Arpitha Rani,
Konagala Anudeep,
Shwetha Gowth,
- Assistant Professor, Department of Cyber Security, Malla Reddy University (Private University), Hyderabad, Telangana, India
- Student, Department of Cyber Security, Malla Reddy University (Private University), Hyderabad, Telangana, India
- Student, Department of Cyber Security, Malla Reddy University (Private University), Hyderabad, Telangana, India
Abstract
The rapid digitization of healthcare has led to an unprecedented growth in medical data, ranging from diagnostic images and laboratory reports to electronic health records and clinical notes. Despite this abundance, patients and healthcare providers often struggle to extract meaningful insights due to data complexity and fragmentation. MedVerse AI proposes an intelligent digital health platform that unifies medical image analysis, clinical report interpretation, real-time interaction, and predictive disease analytics into a single ecosystem. The system integrates artificial intelligence models with interactive Power BI dashboards to support proactive healthcare decision-making, including heart attack and multi- disease risk prediction. By transforming raw medical data into actionable insights, MedVerse AI shifts healthcare from a reactive treatment model to a preventive and patient-empowered approach. This flatform mainly gives you an idea before going to the doctor and in many cases, we cannot properly communicate with the doctor, by using Medverse AI we can have rough sketch about the main diagnosis of the prescription by this the overall customer satisfaction can be fulfilled. The integrity of the data is the topmost priority if the application and your data are deleted whenever you logout form the application or the website. We are making sure to consider the most accurate and relative analysis, especially your sensitive medical data, many AI scores are also available that describes how much the AI is confident about the outcome and the suggestion. We ensure proper analysis and prediction based on the data you give us and include many factors so that a person with least amount of medical knowledge can also easily understand the prediction we are giving and can analyze the outcomes we provide. Based on the given references and articles we trained our model to give accurate predictions we mainly used the past medical reports specifically for power BI reports we used mainly the end year medical analysis given by WHO, newspapers and other by this we know the places intensity and many more this plays an important role in the process. We also included many trending AI tools – like using Google AI Studio Grok and many more – for more accurate results. This will ensure the overall action and prediction of the data you give us. Mainly we ensure proper structured diagnosis of the data you give in and make you understand even the minute detail. This will help the overall processing of the website and the application.
Keywords: Digital healthcare, artificial intelligence, disease prediction, medical image analysis, predictive analytics
[This article belongs to International Journal of Biomedical Innovations and Engineering ]
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International Journal of Biomedical Innovations and Engineering
| Volume | 04 | |
| Issue | 02 | |
| Received | 01/06/2026 | |
| Accepted | 14/07/2026 | |
| Published | 25/08/2026 | |
| Publication Time | 85 Days |