Table 1.

Strength and Limitations of Artificial Intelligence in Radiology

Strengths	Limitations
● Automated lesions screening, detection, segmentation, and characterization by using input data from other modalities (eg, x-ray, CT, MRI).	● AI-based applications not familiar with the global context of patients.
● Classify images based on the presence or absence of abnormality.	● Training data time, cost, and resource consuming.
● Extract additional data from previous detected abnormality (eg, lesion)	● Lack the power of supervised algorithms.
● Identification of anatomical landmarks or organs, which are important for both image acquisition and analysis.	● Lack of accurate validation of the AI applications during training which may lead to random noise than the actual data.
● Detecting scan planes for rapid examination planning and minimum interindividual variability, bias and scanning time.	● Lack of specific multidisciplinary road maps for AI-based application implementation in medical imaging field.