Check out the project I submitted during my final year of my Bachelor of Technology in Computer Science Engineering under the research cluster "Optimization Techniques in Machine Learning Associated with Medical Applications and Bioinformatics".

Project Abstract:

Medical image analysis is essential for disease diagnosis and treatment, yet it often relies on specialized radiologists, causing delays, increased costs, and errors due to fatigue. Limited access to diagnostic resources in under-resourced areas further exacerbates healthcare disparities. Current AI tools, such as Computer Aided Detection (CAD) systems, support radiologists by flagging abnormalities but lack adaptability and contextual understanding, often requiring extensive labeled data and failing to provide follow-up recommendations. The AI Medical Assistant utilizes large language models (LLMs) and GenAI to enhance medical image analysis. This application not only detects anomalies but also offers expert-level analysis, structured findings, follow-up recommendations, and potential treatment options, thereby supporting healthcare professionals. Key features include comprehensive multi-image analysis, a user-friendly interface, and context-aware recommendations. By reducing reliance on human intervention, the AI Medical Assistant extends diagnostic capabilities to under-served regions and assists patients in obtaining second opinions, fostering confidence in their diagnoses. Ethical safeguards ensure responsible AI use, making this tool a scalable, cost-effective solution that enhances diagnostic accuracy and accessibility, ultimately promoting equity in healthcare delivery.

Content Overview: 

➛ Problem Statement
➛ Product Scope and Applications
➛ Requirements Elicitation and Analysis: Functional, Non-functional & System Requirements
➛ Feasibility Study: Technical, Operational, Legal, Economic, Scheduling
➛ SRS Table
➛ System Design: Object-Oriented Analysis & UML Diagrams
➛ User Interface Design
➛ Software Stack & Technologies Used
➛ Implementation Details of AI Algorithm (Transformer-based LLM)
➛ Workflow and Prompt Engineering
➛ Test Reports : Functional & Non-functional Test Cases
➛ Validation by Expert Doctors & Medical Professionals
➛ References & Appendix
➛ Input/Output Screens
➛ Sample Code