## 1 Introduction  
In recent years, the main reason for the high mortality rate associated with female breast cancer (FBC) cases as reported by several global health organizations is attributed to delayed diagnosis and late treatment. Several studies [1, 2, 3] have revealed that lack of awareness, hesitancy, and shyness to discuss FBC symptoms during its early stages are among the top contributing factors exposing women to the danger of late treatment. Previous research has shown that Asian women were considered the least vulnerable to FBC compared to women in other parts of the world [4], however, recently there is an unprecedented rapid rise in the rate of FBC incidences in Asia [3, 4, 5]. In Taiwan; for example, the government has deployed several medical vans across the country, known as mobile mammography units [6], to serve as a desperate community outreach initiative to encourage screening for FBC symptoms. Unfortunately, irrespective of the costs of such an initiative, several patient-centric drawbacks concerning privacy, COVID-19 scare, lack of knowledge of FBC symptoms, etc., render such efforts less effective. Although the precise cause of FBC and its prevalence remains unascertained [7], the first step to averting these life-threatening risks requires that the affected persons address their symptoms in a timely manner by communicating with the appropriate healthcare providers. In medical practice, before diagnosis and subsequent treatment are administered to patients, bilateral interactions based on question-and-answer (Q&A) is the basic and most vital approach used by doctors to fathom the type and stage of patients' internal symptoms (such as early-stage FBC), which are usually invisible to the eyes by mere observation. Therefore, effective communication between doctors and patients is generally advantageous towards achieving successful medicare outcomes [8], and telemedicine via chatbots is the promising modern way to foster it [9; 10]. Based on this predication, various healthcare establishments are beginning to adopt interactive text-based chatbot systems [11] to streamline communication between doctors and their remotely distributed patients; with artificial intelligence (AI) technologies, chatbots are quickly gaining the potential to facilitate interactive communication-dependent services and the capability to independently make adaptive judgment calls reliably in task-oriented closed domains. In this paper, we present the FBC symptomatic assessment chatbot (SAC) which is a Chinese-text-based Q&A interactive dialog system developed for Chang Gung Memorial Hospital (CGMH) in Taiwan. The aim of our research study is to proffer a solution to the patient-centric communication barriers hindering most women from initiating consultations for mammography screening, especially at the early stage of FBC symptoms. For this task, first, we understand that, in critical vertical domains such as the healthcare sector, it is important that a chatbot should be built to process and match the textual Q&A dialog contexts with high-level logical consistency. According to [PERSON] et al. [12], the degree of dialog output accuracy is often the primary criteria used in judging the performance of a Chatbot. However, with respect to achieving high output accuracy, modeling a healthcare chatbot in Chinese presents both technical and linguistic challenges, especially with regards to Chinese word segmentation (CWS) [13] and other factors pertaining to medical ethics that often limit access to substantial patients' medical data samples [11; 14] required for the training of AI models. Nonetheless, we implemented the SAC model with a light-weight trigram-based natural language processing (NLP) algorithm trainable with low volume data. In addition, we created a web and an iOS front-end chat interface for easy access to the services of the chatbot. Figure 1 illustrates how the SAC achieves its goal of connecting prospective FBC patients to the healthcare providers over the network.