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Pulmonary Tuberculosis V01:28

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Medical management of tuberculosis (TB) patients involves a comprehensive approach that includes diagnosis, treatment, and monitoring. The specific strategies can vary depending on the type of tuberculosis (latent or active), the patient's overall health status, and other considerations.
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Tuberculosis, often called TB, is a contagious illness primarily caused by Mycobacterium tuberculosis. It mainly affects the lung parenchyma but can also impact other body parts.
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TBAid: A domain-restricted diagnostic assistant for tuberculosis awareness and patient support using OpenRouter API

Lakshmi Ravi Teja Meka1, Dalwinder Singh1, Arun Singh1

  • 1Department of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, India.

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This study introduces TBAid, a conversational chatbot providing tuberculosis (TB) health advice and awareness, especially for rural areas. Its dual-explanation feature makes TB information accessible to both patients and healthcare professionals.

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Area of Science:

  • Artificial Intelligence
  • Public Health
  • Medical Informatics

Background:

  • Tuberculosis (TB) awareness and access to health advice are limited in low-resource and rural areas.
  • Effective interpretation of medical reports and specialist consultations are often inaccessible in underserved communities.
  • There is a need for accessible, domain-specific tools to disseminate accurate TB information.

Purpose of the Study:

  • To develop and evaluate TBAid, a domain-specific intelligent assistant for tuberculosis (TB) awareness and health advice.
  • To enhance public health outreach in resource-limited settings through a conversational chatbot.
  • To provide dual-explanation capabilities for both non-expert users and healthcare professionals.

Main Methods:

  • A structured rule-based system integrated with the Hugging Face Inference API and the Qwen/Qwen2.5-72B-Instruct large language model.
  • Development of a user interface using HTML, CSS, and JavaScript for deployment as a static web app or local Flask server.
  • Architecture designed for future integration with AI-based medical diagnostics, including CT-based TB classification models.

Main Results:

  • TBAid provides TB-focused responses to structured user queries, combining rule-based logic with conversational AI.
  • The chatbot offers dual-explanation capabilities, tailoring responses for patients and healthcare workers.
  • The system is modular, scalable, and deployable in low-resource settings, enhancing accessibility to TB information.

Conclusions:

  • TBAid effectively combines rule-based logic and conversational AI to offer domain-specific tuberculosis support.
  • The chatbot enhances accessibility to health information through lightweight, local, and online deployment options.
  • The modular framework supports future integration with advanced AI diagnostics, improving TB management and awareness.