Multimodal Framework for Automated Chest X-ray Diagnosis and Report Generation
Year: 2023
Type: conference-paper
Abstract: Chest radiography accounts for 30–40% of all radiological examinations worldwide, yet its clinical effectiveness is constrained by inter-observer variability, persistent human error, and a global shortage of radiologists. This paper presents a modular fou
Source: Sworld-Us conference proceedings
Authors: Amina Baiuzak, Aislu Kassekeyeva
Institution: Astana IT University
Topic: COVID-19 diagnosis using AI
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W7204268752
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