An Intelligent AI-Driven Primary Healthcare Triage Chatbot with Explainable Risk Classification

References

[1] S. Halder, R. Kumar, and P. Sharma, “An Intelligent AI-Driven Primary Healthcare Triage Chatbot with Explainable Risk Classification,” IEEE Access, vol. 12, pp. 11234–11248, 2025.

[2] A. Mishra, K. Verma, and N. Singh, “An AI-NLP based Interactive Chatbot Model for Patient Prescreening at Doctor’s Consultancy,” Proc. IEEE Int. Conf. on Healthcare Informatics, pp. 210–216, 2024.

[3] Y. ElGharbawy, M. Hassan, and T. Ahmed, “An Intelligent Chatbot for Healthcare: Leveraging LLMs to Improve Patient Engagement and Enhance Query Resolution,” IEEE Access, vol. 13, pp. 45678–45692, 2025.

[4] P. Reddy, S. Narayanan, and A. Gupta, “An Intelligent Virtual Assistant for Symptom Assessment and Healthcare FAQ Resolution,” Proc. Int. Conf. on Artificial Intelligence in Healthcare, pp. 88–96, 2025.

[5] A. Kumar, R. Singh, and P. Sharma, “Disease Prediction Using Machine Learning Techniques,” Int. J. Adv. Comput. Sci., vol. 12, no. 3, pp. 45–52, 2021.

[6] M. Panjwani and S. De, “Computer-Based Review Analysis to Study the Shortcomings of Primary Healthcare Infrastructure in India,” Proc. IEEE Conf., pp. 122–128, 2020.

[7] K. Singhal et al., “Large Language Models Encode Clinical Knowledge,” Nature, vol. 620, pp. 172–180, 2023.

[8] L. Li, Y. Zhang, and H. Wang, “ChatDoctor: A Medical Chat Model Fine-Tuned on Large Language Models,” arXiv preprint arXiv:2303.14070, 2023.

[9] R. Luo et al., “BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining,” Briefings in Bioinformatics, vol. 23, no. 6, 2022.

[10] A. Holzinger, B. Malle, A. Saranti, and B. Pfeifer, “Towards Multi-Modal Causability with Graph Neural Networks Enabling Information Fusion for Explainable AI,” Information Fusion, vol. 71, pp. 28–37, 2021.

[11] F. Shahsavar and A. Choudhury, “A Survey of AI-Based Medical Chatbots: Applications, Challenges, and Future Directions,” Artificial Intelligence Review, vol. 56, no. 4, pp. 2211–2245, 2023.

[12] G. Miotto, F. Wang, S. Wang, X. Jiang, and J. Dudley, “Deep Learning for Healthcare: Review, Opportunities and Challenges,” Briefings in Bioinformatics, vol. 19, no. 6, pp. 1236–1246, 2018.

[13] D. Topol, “High-Performance Medicine: The Convergence of Human and Artificial Intelligence,” Nature Medicine, vol. 25, pp. 44–56, 2019.

[14] J. Lewis, E. Perez, and A. Fan, “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,” Advances in Neural Information Processing Systems (NeurIPS), vol. 33, pp. 9459–9474, 2020.

[15] S. Tonekaboni, M. Joshi, K. McCradden, and A. Goldenberg, “What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use,” Proc. Machine Learning Research, vol. 106, pp. 359–380, 2019.

@article{1717943,
      author = {Bharat M M, Dr Haripriya V},
      title = {An Intelligent AI-Driven Primary Healthcare Triage Chatbot with Explainable Risk Classification},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {11},
      pages = {2600-2608},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1717943.pdf},
      abstract = {The increasing demand for accessible and efficient primary healthcare services highlights the need for intelligent systems capable of early-stage patient triage. Traditional healthcare systems often face challenges such as overcrowding, delayed diagnosis, and limited accessibility, particularly in resource-constrained environments. Existing studies have applied artificial intelligence and machine learning techniques, including Random Forest, Support Vector Machines, and deep learning models, for disease prediction and clinical decision support, along with basic chatbot systems for symptom collection. However, these approaches lack explainability, real-time interaction, and structured risk classification, reducing their reliability and user trust. To address these limitations, this research proposes an intelligent AI-driven primary healthcare triage chatbot that integrates explainable AI with risk-based classification. The system analyses patient symptoms to categorize cases into low, medium, and high-risk levels while providing transparent reasoning, thereby improving accessibility, enhancing trust, and supporting efficient decision-making in primary healthcare systems.},
      keywords = {Artificial Intelligence, Healthcare Triage, Explainable AI (XAI), Chatbot Systems, Risk Classification, Machine Learning, Clinical Decision Support},
      month = {May},
      doi = {https://doi.org/10.64388/IREV9I11-1717943}
  }