[The future of computer-aided diagnostics in chest computed tomography].

Area of Science:

• Medical Imaging and Artificial Intelligence
• Radiology and Machine Learning

Background:

• Artificial intelligence (AI) is gaining traction in medical applications.
• Radiology, with its digital nature and reproducible medical diagnostic images, is well-suited for machine learning integration.
• The thorax is a common area for computed tomography (CT), especially with lung cancer screening programs.

Purpose of the Study:

• To review the potential applications of machine learning in chest computed tomography (CT).
• To address the increasing workload for radiologists due to a rise in CT scans.
• To highlight how AI can aid in the interpretation of medical images.

Main Methods:

• Literature review of machine learning applications in radiology.
• Focus on AI in chest CT analysis.
• Discussion of image analysis for radiologists.

Main Results:

• Machine learning shows significant promise for automating image analysis in radiology.
• AI can help manage the increasing volume of chest CT data.
• The high reproducibility of medical diagnostic images supports ML implementation.

Conclusions:

• Machine learning offers valuable tools for enhancing the interpretation of chest CT scans.
• AI can assist radiologists in handling large datasets from lung cancer screening.
• The integration of AI in radiology is a growing and important field.