MediSense AI – Smart Health Analysis System

Year : 2025 | Volume : 14 | Issue : 03 | Page : 20 30
    By

    Sapna Kumari,

  • Rakesh Dhar,

  • Mukesh Kumar,

  1. Student, Department of Computer Applications Echelon Institute of Technology Faridabad, Haryana, India
  2. Student, Department of Computer Applications Echelon Institute of Technology Faridabad, Hariyana, India
  3. Student, Echelon Institute of Technology Faridabad, Hariyana, India

Abstract

MediSense AI is a revolutionary health analysis system that empowers users by transforming complex medical data into understandable insights. This platform utilizes advanced technologies, particularly natural language processing and machine learning, to make intricate medical terminologies accessible to individuals without a healthcare background. Leveraging Llama 3, a cutting-edge AI model developed by Meta AI, the system can analyze various forms of medical data, including the ability for users to upload medical images and PDF documents. For the purpose of extracting text from scanned documents and images, a software tool, Tesseract OCR is used, which uses a machine learning algorithm to analyze them. Upon processing these inputs, the platform generates AI-driven results and personalized health recommendations, reinforcing the user’s understanding of their health conditions. The backend framework, built with either FastAPI or Flask, efficiently manages user requests and integrates seamlessly with a dynamic frontend that employs HTML5, CSS3, and TailwindCSS for an engaging user experience. Users can interact directly with the system through a user-friendly interface, facilitating easy uploads and queries. Local deployments are supported via Ollama CLI tools, allowing for agile testing and iteration. Key libraries such as requests and essential tools like Postman and Docker enhance the development experience. MediSense AI ultimately serves as a valuable resource, equipping patients and healthcare providers with actionable insights and fostering better health management through the intelligent analysis of medical information.

Keywords: Artificial Intelligence, Natural Language Processing, Medical Report Analysis, Machine Learning, Healthcare System, Disease Prediction, Tesseract OCR, Personalized Recommendations.

[This article belongs to Research and Reviews : A Journal of Medical Science and Technology ]

How to cite this article:
Sapna Kumari, Rakesh Dhar, Mukesh Kumar. MediSense AI – Smart Health Analysis System. Research and Reviews : A Journal of Medical Science and Technology. 2025; 14(03):20-30.
How to cite this URL:
Sapna Kumari, Rakesh Dhar, Mukesh Kumar. MediSense AI – Smart Health Analysis System. Research and Reviews : A Journal of Medical Science and Technology. 2025; 14(03):20-30. Available from: https://journals.stmjournals.com/rrjomst/article=2025/view=228436


References

  1. Raj PP, Ranjan AK, Vaishnav G, Kumar KN, Kundra S. MediSense: A smart disease prediction and drug recommendation system leveraging machine and deep learning techniques. In: Proc Int Conf on Intelligent Systems.
  2. Lu HY, Ding X, Hirst JE, Yang Y, Yang J, Mackillop L, Clifton DA. Digital health and machine learning technologies for blood glucose monitoring and management of gestational diabetes. IEEE Rev Biomed Eng. 2023;17:98–117.
  3. Gao X, He P, Zhou Y, Qin X. Artificial intelligence applications in smart healthcare: A survey. Future Internet. 2024;16(9):308. doi:10.3390/fi16090308
  4. Nasr M. Artificial intelligence in healthcare: Addressing privacy and local AI. arXiv. 2021;arXiv:2107.03924.
  5. Esther D, Johnson W. AI and robotics synergy in smart hospital systems. Journal Article. 21 March 2025.
  6. Falkner S. Smart biosensors: AI-driven analysis for personalized medicine and dentistry. 24 March 2025;1:1–6.
  7. Panahi O, Eslamlou S. Artificial intelligence in oral surgery: Enhancing diagnostics, treatment, and patient care. J Dent Oral Care. 2025;3:1–5.
  8. Bravo J, Hervás R, Fontecha J, Gonzalez I. M-health: Lessons learned by m-experiences. Sensors. 2018;18(5):1569.
  9. Coutinho PCED. Employment of artificial intelligence mechanisms for e-health systems in order to obtain vital signs and detect diseases from medical images improving the processes of online consultations and diagnosis. 2022.
  10. Antunes CFPDCM. Employment of artificial intelligence mechanisms for e-health systems in order to obtain vital signs and detect diseases from medical images improving the processes of online consultations and diagnosis. Master’s thesis. 2022.
  11. Garzón Mohammed LF. Control inteligente del perfil glucémico para diabéticos.
  12. Gunton JE, McElduff A. Improvement of glycemic control after treatment with mosapride for diabetic gastropathy. Diabetes Care. 2000;23(8):1197.
  13. Ishikawa R, Yuzawa T, Fukiage T, Kagesawa M, Watsuji T, Oishi T. Visibility enhancement of lesion regions in chest X-ray images with image fidelity preservation. IEEE Access. 2025.
  14. Shekhar S, Yadav AK, Khosla A. Challenges and opportunities for developing electrochemical biosensors with commercialization potential in the point-of-care diagnostics market. 2024.

Regular Issue Subscription Original Research
Volume 14
Issue 03
Received 10/07/2025
Accepted 22/08/2025
Published 30/09/2025
Publication Time 82 Days


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