Emergency Doctor Appointment Using AI

Abstract

In modern healthcare systems, timely access to medical attention during emergencies remains a significant challenge due to overcrowded hospitals, inefficient scheduling, and limited availability of specialists. This paper proposes an Emergency Doctor Appointment Using AI designed to optimize patient triage, appointment scheduling, and doctor allocation in real time. The system employs machine learning algorithms and natural language processing (NLP) to assess patient symptoms, determine the severity of medical conditions, and recommend appropriate specialists or emergency care pathways. By integrating patient data, hospital resource availability, and geolocation services, the AI model can dynamically prioritize critical cases and connect patients with nearby doctors or emergency departments within minutes. The proposed solution enhances healthcare accessibility, reduces waiting times, and improves resource utilization in emergency scenarios. Simulation results and prototype testing demonstrate the potential of the AI system to revolutionize emergency healthcare delivery by enabling faster, smarter, and more efficient doctor-patient interactions.

Introduction

Modern healthcare often struggles to provide timely support in emergencies due to long queues, manual scheduling, inefficient triage, and lack of real-time doctor availability. Delays in treatment can cause serious complications or even be life-threatening.

Project Goal:
The “Emergency Doctor Appointment Using AI” system aims to create an intelligent platform that analyzes patient symptoms, determines emergency severity, and connects users to the nearest available doctors instantly. It integrates AI, Machine Learning (ML), Natural Language Processing (NLP), telemedicine, and location-based services for efficient emergency healthcare delivery.

Key Objectives:

• Instant symptom-based triage into low, moderate, or high emergencies.

• Automated recommendation of suitable doctors or specialists.

• Real-time appointment booking with doctor availability.

• GPS-based identification of nearest hospitals or clinics.

• Smart queue management to reduce waiting time.

• Telemedicine support for remote consultation.

• Secure storage of patient medical history and notifications for appointments or emergencies.

Methodology:

• Users enter symptoms via web or mobile app.

• AI chatbot analyzes symptoms and urgency level.

• System checks doctor availability and suggests nearest hospitals.

• Appointment is booked, and confirmation sent to patient.

System Implementation:

• Frontend: Built with HTML, CSS, JavaScript, Bootstrap; responsive UI with interactive Google Maps and AI chatbot sidebar.

• Backend: Flask handles booking, geo-location, AI processing, and chatbot integration.

• Data Handling: Temporary in-memory storage for bookings; ready for database integration.

• AI Chatbot: Provides guidance, recommends doctors, answers queries, and assists in booking.

• Geo-location & Maps: Google Maps API identifies nearest hospitals, displays markers, and highlights closest doctor.

• UI/UX Design: Modern, premium feel using glassmorphism and consistent color themes.

Benefits:

• Reduces waiting time and manual errors.

• Provides instant, accurate triage and doctor recommendation.

• Enhances patient safety and healthcare efficiency.

• Bridges the gap between patients and healthcare providers in emergencies.

References

[1] TensorFlow Documentation. (n.d.). Retrieved from https://www.tensorflow.org/ This source provides detailed information about AI model building, training, and deployment, which was useful for developing the symptom analysis module in the project. [2] Scikit-learn Documentation. (n.d.). Retrieved from https://scikit-learn.org/stable/ Used as a reference for implementing machine learning algorithms for patient data classification and urgency prediction. [3] Dialogflow Documentation. (n.d.). Retrieved from https://cloud.google.com/dialogflow/docs Helpful for designing the AI chatbot interaction system used to collect and analyze patient symptoms. [4] MySQL Documentation. (n.d.). Retrieved from https://dev.mysql.com/doc/ Provided guidance on creating and managing the project database for storing patient, doctor, and appointment information. [5] Google Maps Platform Documentation. (n.d.). Retrieved from https://developers.google.com/maps/documentation used for integrating location-based services, enabling users to find the nearest available emergency doctor or hospital and visualize routes using the map API. [6] AI-Driven Doctor Scheduling System Research Paper. (2025). Retrieved from https://irjaeh.com/index.php/journal/article/view/924 This paper supported the conceptual design of the AI scheduling model, including location-based hospital search, specialist recommendation, and optimized appointment allocation. [7] Outpatient Scheduling Using Deep Reinforcement Learning. (2024). Retrieved from https://link.springer.com/article/10.1007/s10791-024-09474-1 This study supported the appointment-optimization logic for efficient doctor allocation, especially during emergency or high-demand periods. [8] Google Firebase Documentation. (n.d.). Retrieved fromhttps://firebase.google.com/docs Useful for integrating authentication, cloud storage, and real-time database components support secure patient login and appointment data synchronization.

Copyright

Copyright © 2025 Mr. Pralhad Bamroliya , Mr. Piyush Chopade , Mr. Deepak Bhave , Mr. Rohan Nandanwar, Prof. Kalpana Dongre. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.