azmed Rayvolve Trauma https://radiology.healthairegister.com/products/azmed-rayvolve/ Gasmi I, Calinghen A, Parienti JJ, Belloy F, Fohlen A, Pelage JP. Comparison of diagnostic performance of a deep learning algorithm, emergency physicians, junior radiologists and senior radiologists in the…

carebot AI Bones Hruby, R., Kvak, D., Dandar, J., Atakhanova, A., Misar, M., & Dufek, D. (2024). Cross-Center Validation of Deep Learning Model for Musculoskeletal Fracture Detection in Radiographic Imaging: A Feasibility Study. medRxiv, 2024-01. https://doi.org/10.1101/2024.01.17.24301244

carebot AI CXR https://radiology.healthairegister.com/products/carebot-ai-cxr/ Kvak, D.; Chromcová, A.; Biroš, M.; Hrubý, R.; Kvaková, K.; Pajdaković, M.; Ovesná, P. Chest X-ray Abnormality Detection by Using Artificial Intelligence: A Single-Site Retrospective Study of Deep Learning Model…

Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement from the ACR, CAR, ESR, RANZCR and RSNARadiology: Artificial IntelligenceVol. 6, No. 12024.01.22.DOI: 10.1148/ryai.230513   Developing, purchasing, implementing and…

van Leeuwen, K.G., Schalekamp, S., Rutten, M.J.C.M. et al.Artificial intelligence in radiology:100 commercially available products and their scientific evidence.Eur Radiol 31, 3797–3804 (2021). https://doi.org/10.1007/s00330-021-07892-z   A radiológiában használt mesterséges intelligencia (AI)…