Cost-Effectiveness of AI-Assisted Kellgren-Lawrence Grading of Knee Osteoarthritis in the South Korean Health-Care System.

BACKGROUND: Early diagnosis of knee osteoarthritis (KOA) is often delayed due to reliance on subjective interpretation of radiographs. Recent advances in artificial intelligence (AI)-based automated Kellgren-Lawrence (KL) grading offer the potential to improve diagnostic accuracy and enable earlier nonoperative treatment. However, the cost-effectiveness of such AI tools has not been comprehensively evaluated. This study aimed to assess the economic value of AI-assisted KL grading in comparison with conventional radiographic assessment by health-care professionals. METHODS: We developed a model-based cost-effectiveness analysis comparing AI-assisted KL grading versus conventional human-reader assessment. A hybrid decision tree and Markov model simulated disease progression and treatment pathways in a hypothetical cohort of South Korean adults aged ≥50 years. Outcomes included lifetime costs, quality-adjusted life years (QALYs), and incremental cost-effectiveness ratios (ICERs). Analyses were performed from a health-care system perspective. Deterministic and probabilistic sensitivity analyses (DSAs and PSAs) were conducted to evaluate the effects of uncertainty. RESULTS: The AI-assisted strategy yielded a total cost of $137,710 and 22.721 QALYs, compared with $140,834 and 22.461 QALYs for usual care. This resulted in cost savings of $3,125 and a QALY gain of 0.260, leading to an ICER of -$12,031 per QALY gained. One-way sensitivity analysis showed that the model was most sensitive to AI-related costs and the effectiveness of nonoperative treatment. PSA demonstrated a 55.1% probability of the AI-assisted strategy being cost-effective at a willingness-to-pay threshold of $34,642. CONCLUSIONS: AI-assisted radiographic grading of KOA demonstrated potential cost savings and a favorable cost-effectiveness profile compared with usual care. However, given the uncertainty reflected in the PSA, its integration into routine imaging workflows should be approached cautiously and supported by further validation in real-world clinical settings. LEVEL OF EVIDENCE: Economic and Decision Analysis Level II. See Instructions for Authors for a complete description of levels of evidence.