Comprehensive Summary
This peer-reviewed paper discusses how artificial intelligence is reshaping radiology by improving diagnostic accuracy, workflow efficiency, and patient care across imaging modalities. The authors describe how deep learning convolutional neural networks have achieved near–expert radiologist performance in detecting and classifying lesions on X-ray, CT, MRI, and ultrasound. The best deep learning model was ConvNeXt which had a 90-99% accuracy for most osteoarthritis classification features. Beyond diagnosis, AI tools are optimizing workflow through automated image triage, report generation, and radiation dose optimization, which reduces reporting delays and clinician workload. However, most current systems remain limited by retrospective validation, data bias, lack of explainability, and inconsistent regulatory oversight. Many models perform well only in controlled settings and have not been tested across diverse populations or institutions.
Outcomes and Implications
For the medical community, this review underscores that AI, rather than becoming a replacement for clinicians, should be used as a key partner in radiology. AI tools can help manage increasing imaging volumes by automating repetitive tasks, ultimately improving diagnostic speed and consistency. These advances could free radiologists to focus on complex interpretations and patient-centered decision-making. However, the article also emphasizes the need for safeguards to limit risks such as biased results, overreliance on untested algorithms, and unequal performance across patient groups could undermine trust and safety.