The need for better quality studies: A systematic scoping review of current utility of artificial intelligence in orthopaedics and research gaps in the knee joint.

Abstract

The application of artificial intelligence (AI) in orthopaedic research, particularly for knee conditions, is growing rapidly. While AI offers the potential to enhance the efficiency and precision of care, its role and impact remain underexplored. This systematic scoping review evaluates studies employing AI algorithms, including machine learning and deep learning, to support clinical decision making for knee diseases. A scoping review was conducted using the Joanna Briggs Institute methodology and PRISMA-ScR guidelines. MEDLINE, EMBASE, and ISI Web of Science were searched for English-language studies published between 2008 and 2025. Eligible studies involved adult patients and applied AI to diagnose knee conditions, predict outcomes, or support clinical processes. Extracted data included study demographics, targeted knee conditions, AI algorithms used, and their applications. From 2761 studies screened, 816 (30 %) were included after title and abstract screening. Among these, 66 % addressed diagnosis and 34 % focused on clinical prediction. Osteoarthritis was the most studied condition (71 %), followed by soft tissue damage (15 %). Deep learning was the most utilized AI method (43 %), followed by traditional machine learning (39 %). Among excluded studies, AI was indirectly applied in 21 %, primarily for identification (25 %), segmentation (42 %), and measurement (17 %). Over the past 16 years, AI use in knee orthopaedic research has grown, yet only 30 % of studies directly addressed diagnostic or predictive applications. Challenges include limited reproducibility, generalizability, and clinical applicability of AI models. Future research should focus on improving reporting standards and exploring the application of AI in intra-operative, post-operative, and non-imaging use cases to enhance clinical utility.