ABSTRACT:

This paper outlines the development of a web application that integrates iris recognition technology with a patient appointment management system. The application uses Convolutional Neural Networks for accurate iris detection, enhancing patient identification. It includes features for user registration, secure login, appointment scheduling, and doctor management. Patients who upload their iris images are matched with their previous medical records, allowing the system to present a list of relevant specialists based on their medical history. Patients can then select a specialist and book an appointment directly with that doctor. The backend is built using Flask, and data is stored in an SQLite database. This setup ensures that appointment details and doctors' prescriptions are carefully recorded and maintained. By combining iris recognition with traditional appointment management, the system streamlines the booking process, improves patient identification, and maintains comprehensive medical records, thus enhancing the efficiency of healthcare services.

INTRODUCTION:
The incorporation of biometric technologies into healthcare systems has become increasingly ubiquitous, offering enhanced security and precision in patient identification. Among these technologies, distinguished by its exceptional fraudulent activities. The genesis of iris recognition technology can be traced back to the late 1980s, with significant contributions from researchers like John Daugman, who pioneered algorithms for expeditious iris pattern matching.
This technology exploits the distinct and stable, making it a dependable biometric identifier. In the domain of patient identification, existing methodologies frequently utilize traditional approaches such as RFID tags, barcode systems, and facial recognition. While these approaches provide certain advantages, they are limited by issues such as lower accuracy, potential for duplication, and susceptibility to security breaches. Furthermore, conventional
appointment management systems in healthcare environments often depend on manual processes or basic digital frameworks that do not incorporate sophisticated biometric authentication, potentially resulting in inaccuracies in patient record management and inefficiencies in appointment scheduling.