Study aim: Development and evaluation of the effectiveness of an AI-based educational–clinical platform as a “second reader” for Kellgren–Lawrence grading in knee radiography: a randomized controlled trial Design: Two-arm parallel-group randomized controlled trial, single-center, individual randomization, 1:1 allocation, multi-reader multi-case design, with pretest and posttest Settings and conduct: This randomized clinical trial evaluates the diagnostic performance and accuracy of a deep learning (AI) algorithm in grading knee osteoarthritis using Kellgren-Lawrence (KL) criteria, comparing AI-assisted interpretations against human readers using standard digital knee radiographs. Participants/Inclusion and exclusion criteria: Eligible participants are interns and residents of orthopedics, physical medicine and rehabilitation, and radiology in training years 1–3, without prior structured training or substantial experience in Kellgren–Lawrence grading or AI-based diagnostic support systems. Inclusion criteria include written informed consent, stable internet and computer access, and ability to complete the pretest and posttest within the study period. Exclusion criteria include incomplete pretest or posttest, failure to receive the minimum educational dose, withdrawal of consent, or any technical/operational issue preventing study completion Intervention groups: Standard education plus an AI-based educational–clinical “second reader” platform providing Kellgren–Lawrence grading, confidence estimation, and explainable heatmap feedback; the control group receives standard education without AI output Main outcome variables: Diagnostic accuracy of deep learning algorithm for Kellgren-Lawrence grading of knee osteoarthritis Inter-observer agreement (Cohen's kappa) between AI predictions and expert radiologist readings