Artificial intelligence and photon‐counting CT: the three phases of the software–hardware revolution in radiology

Comprehensive Summary

This article explores how artificial intelligence and photon-counting CT (PCCT) are aiding radiology in image interpretation and clinical decision making. AI recognizes patterns in imaging data, allowing detection and prediction of disease. PCCT counts individual x-ray photons and measures their energy, producing higher resolution images with less noise and more data than traditional CT images. The integration of AI into radiology is described in three stages: assistance, partnership, and autonomy. In the assistance stage, AI acts as a tool to perform small tasks, such as circling possible tumors while the radiologist makes the diagnosis. In the second stage, partnership, AI analyzes by combining patient history with lab and scan data, suggesting possible diagnoses. In the final stage, autonomy, AI can prioritize scan by analyzing images before doctors and send alerts about possible medical concerns. In every stage, AI would be overseen by a radiologist to monitor errors. Limitations highlighted include limited data sets to train AI models, possibilities of over reliance on AI, and the inability of AI to fully understand clinical context.

Outcomes and Implications

The integration of AI and PCCT into radiology will improve efficiency and diagnostic accuracy, but will still rely on radiologists to provide clinical oversight.