“It is possible that the relatively large sample size in this study bolstered readers’ confidence in the AI’s suggestions,” said study lead author Chang Min Park, MD, PhD, from the Department of Radiology and Institute of Radiation Medicine at Seoul National University College of Medicine. Use of high-accuracy AI also led to more frequent changes in reader determinations-a concept known as susceptibility. Use of the high accuracy AI improved readers’ detection performance to a greater extent than low-accuracy AI. The readers were blind to the fact that two different AIs were used. In a second session, each group reinterpreted the X-rays, assisted by either a high- or low-accuracy AI. Of the 120 chest radiographs assessed, 60 were from lung cancer patients (32 males) and 60 were controls (36 males). In this retrospective study, 30 readers, including 20 thoracic radiologists with five to 18 years of experience and 10 radiology residents with only two to three years of experience, assessed 120 chest X-rays without AI. Researchers at Seoul National University in Korea looked at how these factors might influence the detection of malignant lung nodules during AI-assisted reading of chest X-rays. While AI-based image diagnosis has advanced rapidly in the medical field, the factors affecting radiologists’ diagnostic determinations in AI-assisted image reading remain underexplored. Assistance from an AI algorithm with high diagnostic accuracy improved radiologist performance in detecting lung cancers on chest X-rays and increased human acceptance of AI suggestions, according to a study published in Radiology.
0 kommentar(er)
