Artificial intelligence and multimodal imaging in orthopaedics: from technological advances to clinical translation

TL;DR

This paper reviews how combining AI with multiple imaging techniques is transforming orthopaedics by enabling more accurate and personalized diagnosis and treatment.

Contribution

The paper highlights the translational challenges and recent advances in multimodal AI for orthopaedics, emphasizing the need for standardized data and ethical frameworks.

Findings

Multimodal AI improves fracture detection, osteoarthritis assessment, and tumor differentiation.

AI-assisted tools support preoperative planning and personalized treatment decisions.

Challenges include data heterogeneity, model generalization, and clinical integration.

Abstract

The integration of multimodal medical imaging with artificial intelligence (AI) is potentially catalysing a paradigm shift in orthopaedic diagnosis and treatment, moving beyond experience-based practices toward intelligent, data-driven precision medicine. This narrative review synthesizes recent key evidence across imaging modalities and AI frameworks, and highlights the translational gap that persists between algorithmic development and real-world clinical implementation. By combining complementary information from X-ray, CT, MRI, PET, ultrasound, and biochemical data, multimodal AI overcomes the inherent limitations of single-modality approaches, enabling more comprehensive structural, functional, and metabolic assessments. Recent advances demonstrate broad applications, including accurate fracture detection and classification, differentiation of benign and malignant bone tumours, quantitative assessment of osteoarthritis, risk prediction for osteoporosis, and intelligent preoperative planning and intraoperative navigation. Moreover, multimodal AI facilitates efficacy prediction and personalised treatment decision-making, positioning future systems as AI-assisted decision-support tools that support surgeons in surgical strategy, implant design, and long-term follow-up. Nevertheless, significant challenges remain, particularly in data heterogeneity, model generalisation, interpretability, and clinical integration. Progress in constructing standardised multimodal databases, developing self-supervised and multi-task learning strategies, and strengthening ethical–regulatory frameworks will be essential for clinical translation. Ultimately, multimodal AI holds immense potential to transition from laboratory validation to routine practice, delivering safer, more efficient, and precise diagnostic and therapeutic solutions for orthopaedic patients.

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