Artificial Intelligence in Interventional Radiology: A Literature Review and Future Perspectives.

Area of Science:

• Radiology
• Artificial Intelligence
• Interventional Radiology

Background:

• Artificial intelligence (AI) encompasses algorithms performing human-like intelligence tasks autonomously.
• Advancements in AI are driven by artificial neural networks (ANN), machine learning (ML), and deep learning (DL).

Purpose of the Study:

• To explore the promising applications of AI in radiology.
• To highlight AI's role in interventional radiology (IR) and interventional oncology.
• To provide guidance for IR trainees and practicing physicians on AI in locoregional treatments.

Main Methods:

• Integration of evidence-reported literature.
• Inclusion of experience-based perceptions.

Main Results:

• AI demonstrates potential in enhancing lesion detection, segmentation, and interpretation in radiology.
• AI applications are emerging in interventional radiology, including prognostic information for oncology procedures.

Conclusions:

• AI holds significant promise for advancing interventional radiology practices.
• This review assists professionals in understanding and adopting AI for locoregional treatments.