Need to Increase Facetime Between Patients and Clinician

Need to Increase Facetime Between Patients and Clinician

Harish Kumar K S1,Y Amarnath Chowdhary2, Charan G3, Deekshith K A4, K R Vishnu Kumar5 ,Vignesh R 6

1 Associate Professor, Dept. of Computer Science and Engineering, Presidency University, Bengaluru, Karnataka, India

2,3,4,5,6UG Student, Dept. of Computer Science and Technology, Presidency University, Bengaluru, Karnataka, India

Harishkumar@presidencyuniversity.in1,Yendluri.20211CST0012@presidencyuniversity.in2,Charan.20211CST0060@presidencyuniversity.in3,Deekshith.20211CST0068@presidencyuniversity.in4,Vishnu.20211CST0106@presidencyuniversity.in5,Vignesh.20211CST0135@presidencyuniversity.in6.

1.     ABSTRACT:
We will propose the development and implementation of two novel medical question-and-answer systems that will make use of advanced NLP and image analysis techniques to improve the accuracy and relevance of medical domains. The first system will utilize the RAG approach for answering text-based queries by retrieving context from multiple medical PDFs and generating informed responses. The second framework integrates both text and medical image data, including X-rays and MRIs, to provide complete, contextually rich answers. Both models are designed to answer domain-specific medical queries, focusing on scalability, precision, and real-time performance. This paper discusses the design, implementation, and evaluation of these systems, outlines their strengths and weaknesses, and suggests future improvements such as multi-modal transformers and domain-specific fine-tuning. Proposed systems will be beneficial in enhancing the medical decision-making processes with timely, reliable information acquired from textual as well as visual sources