MEDICAL CHATBOT FOR DIAGNOSIS AND DOCTOR RECOMMENDATION USING DEEP LEARNING

Abstract

In recent years, the advancement of artificial
intelligence (AI) and deep learning technologies has
revolutionized various industries, including
healthcare. This project proposes the development of
a medical chatbot aimed at facilitating diagnosis and
recommending suitable doctors to users based on
their symptoms and medical history. Leveraging deep
learning techniques, the chatbot analyzes user-input
data in natural language to accurately assess potential
medical conditions. Through a neural network
architecture, it continuously learns and improves its
diagnostic capabilities over time. Additionally, the
chatbot incorporates data on healthcare providers,
including specialties, expertise, and availability, to
recommend appropriate doctors for further
consultation and treatment. The implementation of
this medical chatbot has the potential to enhance
accessibility to healthcare services, aid users in
making informed healthcare decisions, and
contribute to the advancement of AI in healthcare
this project proposes the development of a Medical
Chatbot for Diagnosis and Doctor Recommendation
using Deep Learning.
The objective of this project is to leverage advanced
AI algorithms to create a medical chatbot capable of
analyzing user-input symptoms and medical history to
provide accurate diagnoses and recommend suitable
doctors for further consultation. The chatbot will
utilize deep learning techniques to comprehend
natural language queries, enabling users to interact
with it in a conversational manner, similar to
communicating with a human healthcare provider.
The motivation behind this project stems from the
need to address several challenges in the current
healthcare system. Access to healthcare services is
often limited by factors such as geographical location,
availability of healthcare professionals, and long wait
times for appointments. Additionally, misdiagnosis
and delayed diagnosis remain significant concerns,
leading to ineffective treatment and patient
dissatisfaction