Artificial intelligence-assisted reader evaluation in acute CT head interpretation (AI-REACT): a multireader multicase study

Objective To assess whether an artificial intelligence (AI) tool improves the accuracy, speed and confidence of general radiologists, emergency clinicians and radiographers in detecting critical non-contrast CT head (NCCTH) abnormalities and to evaluate its stand-alone performance and factors influe...

Полное описание

Библиографические подробности

Главные авторы:	Jason Oke, Laura Hunter, Michael Thompson, Mark Harrison, Louise Hartley, Kyle Stephenson, Benjamin Scally, David Lowe, Alex Novak, Nick Woznitza, Hannah Yang, Tilak Das, Ruchir Shah, Fiona Hunter, Ana Nicolescu, Josh Beck, Ross Hunter, Sarim Ather, Shair Ali, Nazreen Kaneez, Kavitha Vimalesvaran, Rhys Worgan, Ravi Shashikala, Dennis Robert, Shamie Kumar, Kanika Bhatia, Andrea Romsauerova, Mariapaola Narbone, Rahul Dharmadhikari, Jane Gooch, Nabeeha Salik, Haris Shuaib, Avneet Gill, Abdala T Espinosa Morgado, Satish Golla, Irfan Ullah Akbarkhan, Emma Kelliher, Danielle Benson, Ryan Grech, Martine Walker, Neil Mitchell, Alice Gibson, Hélène Matte, Shubhendu Kulshrestha, Radoslaw Rippel, Roland Amoah, Zahi Qamhawi, Thomas Millard, Harsh Merchant, Ben Lockwood
Формат:	Artigo
Язык:	Inglês
Опубликовано:	BMJ Publishing Group 2026-03-01
Серии:	BMJ Digital Health & AI
Online-ссылка:	https://bmjdigitalhealth.bmj.com/content/2/1/f000071.full
Метки:	Добавить метку Нет меток, Требуется 1-ая метка записи!