Evaluating Equity in AI-Supported Functional Assessment: Agreement Between Clinician Judgment and Digital Metrics in Stroke Rehabilitation

Ravi Shankar Reddy, Khalid A. Alshamrani, Batool Abdulelah Alkhamis, Mohammad A. ALMohiza, Ahmed Mohamed Elshiwi, Ghada Mohamed Koura, Saleh M. Kardm, Debjani Mukherjee, Zayed H. Alabbas, Husam Alfaifi, Abdulaziz Matiq, Khaled Awad Saleh Al-Qahtani, Department of Medical Rehabilitation Sciences, College of Applied Medical Sciences, King Khalid University, Saudi Arabia, Department of Health Rehabilitation Sciences, College of Applied Medical Sciences, King Saud University, Saudi Arabia, Department of Physiotherapy, Saudi German Hospital, Saudi Arabia, Department of Surgery, College of Medicine, Najran University, Saudi Arabia, Department of Physical Therapy, College of Applied Medical Sciences, Najran University, Saudi Arabia, Department of Medical Rehabilitation Sciences, College of Applied Medical Sciences, Najran University, Saudi Arabia

Evaluation & the Health Professions

Published online on May 28, 2026

Abstract

Evaluation & the Health Professions, Ahead of Print.
Evaluating agreement between AI-generated functional assessment metrics and clinician judgment in stroke rehabilitation is important for understanding how AI-supported evaluation tools may complement clinical practice. This cross-sectional study examined ...