Performantie van AI bij fractuurdetectie en effect op de prestaties van artsen: een systematische review

Abstract

Performance of AI in fracture detection on radiography and its effect on the performance of physicians: a systematic review This systematic review has a twofold objective regarding the evaluation of the use of artificial intelligence (AI) for fracture detection on radiography. The first is to examine the performance of the current AI algorithms. The second concerns an evaluation of the effect of AI support on the performance of physicians in fracture detection. A systematic literature search was performed in 4 databases: PubMed, Embase, Web of Science and CENTRAL. Fourteen studies met the inclusion and exclusion criteria. The studies were divided into 2 categories: a first group in which a comparison was made between the performance of AI and the performance of physicians and a second group comparing the performance of physicians with and physicians without AI aid. Seven studies reported a comparable or superior fracture detection performance for AI compared to physicians, including radiologists. One study established a comparable performance on the internal test. On the external test, a lower AI performance was found compared to physicians. The second group of 6 studies reported a positive effect on the fracture detection performance of physicians when aided by AI. The current AI algorithms have a fracture detection performance comparable with physicians. At present, AI can be used as an aid in fracture detection. The potential impact of AI as an aid is greater with regard to less experienced doctors. The biggest hurdle of the current AI algorithms is the lack of large quantities of high-quality training data. Prospective studies, as well as further development and training of detection algorithms are needed in the future, in addition to larger datasets.