Jul 2026· International Scientific Journal of Engineering and Management· 0 citations
TL;DR
HealthMate is presented, an intelligent, explainable AI chatbot framework designed for preliminary healthcare consultation that demonstrates rapid retrieval, robust natural language comprehension, and clear explainability without replacing professional medical diagnosis.
Abstract
ABSTRACT
In modern healthcare systems, preliminary diagnostic guidance is often constrained by high consultation costs, long clinic waiting times, and limited access to healthcare professionals in underserved regions. Consequently, individuals frequently turn to online search engines, leading to self-diagnosis errors and anxiety caused by unstructured misinformation. Traditional automated symptom checkers rely heavily on rigid rule-based systems or basic keyword matching, failing to capture semantic nuances, user phrasing variations, and clinical context.
To address these limitations, this paper presents HealthMate, an intelligent, explainable AI chatbot framework designed for preliminary healthcare consultation. The proposed system utilizes Natural Language Processing (NLP) and Sentence-BERT (all-MiniLM-L6-v2) transformers to convert natural language symptom descriptions into 384-dimensional dense semantic embeddings. High-speed similarity search is conducted against a structured medical knowledge base using Facebook AI Similarity Search (FAISS). Furthermore, a novel hybrid scoring mechanism is developed, combining dense semantic similarity with explicit token-level symptom overlap coverage to re-rank candidate conditions accurately. To eliminate "black-box" outputs, a Retrieval-Augmented Generation (RAG) architecture powered by a local LLaMA-3 language model synthesizes transparent diagnostic reasoning, precautionary guidance, and specialist advice. Implemented using Python and Streamlit, HealthMate demonstrates rapid retrieval, robust natural language comprehension, and clear explainability without replacing professional medical diagnosis.
Keywords: Medical Chatbot, Natural Language Processing, Sentence Transformers, FAISS, Hybrid Scoring, Retrieval-Augmented Generation (RAG), Healthcare AI, Streamlit.
Abstract— A healthy existence is greatly dependent on access to healthcare. Nevertheless, getting a doctor's consultation for any health issue is exceedingly challenging. The goal is to employ artificial intelligence to develop a medical chatbot that, when presented with symptoms, can diagnose a patient's condition before the patient ever sees a doctor. A user can determine the actual condition by reporting symptoms of the ailment.[1] The real goal of this activity is to address the user's symptoms and provide medical recommendations based on them in order to save time and money on the procedure. It is a system that interacts with customers through natural language processing (NLP). Chatbots are computer programs that converse with users in natural language.[2] The chatbot saves the information in the database in order to identify the keywords from the sentences, make a decision for the query, and respond to the question.
Keywords— Chatbot, Machine Learning, Deep Learning, Artificial Intelligence, HealthCare, Artificial Neural Networks.
Manisha Y, P. Rani, D. Masthan et al.· International Scientific Jou...· 0 citations
The increasing demand for accessible healthcare services, coupled with the shortage of medical professionals and geographical barriers, highlights the need for intelligent digital healthcare solutions. Traditional medical chatbots are largely limited to text-based interactions, lacking the ability to process multimodal inputs such as speech and medical images, thereby restricting their effectiveness in real-world scenarios. This paper presents DawAI, a multimodal AI-powered virtual medical assistant designed to simulate real-time doctor– patient interactions. The system integrates advanced technologies including speech-to-text conversion for interpreting spoken symptoms, image-based analysis for visual medical inputs, and large language models for generating contextaware medical responses. Additionally, a text-to-speech module enables the system to deliver responses in a natural, human-like voice, enhancing user accessibility and interaction. DawAI operates through a unified architecture that processes voice and image inputs, performs multimodal reasoning, and generates informative, empathetic responses within seconds. A structured dataset comprising symptom descriptions, severity levels, and precautionary measures supports the system’s reasoning capability, ensuring coherent and medically relevant outputs. Experimental evaluation demonstrates that the system provides consistent and context-sensitive responses while maintaining real-time performance. By addressing the limitations of existing healthcare chatbots, DawAI offers a scalable, accessible, and user-friendly solution for preliminary medical consultation, particularly benefiting users in remote and resource-constrained environments.
