commentary

Artificial intelligence (AI) is increasingly integrated into healthcare, especially within diagnostic radiology. AI algorithms, particularly those utilizing machine learning and computer vision, are widely used for evaluating MRI, CT, and radiographic images. These models, trained on extensive patient databases, are now fully commercialized. Ongoing research compares AI’s efficacy to human radiologists in diagnosing various pathologies, such as scoliosis, hip dysplasia,

Recently, Kemper et al. published an insightful paper on the challenges and opportunities of health technology assessment (HTA) models to evaluate the value, safety, and trustworthiness of radiology computer vision AI (RCVAI) tools used in daily practice. Formal HTA frameworks are theoretically accurate and help define complex issues, but they are ideal approaches. The real world is messier. Ideal descriptions

Huisman et al. highlight the context-dependent value of AI in radiology. In their article, “The Emperor Has Few Clothes: A Realistic Appraisal of Current AI in Radiology,” they present a critical perspective on AI’s actual impact in the Western setting. Merel Huisman, a radiologist and clinical epidemiologist with a special focus on AI, sees it as her mission to caution

In the near future, I believe AI support systems will play a crucial role in the daily practice of radiologic reading. Radiologists are faced with an ever-increasing number of examinations and it remains vital to identify urgent cases as quickly as possible. An initial certified support system on the market has reported interesting performance data that proved the positive effect