Authors :
Seemakurthi Rohith Kumar; Gowthul Alam M M; Paruchuri Anil Kumar; Paruchuri Anil Kumar; Pothineni Nikhil Yadav
Volume/Issue :
Volume 10 - 2025, Issue 5 - May
Google Scholar :
https://tinyurl.com/mryd3ar4
DOI :
https://doi.org/10.38124/ijisrt/25may629
Google Scholar
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Abstract :
By providing automated symptom analysis, prescription suggestions, and patient support, AI-powered medical
chatbots are revolutionizing digital healthcare. This work introduces a sophisticated chatbot that combines FAISS for quick
and precise medical knowledge retrieval, Large Language Models (LLMs) for natural, human-like discussions, and
Langchain for enhanced contextual comprehension and reasoning. The chatbot ensures data protection and regulatory
compliance while providing real-time responses and personalized support. It is effective and scalable, supporting a variety
of healthcare jobs, improving patient involvement, and optimizing clinical operations.
Utilizing cutting-edge AI technology, the system lessens the workload for medical personnel, promotes prompt decision-
making, and increases accessibility to medical information and consultations. In addition to providing patients with
immediate, dependable assistance, this breakthrough opens the door for more intelligent, networked digital healthcare
services. All things considered, the combination of LLMs, FAISS, and Lang Chain marks a significant advancement in the
creation of intelligent, safe, and easily available AI healthcare solutions.
Keywords :
AI-Driven Healthcare, Medical Chatbot, Symptom Analysis, Llms, FAISS, Langchain, Personalized Assistance, Real- Time Response, Data Security, Patient Engagement.
References :
- Basit, A., Hussain, K., Hanif, M. A., & Shafique, M. (2024). MedAide: Leveraging Large Language Models for On-Premise Medical Assistance on Edge Devices. arXiv preprint arXiv:2401.12345.
- Singh, A., Ehtesham, A., Mahmud, S., & Kim, J.-H. (2024). Revolutionizing Mental Health Care through LangChain: A Journey with a Large Language Model. Proceedings of IEEE AI for Healthcare Symposium.
- Xie, Q., Chen, Q., Chen, A., et al. (2023). Me LLaMA: Foundation Large Language Models for Medical Applications. arXiv preprint arXiv:2311.05678.
- Cárdenas, O., Falconi, S., Tusa, E., & Rodríguez, A. (2024). Development of a ChatBot Model for Health Telecare: Integration of LangChain, Embeddings with OpenAI, and Pinecone. International Conference on AI in Healthcare.
- Brown, T., Mann, B., Ryder, N., et al. (2020). Language Models are Few-Shot Learners. In Advances in Neural Information Processing Systems (NeurIPS), 33.
- Lehman, E., Jain, S., White, R., & Wallace, B. C. (2021). Does BERT Pretrained on Clinical Notes Reveal Sensitive Data?. In Proceedings of NAACL-HLT 2021.
- Lee, J., Yoon, W., Kim, S., et al. (2020). BioBERT: A Pre-trained Biomedical Language Representation Model for Biomedical Text Mining. Bioinformatics, 36(4), 1234–1240.
- Touvron, H., Lavril, T., Izacard, G., et al. (2023). LLaMA: Open and Efficient Foundation Language Models. arXiv preprint arXiv:2302.13971.
- Rajpurkar, P., Chen, E., Banerjee, O., & D'Amour, A. (2022). AI in Healthcare: The Hope, the Hype, the Promise, the Peril. Cell, 184(24), 6140–6151.
- Miloslavskaya, N., & Tolstoy, A. (2016). Big Data, Fast Data and Data Lake Concepts. Procedia Computer Science, 88, 300–305.
- Hugging Face. (2023). Transformers Documentation. Retrieved from https://huggingface.co/docs/transformers
- Facebook AI Research. (2023). FAISS: A library for efficient similarity search. Retrieved from https://faiss.ai/
- LangChain. (2024). LangChain Framework Documentation. Retrieved from https://docs.langchain.com/
- OpenAI. (2023). GPT-4 Technical Report. Retrieved from https://openai.com/research/gpt-4
- Chainlit. (2024). Chainlit: Build AI-Powered Chat UIs in Minutes. Retrieved from https://docs.chainlit.io
- U.S. Department of Health and Human Services. (2023). Health Insurance Portability and Accountability Act (HIPAA). Retrieved from https://www.hhs.gov/hipaa/
- European Commission. (2023). General Data Protection Regulation (GDPR). Retrieved from https://gdpr.eu/
- World Health Organization. (2021). Ethics and Governance of Artificial Intelligence for Health. ISBN: 978-92-4-002920-0.
- Python Software Foundation. (2024). PyPDF2 Documentation. Retrieved from https://pypdf2.readthedocs.io/
- OpenAI. (2023). Fine-Tuning GPT for Domain-Specific Tasks. Retrieved from https://platform.openai.com/docs/guides/fine-tuning
By providing automated symptom analysis, prescription suggestions, and patient support, AI-powered medical
chatbots are revolutionizing digital healthcare. This work introduces a sophisticated chatbot that combines FAISS for quick
and precise medical knowledge retrieval, Large Language Models (LLMs) for natural, human-like discussions, and
Langchain for enhanced contextual comprehension and reasoning. The chatbot ensures data protection and regulatory
compliance while providing real-time responses and personalized support. It is effective and scalable, supporting a variety
of healthcare jobs, improving patient involvement, and optimizing clinical operations.
Utilizing cutting-edge AI technology, the system lessens the workload for medical personnel, promotes prompt decision-
making, and increases accessibility to medical information and consultations. In addition to providing patients with
immediate, dependable assistance, this breakthrough opens the door for more intelligent, networked digital healthcare
services. All things considered, the combination of LLMs, FAISS, and Lang Chain marks a significant advancement in the
creation of intelligent, safe, and easily available AI healthcare solutions.
Keywords :
AI-Driven Healthcare, Medical Chatbot, Symptom Analysis, Llms, FAISS, Langchain, Personalized Assistance, Real- Time Response, Data Security, Patient Engagement.