Jul 2026· BMC Medical Informatics and Decision Making· 1 citation
Medicine
TL;DR
The findings suggest that integrating advanced AI techniques enhances the accuracy, responsiveness, and contextual relevance of AI-driven medical agents, suggesting this scalable and reliable system presents a viable solution to reduce healthcare workload, enhance patient engagement, and democratize access to trusted medical information.
Abstract
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.
DawAI is presented, a multimodal AI-powered virtual medical assistant designed to simulate real-time doctor– patient interactions and 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
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
Artificial intelligence (AI) is supporting clinical decision-making processes in healthcare systems, personalizing patient care, and optimizing workflows. The aim of this narrative review is to examine the role of AI technologies in supporting physicians, nurses and physiotherapists, as well as to evaluate the barriers, ethical issues, and educational transformations associated with integration. Articles related to the topic were selected from Google Scholar, PubMed, and Scopus search databases using the keywords “Artificial intelligence, healthcare team, healthcare services, informatics,” without any restrictions on publication year. Findings show that clinical decision support systems increase diagnostic accuracy and, through personalized treatment planning, enhance treatment efficacy. In nursing, AI-supported monitoring systems improve patient safety while reducing administrative burden; in physiotherapy, robotic devices, wearable sensors, and machine learning-based movement analysis support rehabilitation. AI-based health education requires new competencies such as health, data, and human literacy. Key barriers include lack of algorithmic transparency, data privacy concerns, bias, and legal uncertainty regarding accountability. In conclusion, AI functions as “augmented intelligence” that complements rather than replaces healthcare professionals. Effective integration requires transparent infrastructures, clear legal boundaries, workforce training, and human-centered practices. Healthcare teams utilizing these AI-supported systems can maximize patient health outcomes.
Ramazan Demirer· İstanbul Gelişim Üniversites...· 0 citations
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.
Sandra C. Nwobi, Zainab Loukil, Abbas Jawahar· Frontiers in Digital Health· 0 citations
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
Artificial intelligence (AI) is widely regarded as one of the most promising innovations in healthcare, yet its adoption in routine clinical practice remains limited. Only a small proportion of AI applications developed in research settings are successfully integrated into healthcare delivery. Major barriers include poor interoperability with existing health information systems, complex regulatory requirements, limited scientific evidence, and the lack of clear clinical guidelines. Many AI tools have been evaluated through methodologically weak studies, often retrospective and lacking external validation, contributing to skepticism among healthcare professionals. Additional challenges involve healthcare professionals' education and training, algorithm transparency, and the ability of healthcare organizations to effectively incorporate these technologies into clinical workflows. To promote the safe and effective adoption of AI, stronger clinical evidence, structured training programs, and organizational models capable of supporting its implementation are required. Addressing these issues is essential to ensure that AI can deliver meaningful benefits for patients, healthcare professionals, and healthcare systems.
Eugenio Santoro· Recenti progressi in medicin...· 0 citations
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