Jul 2026· International Scientific Journal of Engineering and Management· Vol 05, pp. 1-9· 0 citations
TL;DR
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.
Abstract
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.
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.
K. Jyothi, Shaik Khasim Basha· International Scientific Jou...· 0 citations
The provision of medicines information (MI) services requires interpretation and clinical judgement of complex scenarios by pharmacists. To date, few studies have assessed the performance of artificial intelligence (AI) chatbots to assist pharmacists providing MI advice.
To evaluate the performance and risk associated with two AI chatbots (Microsoft Copilot and Google Gemini) to answer medicines‐related questions.
A sample of 20 questions answered by the local MI service in November 2023 was entered in the two chatbot applications in January 2024 (round 1) and May 2024 (round 2). All questions were preceded with the prompt ‘I'm a pharmacist’. Chatbot responses were evaluated by comparing with a reference answer given by the MI service using a consensus process in the domains of content, patient management, risk of patient harm, and follow up review. Ethical approval was granted by the Canterbury District Health Board Research Office (Reference no: 20311) and the study conforms with the Declaration of Helsinki.
For the 20 questions answered by both chatbots, few of the round 1 responses (
n
= 4 for Copilot and
n
= 2 for Gemini) were considered complete and with adequate information to commence patient management with no risk of harm. Most were incomplete (
n
= 13 for Copilot and
n
= 15 for Gemini) regarding content, but none were high risk of causing harm. In round 1, four responses from Copilot and eight from Gemini were flagged for follow up review. There was no significant difference in performance between chatbots in round 1 (p = 0.68) or between rounds 1 and 2 (Copilot p = 0.25 and Gemini p > 0.99).
Our study results demonstrated the chatbots' responses were typically suboptimal; albeit, a significant minority prompted a follow up to review the chatbot response.
Duncan Yorkston, Tracey Borrie, Paul K L Chin· Journal of Pharmacy Practice...· 0 citations
Patients with diabetes increasingly consult artificial intelligence (AI) chatbots for medical advice, including guidance on antidiabetic medication management during Ramadan fasting, because AI can simplify and summarize long, complex guidelines. Also, in hospital settings, these tools are being used in hospitals much faster than it takes to establish formal regulations and guidelines for their use. Evaluations of the accuracy, completeness, and reproducibility of such advice across languages are still lacking. Therefore, the study aims to evaluate and compare the accuracy, completeness, safety, and reproducibility of three widely used AI chatbots—ChatGPT, Google Gemini, and Microsoft Copilot—when providing antidiabetic medication adjustment advice during Ramadan in both English and Arabic.
Twenty-three standardized clinical scenarios covering common antidiabetic regimens were presented to each chatbot in both English and Arabic. Each query was repeated to evaluate reproducibility, resulting in 276 responses scored. Responses were assessed against the International Diabetes Federation–Diabetes and Ramadan (IDF-DAR) Guidelines using a 0–2 accuracy scale, a 0–4 completeness scale, and a 0–3 safety scale.
Overall, 77% of responses were fully consistent with the guideline, 12% were partially consistent, and 11% (30/276) contained clinically harmful or contradictory advice; harmful responses were about twice as common in Arabic as in English (14% vs. 8%). Completeness and safety were high, with medians at the observed ceiling. In the generalized linear mixed models, chatbots did not differ significantly in accuracy, completeness, or safety, and there was no significant main effect of language or chatbot × language interaction; the strongest signals were a chatbot effect on completeness (
p
= 0.068) and a language effect on safety (
p
= 0.064), both non-significant. Two-week reproducibility was fair for accuracy (weighted κ = 0.20,
p
= 0.009) and completeness (κ = 0.29,
p
= 0.001) and showed a very low κ in the safety scale (κ = 0.02,
p
= 0.81).
AI chatbots demonstrated comparable performance in delivering guideline-based advice for diabetes management during Ramadan, with no significant differences in accuracy, completeness, or safety. While most responses aligned with the IDF-DAR guideline, some harmful recommendations persisted, and response consistency fluctuated over time. These results suggest that AI chatbots should serve as a supplementary resource rather than a substitute for professional medical advice.
Sufyan Alomair, Maryam Alsuwayq, Walla Alabbad et al.· Frontiers in Medicine· 0 citations
With the rapid development of artificial intelligence (AI), natural language processing (NLP), and mobile health technologies, remote healthcare services have significantly improved. Intelligent healthcare chatbots have emerged as an important tool for providing scalable, affordable, and continuous patient care outside clinical environments. These chatbots use conversational interfaces to deliver medical information, perform symptom checks, schedule appointments, remind patients about medications, provide mental health support, and offer personalized health education. The increasing workload in healthcare systems, shortage of healthcare professionals, and rising chronic diseases have accelerated the adoption of chatbot-based remote care solutions. This paper presents a comprehensive study of intelligent healthcare chatbots for supporting remote patients, including their architecture, features, integration with health information systems, and clinical applications. Modern chatbots employ machine learning models, deep learning-based NLP techniques, and medical knowledge bases to enable context-aware, adaptive, and personalized patient interactions. Healthcare chatbots also improve access to medical services, particularly in underserved and rural areas where healthcare facilities may be limited. The paper further discusses ethical, legal, and privacy concerns such as patient data protection, regulatory compliance, and potential algorithmic bias. Chatbot performance is evaluated using metrics like response accuracy, user satisfaction, task completion rate, and clinical relevance, highlighting their advantages over traditional telehealth methods. The proposed framework integrates multimodal data sources, real-time patient feedback, and active learning techniques to enhance clinical decision-making and patient engagement. Overall, intelligent healthcare chatbots can reduce response time, improve treatment adherence, and enhance patient experience. The study concludes that healthcare chatbots have strong potential to transform remote healthcare delivery while emphasizing the need for further research to improve clinical reliability and regulatory compliance.
Anita Verma· International Journal of Mod...· 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
An AI-assisted healthcare kiosk, a web-based, fully offline system that provides automated disease prediction, medication recommendations, BMI assessment, cardiovascular risk evaluation, and doctor referrals without needing a permanent physician or internet connection is discussed.
Thanu Shree M. N, M. Vijayalakshmi· International Research Journ...· 0 citations