ArticleMethodsX2026
TBAid: A domain-restricted diagnostic assistant for tuberculosis awareness and patient support using OpenRouter API Integration.
Article in MethodsX, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.