Dr. Abdul Khadeer, Mohammed Zubair Ahmed· International Journal of Eng...· 0 citations
Background Scotland faces a severe public health crisis with drug-related deaths reaching 267 per million people, ranking second globally after the United States. Medication-Assisted Treatment (MAT) represents a proven intervention for heroin addiction. However, healthcare professionals struggle with accessing and interpreting current MAT standards through fragmented information systems and time-consuming manual searches across multiple websites. Despite advances in healthcare chatbots leveraging Large Language Models (LLMs), no specialized systems exist to support MAT delivery or integrate advanced technologies like Retrieval-Augmented Generation (RAG) and Knowledge Graphs for addiction treatment. Objective To develop and evaluate an AI-driven chatbot prototype that integrates LLMs, RAG, and Knowledge Graphs to enhance healthcare professionals’ access to MAT standards in Scotland, addressing current barriers in information delivery and clinical decision-making. Methods We employed a mixed-methods approach combining a survey of 39 MAT healthcare professionals (31% response rate) and systematic literature review following PRISMA guidelines. The chatbot prototype was developed using Llama2 language model, Neo4j knowledge graphs, and custom RAG implementation. Data was ethically collected from Public Health Scotland and Healthcare Improvement Scotland websites. Performance was evaluated using BLEU and ROUGE metrics, with prototype deployment via Streamlit interface. Results Survey findings revealed significant challenges with current communication methods: only 5 of 39 respondents rated existing systems as “exceptional,” while 17 rated them as “average” or below. Primary challenges included decentralized information (n=13) and time-consuming access processes (n=8). Literature review of 14 healthcare chatbot studies identified a critical gap in MAT-specific applications. The developed prototype demonstrated moderate performance with BLEU score of 36.64, ROUGE-1 score of 0.48, and ROUGE-L score of 0.42. The knowledge graph successfully integrated 227 nodes, 136 relationships, and 8 characteristics representing comprehensive MAT standards. The system successfully retrieved relevant MAT standards information in response to queries about specific MAT standards, medication protocols, and implementation guidance Conclusions To our knowledge, this study provides the first prototype of an AI-driven chatbot specifically designed for MAT professionals, demonstrating feasibility of integrating advanced AI technologies to address information access barriers in addiction treatment. While performance metrics indicate potential for enhancing MAT information delivery, further development is needed to improve semantic understanding and response naturalness. The prototype establishes a foundation for future integration with electronic health records and broader healthcare systems, with the potential to support improved treatment outcomes for individuals with heroin addiction in Scotland, subject to longitudinal clinical validation.
Sandra C. Nwobi, Zainab Loukil, Abbas Jawahar· Frontiers in Digital Health· 0 citations
CIMAS HealthMate is proposed, a hybrid multilingual VHA that integrates transformer-based natural language processing (NLP) with an explainable extreme gradient boosting (XGBoost) decision model to provide accurate and transparent symptom triage.
Shamiso Simango, M. Mutandavari· Computer Science and Informa...· 0 citations
The integration of artificial intelligence (AI) in healthcare presents significant opportunities to enhance patient care and streamline medical workflows. However, challenges related to accuracy, reliability, and accessibility continue to limit the widespread adoption of AI-driven medical assistants. To the best of our knowledge, this is the first study to integrate multiple advanced AI techniques into a unified system designed to provide real-time, context-aware medical insights while ensuring accuracy, engagement, and interpretability. This study aims to develop an AI-powered medical agent capable of assisting both patients and healthcare professionals by generating informed medical responses, automating healthcare-related tasks, and improving patient interaction through interactive and reliable assistance. The methods employed in this research include Retrieval-Augmented Generation (RAG) for contextualized medical responses, the Wikipedia API for real-time knowledge retrieval, and knowledge graphs for mapping symptoms to potential diseases. Additionally, a symptom checker tool enables preliminary diagnosis and personalized health recommendations using a prompt-based system. The system was evaluated through automated performance assessments and expert reviews. The results demonstrate that the knowledge graph tool achieved 95% accuracy in medical query responses, showcasing its reliability in symptom-disease mapping. Additionally, sentiment analysis of patient interactions reached 97% accuracy, reinforcing the system's ability to understand and respond empathetically. The Wikipedia-based retrieval system maintained an average response time of 10 seconds, ensuring real-time applicability. Furthermore, 100 medical experts rated the AI agent's responses 4.6 for comprehensiveness, 4.5 for engagement, and 4.5 for empathy and tone on a 5-point scale. These findings suggest that integrating advanced AI techniques enhances the accuracy, responsiveness, and contextual relevance of AI-driven medical agents. This scalable and reliable system presents a viable solution to reduce healthcare workload, enhance patient engagement, and democratize access to trusted medical information, reinforcing its potential as a transformative tool in modern healthcare.
Yasmine Abu Adla, A. Hajj· BMC Medical Informatics and...· 1 citation
In the digital era, the internet and social media have emerged as essential platforms for individuals facing mental health issues, often used for seeking information and community support. Despite the resources of informal advice available on social media, the complexity of these issues frequently exceeds non-expert knowledge. Specialized sites such as CounselChat and 7Cups offer professional guidance, yet many at-risk individuals still rely on unmoderated sources and general web search. We address this gap by investigating ranking strategies that match pre-existing expert advice to incoming mental-health questions. We introduce , a collection built from two specialized websites, pairing user questions with verified expert responses. We address the task as answer retrieval (AR): given a question, rank expert answers by relevance. We evaluate dense retrieval with SentenceBERT and MentalBERT, and propose a second stage that improves the initial ranking via transformer-based models and large language models (LLMs), used for filtering non-relevant candidates and for reordering. Beyond retrieval, we analyze linguistic style and affective attributes across topics, questions, and responses. Results show that dense retrieval provides strong candidates and that transformer/LLM-driven reranking further elevates relevant, on-topic advice to the top positions. We further conduct qualitative error analyses, including human evaluation to study the benefits and limitations of our approaches. Taken together, these findings indicate that retrieval-first pipelines can help scale access to professional guidance.
Anxo Perez, César Piñeiro, Javier Parapar· Social Network Analysis and...· 0 citations