MediGuard: A Safety-First AI-Assisted Healthcare Companion Platform

Abstract: Access to reliable health information, routine self-monitoring and appropriate specialist guidance remains uneven, and general-purpose chatbots can produce unsafe or overconfident medical statements. This paper presents CareBot AI, a modular web platform that combines a conversational health assistant with a deterministic safety layer, a vitals tracker, a medicine and hydration manager, a medical-report analyser based on optical character recognition (OCR), a rule-based specialist recommender, a hospital finder and a transfer-learning pipeline for X-ray classification. The system uses a Flask REST back end with JWT authentication and SQLAlchemy persistence, and a plain HTML/CSS/JavaScript front end. A central design principle is that emergency detection, measurement-threshold checks and disclaimers are rule- based and independent of the language model, and that every component that is not yet configured reports its status honestly instead of fabricating output. An automated suite of 55 tests, covering authentication, per-user data isolation, input validation, safety behaviour and graceful degradation, passes on the implemented back end. The paper describes the architecture, module design and safety mechanisms, and discusses current limitations and the validation required before any clinical use.

Keywords: Healthcare chatbot, AI health assistant, safety layer, medical report OCR, X-ray analysis, transfer learning, Flask REST API, health monitoring