Artificial Intelligence in Medicine With Emphasis on Orthopedic Practice

Abstract

This paper presents a comprehensive review of the impact of artificial intelligence (AI) on healthcare, with a particular focus on its applications in orthopedic medicine. AI technologies, notably large language models (LLMs) and machine learning (ML) algorithms, have increasingly enhanced diagnostic accuracy, clinical decision-making, and personalized patient care. The integration of AI into medical education and training fosters improved learning outcomes through interactive simulations and tailored curricula. In orthopedics, AI-driven imaging tools employing deep learning techniques demonstrate superior performance in fracture detection, cartilage segmentation, and musculoskeletal injury diagnosis, often rivaling expert human clinicians. Moreover, AI models show promise in automating medical history taking and supporting diagnostic workflows, although real-world implementation remains limited by data quality, system integration, and ethical concerns. The evaluation of LLMs such as ChatGPT in standardized medical examinations reveals both their potential and current limitations in clinical reasoning. Ethical and technical challenges, including data bias, accountability, privacy, and the need for transparency, emerge as critical considerations for safe AI adoption in clinical practice. The future of AI in orthopedics lies in multidisciplinary collaboration, robust validation, and the development of multimodal models that integrate diverse biomedical data, paving the way for precision medicine and enhanced patient outcomes.

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