X-rAI: Revolutionizing Chest X-Ray Analysis with Deep Learning

About The Project

X-rAI leverages the power of deep learning to generate comprehensive medical reports from chest X-rays. This technology significantly saves time for healthcare professionals, enhances patient care, and contributes to advancements in medical diagnosis. By enabling quicker assessments, X-rAI aims to improve health outcomes and address the current limitations in chest radiograph reporting, which often lack comprehensiveness and can lead to delays and misdiagnoses. Our project focuses on enhancing diagnostic efficiency and improving record management through advanced deep learning, providing detailed textual reports for better patient care.


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Project Impact

Automated Report Generation

X-rAI streamlines the report generation process, allowing radiologists to concentrate on more complex cases.

Reduced Diagnostic Errors

Our technology enhances accuracy in diagnosing lung conditions by providing consistent report generation. X-rAI acts as a second opinion for radiologists, highlighting anomalies that might be missed.

Timely Results

X-rAI provides faster diagnostic reports, leading to quicker medical interventions.

Accessibility in Remote Areas

The project makes advanced healthcare diagnostics more accessible in underserved regions, ensuring timely and accurate health assessments.

Improved Patient Care

By offering timely and accurate diagnostics, X-rAI contributes to better patient outcomes and overall improved healthcare quality.

Schematic Diagram of the Project:




Dr. Muhammad Naseer Bajwa

Assistant Professor, Dept of AI & Data Science


Dr. Zuhair Zafar

Assistant Professor, Dept of AI & Data Science


Team Members

Saad Subhani
Ali Ammar
Saif Ali

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