International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | AI Powered Medical Assistant for Personalized Diagnosis and Hospital Navigation |
|---|---|
| ABSTRACT | The increasing demand for accessible and accurate healthcare services has created a need for intelligent systems that can support diagnosis, guidance, and communication for diverse patient populations. This work introduces MediAssist, an AI-powered medical assistant that leverages multimodal artificial intelligence to analyze medical images and voice-based symptom inputs, generate personalized disease assessments, and provide evidence-based treatment suggestions. With integrated multilingual support and real-time geolocation capabilities, the system overcomes language and accessibility barriers by recommending nearby hospitals specializing in the diagnosed condition and offering clear, patient-friendly explanations of medical findings. By streamlining the diagnostic process and enhancing healthcare navigation, MediAssist aims to empower users, improve clinical decision-making efficiency, and contribute to more equitable global healthcare access. |
| AUTHOR | GURUPRASAD P PISHE, KUSHAL K C, NISHITH S, BHUVANA H N, RAHIMA B U.G. Student, Department of CS&E, Bapuji Institute of Engineering and Technology, Davanagere, Karnataka, India Assistant Professor, Department of CS&E, Bapuji Institute of Engineering and Technology, Davanagere, Karnataka, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312015 |
| pdf/15_AI Powered Medical Assistant for Personalized Diagnosis and Hospital Navigation.pdf | |
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