# TBAid: A domain-restricted diagnostic assistant for tuberculosis awareness and patient support using OpenRouter API Integration

## TL;DR

TBAid is a chatbot designed to provide tuberculosis awareness and health advice, especially for low-resource areas.

## Contribution

The dual-explanation capability for both patients and healthcare workers is a novel feature of TBAid.

## Key findings

- TBAid uses a rule-based system and Hugging Face API for TB-focused responses.
- The chatbot can be deployed locally or online, making it accessible in rural areas.
- The system is modular and can integrate with CT-based TB diagnostic models.

## Abstract

This research introduces a study of a domain-specific intelligent assistant, TBAid, that is a conversational chatbot to assist with tuberculosis (TB) awareness and health advice. A structured rule-based system integrated with the Hugging Face Inference API using the Qwen/Qwen2.5-72B-Instruct large language model provides TB-focused responses to structured user queries. TBAid is designed to increase public awareness in low-resource and rural areas. It specifically targets communities with poor access to specialist consultations and medical report interpretation. A key novelty of the assistant is its dual-explanation capability, which can frame responses for a non-expert user (e.g., a patient) or provide a medically precise version for healthcare workers. This ensures answers are both accessible and clinically safe by remaining strictly domain-relevant. While the chatbot does not currently analyze images directly, its architecture is designed for future integration. It can accept predictive outputs from a separate, pre-existing CT-based TB classification model. It has a user interface written in HTML, CSS, and JavaScript, and can be deployed on GitHub as a static web app or a local Flask server. This framework enables real-time access and secure decision-making. It is modular, scalable, and can be integrated with AI-based medical diagnostics in the future.•Combines rule-based logic and conversational AI for domain-specific TB support.•Enhances accessibility through lightweight, local, and online deployments.•Supports modular expansion for integration with CT-based diagnostic outputs.

Combines rule-based logic and conversational AI for domain-specific TB support.

Enhances accessibility through lightweight, local, and online deployments.

Supports modular expansion for integration with CT-based diagnostic outputs.

Image, graphical abstract