HQAI-TB: A Multimodal Hybrid Quantum–Classical Artificial Intelligence Framework for Tuberculosis Diagnosis from Chest Radiographs and Clinical Records

Tuberculosis (TB) is one of the most dangerous and lethal infectious external agents. In highly prone countries, the diagnosis of TB is hindered by three interconnected bottlenecks: low-quality X-rays, lack of expert readers, and clinical information locked away in bilingual or multilingual paper records and not in digital versions. There have been many AI solutions to address these bottlenecks and challenges in silos: chest X-ray classifiers that incorporate clinical information from radiology reports as covariates, document digitization pipelines that largely ignore imaging findings, and hybrid quantum-classical related learning systems that have not yet been tested on TB diagnosis with integrated clinical records. In this paper, we propose a multi-modal framework with four clinically deployable modules, QASP-Net, which conditions the images and enhances on the estimated quality descriptors, called HQAI-TB. VQC-ResNet is a combination, and it works. In VQC-ResNet works and combines. In this paper, we propose HQAI-TB, which is a multi-modal framework consisting of four clinically deployable modules, QASP-Net, which conditions the images and improves the estimated quality descriptors. VQC-ResNet: a hybrid which operates on a residual convolutional encoder and a parameterized variational quantum circuit. TBScript-OCR that converts bilingual or multilingual clinical documents into digital data and finally TB-ICD-BERT that extracts entities and makes it easy for ICD diagnostic coding. A diagnostic probability and a structured clinical summary are output by an attention-weighted fusion layer. We describe the formalism of all the components and analyze the pipeline cost. Final clinical validation will determine ultimate utility to patient care, but we describe a pre-specified validation protocol: corpora are fixed (800 public domain frontal views, 138 Montgomery, 662 Shenzhen), reference standards hold, and five ablation configurations are compared with standard statistical tests. We give one initial quantitative result: TBScript-OCR, initialized from an off-the-shelf transformer-based recognizer, and fine-tuned on a 500-form institutional pilot corpus, recognized approximately $82$ of characters ($18$ percent error rate) on the printed bilingual set at one site. We report this as a preliminary finding and note that an $18$ character error would be unacceptable for downstream diagnostic coding without the ICD ontology filter, we propose here.