants. These virtual assistants provide an interactive user interface (text, speech, or both) that have the ability to understand requests, handle complex tasks, and generate an appropriate response using the machine learning model [7]. In the healthcare sector, the adoption of machine learning facilitated diagnosis [8,9], treatment [10,11], and streamlining of administrative tasks [12]. With the popularity of virtual assistants, healthcare is also moving toward this technology. It prevents unnecessary visits to the doctor, which reduces the administrative burden, increases efficiency, and support clinical decisions. According to a survey conducted in [13], primary care physicians spent more time managing electronic medical records (EMRs) than engaging with patients. Therefore, several virtual medical assistants were introduced, such as Nuance [14], Suki [15], and Robin Healthcare [16], which automate the process of documenting clinical information using artificial intelligence and provide services to the healthcare provider [17, 18, 19]. Moreover, several virtual medical assistants provide trusted information based on an analysis of medical symptoms, which include MedWhat [20], Your.MD [21], and Sensely [22]. These provide personal healthcare assistance using medical knowledge on the web, and EMR. These virtual medical assistants show a list of relevant diseases that match the input symptoms. Suppose, a statement 'I have abdominal pain' is linked with a list of conditions, which include bowel cancer, constipation, Crohn's disease, and gluten intolerance. The recommendation is predicted based on one symptom and requires approval from the medical specialist [23]. Due to the low accuracy and uncertainty of the existing virtual medical assistants, the resulting list of conditions may lead to depression, anxiety, and hypochondriasis [24]. To the best of our knowledge, none of the existing virtual medical assistants in the natural language processing domain considered real-time disease diagnosis based on the user's chief complaint, which has the utmost priority, stated in the patient's own words, and is the main reason for the patient's visit. It may be possible that more than one disease has the same kind of chief complaint, making it hard to identify a specific disease. Furthermore, every person has their own accent and way of explaining the chief complaint, so understanding this type of conversation is a challenge for the virtual medical assistant as well. In this study, we considered the challenges faced by the existing virtual medical assistant and proposed a solution in terms of the Medical Instructed Real-time Assistant (MIRA). MIRA supports primary healthcare services and uses spoken natural language for interactive communication to achieve a high success rate on task completion [25]. Moreover, MIRA analyzes the user's chief complaint and predicts a specific disease. Then, the users are referred to a nearby appropriate medical specialist based on the predicted disease. For the prototype version of MIRA, we used the chief complaint of glaucoma and diabetes based on the availability of collaborative medical specialists from the Yeouido Saint Mary's Hospital, Republic of Korea.