Track 21: Artificial Intelligence in Radiology

Artificial Intelligence in Radiology
Artificial Intelligence (AI) in radiology involves the use of advanced computer algorithms to support the analysis, interpretation, and management of medical images. By learning from large datasets of labeled images, AI systems can detect disease patterns and assist radiologists in making accurate and timely clinical decisions. The most commonly used techniques are machine learning and deep learning, particularly Convolutional Neural Networks (CNNs), which are highly effective in visual image analysis.

Applications in Imaging
AI is widely applied across major imaging modalities. In X-ray imaging, AI can detect pneumonia, fractures, tuberculosis, and heart enlargement. In CT scans, it assists in identifying stroke, tumors, internal bleeding, and lung nodules. In MRI, AI enhances image reconstruction, detects brain lesions and spinal abnormalities, and improves soft tissue evaluation. In Ultrasound, AI helps guide real-time scanning and reduces operator dependency. For PET-CT, AI supports cancer staging and treatment response evaluation.

Role in Radiology Workflow
AI prioritizes emergency cases, highlights abnormal regions for radiologists, generates structured reports, and tracks disease changes over time. This improves efficiency, reduces reporting delays, and minimizes human errors caused by fatigue or high workload.

Advantages
AI improves diagnostic accuracy, speeds up interpretation, ensures consistency in reporting, and contributes to better patient outcomes.

Challenges
Challenges include data security, ethical concerns, algorithm bias, and the requirement for high-quality training datasets. Successful implementation also requires staff training and technological integration.

Conclusion
AI does not replace radiologists. Instead, it works as a supportive tool, enhancing precision and allowing radiologists to focus on complex decision-making and patient care.