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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM64633
Volume:
10 |
6 |
Jun 2026
Pages:
1-6
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Master of Computer Applications
Giet Engineering College (A)
Master of Computer Applications
Giet Engineering College (A)
In today’s digital world, healthcare accessibility remains a major challenge, especially in rural and underserved areaswheremedicalprofessionals arenot easilyavailable. Manypeopledependonunreliableinternetsearchestounderstandsymptoms and health conditions, which often leads to misinformation and delayed medical attention. To address this problem, the proposed system MedSage is developed as a multimodal and multilingual AI-powered healthcare assistanceplatform. Thesystemcombinestechnologiessuchas Natural Language Processing(NLP), Retrieval-Augmented Generation (RAG), Computer Vision, and Speech Processing to provide intelligent healthcare support through text, voice, and medical image analysis. The platform allows users to describe symptoms through text or speech, upload medical images such as X-rays, CT scans, and MRI scans, and receive structured healthcare insights. The system also supports multilingual voice interaction, making it accessible to users from different language backgrounds. The symptom analysis chatbot uses NLP and RAG techniques to retrieve relevant medical information and generate context-aware responses. The medical image analysis module detects possible abnormalities and estimates severity using computer vision techniques. The voice interaction module enables speech-based communication using speech recognition technology. The proposed system improves healthcare awareness, reduces dependency on manual symptom searching, and provides an intelligent platform for early-stage health assistance. Although the system does not replace professional medical diagnosis, it acts as a supportive healthcare guidance tool for users.
KEYWORDS
The MedSage healthcare platform successfully combines Aritificial Intelligence, Natural Language Processing, Retrieval-Augmented Generation, Computer Vision, and Speech Processing into a single multimodal healthcare assistance system. The platform improves healthcare accessibility by supporting text, voice, and medical image-based interaction. It helps users understand symptoms, analyze medical scans, and receive structured healthcare guidance more effectively. The implemented system demonstrated reliable healthcare assistance capabilities while reducing dependency on unreliable symptom searching methods. The multilingual speech interaction module also improved accessibility for users from different language backgrounds. Although the system does not replace professional medical diagnosis, it can support early health awareness, symptom understanding, and healthcare decision assistance. In the future, the system can be enhanced with: · Real-time doctor consultation · Cloud-based medical record integration · Advanced disease prediction models · Mobile healthcare application support · Improved AI-based clinical reasoning Overall, the project demonstrates how multimodal AI technologies can be effectively used to build intelligent healthcare assistance platforms for modern digital healthcare environments.
· Shashi Kant Ojha, Abhishek Kumar, Tanvi Bhole, Sheenam Naaz, “Rule-Based AI Chatbot,” IJIRT, Vol. 11, Issue 5, 2024. · X. Zhang et al., “Explainable Deep Learning in Medical Imaging: Challenges and Opportunities,” IEEE Reviews in Biomedical Engineering, 2025. · Chen et al., “Multimodal Deep Learning for Healthcare: A Comprehensive Review,” IEEE Access, 2023. · H. Yang et al., “Medical Chatbots for Patient Interaction: Current Trends and Challenges,” IEEE Access, 2023.
Publication Date
6/8/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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No funding information available for this research.
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