Table 3.

Overview of AI applications in medical imaging.

Imaging Modality	Application	Example of AI System	Impact
MRI	AI applications in MRI analysis encompass detection of brain abnormalities, tumors, strokes, neurodegenerative diseases, and more. AI can analyze images and quantify the volume of affected areas.	An AI system analyzes MRI images to detect brain abnormalities, such as tumors or strokes, and quantifies their volume, aiding in treatment planning [118].	Improved detection of tumors, strokes, and neurodegenerative diseases; quantification of affected areas aids in treatment planning and disease monitoring.
CT	AI in CT scan interpretation includes detecting lung nodules, identifying fractures and hemorrhages, assessing stroke severity, and characterizing tumor progression. AI systems can process CT scans rapidly and accurately, aiding in timely diagnosis.	An AI model diagnoses lung cancer with high accuracy and reduced false positives, improving diagnostic precision [119].	Faster detection of life-threatening conditions; enhanced accuracy compared to traditional methods; potential to save lives in emergency situations.
X-ray	AI applications in X-ray enhance image analysis for tumor detection, improving accuracy and reducing false positives and negatives. AI systems serve as a second reviewer, enhancing the sensitivity of cancer screening.	AI-based CAD algorithms significantly improve radiologists’ sensitivity in breast cancer detection, reducing false negatives and improving cancer detection rates [120].	Increased sensitivity in detecting breast cancer lesions; reduction in false positives and negatives; enhancement of radiologists’ diagnostic accuracy.
Ultrasound	AI aids in analyzing echocardiography scans to assess cardiovascular function and detect structural abnormalities of the heart. AI systems measure parameters such as ejection fraction and aid in diagnosing and managing heart diseases.	A novel AI algorithm accurately calculates left ventricular ejection time in echocardiography, providing reliable metrics for cardiac function assessment [121].	Accurate assessment of cardiovascular parameters; reduction in user-dependent variability; enhancement of clinical utility in echocardiography.