Elif Baran, Melissa Lee, Steven Aviv, Jessica Weiss, Chris Pettengell, Irene Karam, Andrew Bayley, Ian Poon, Kelvin K W Chan, Ambica Parmar, Martin Smoragiewicz, Hagen Klieb, Tra Truong, Pejman Maralani, Danny J Enepekides, Kevin M Higgins, Antoine Eskander
IMPORTANCE: Accurate, timely, and cost-effective methods for staging oropharyngeal cancers are crucial for patient prognosis and treatment decisions, but staging documentation is often inaccurate or incomplete. With the emergence of artificial intelligence in medicine, data abstraction may be associated with reduced costs but increased efficiency and accuracy of cancer staging. OBJECTIVE: To evaluate an algorithm using an artificial intelligence engine capable of extracting essential information from medical records of patients with oropharyngeal cancer and assigning tumor, nodal, and metastatic stages according to American Joint Committee on Cancer eighth edition guidelines...
May 16, 2024: JAMA Otolaryngology—Head & Neck Surgery