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Artificial Intelligence in Medicine With Emphasis on Orthopedic Practice
Jakub Bartkowski1, Julia Zerdka2, Patryk Brasse2
1Medicine, Miejskie Zakłady Opieki Zdrowotnej w Żorach, Żory, POL.
Cureus
|January 5, 2026
Summary
Artificial intelligence (AI), including large language models (LLMs) and machine learning (ML), is transforming orthopedic medicine by improving diagnostics and patient care. Challenges like data quality and ethics must be addressed for successful AI integration.
Area of Science:
- Orthopedic Medicine
- Artificial Intelligence
- Medical Technology
Background:
- Artificial intelligence (AI) is increasingly impacting healthcare, particularly in specialized fields like orthopedic medicine.
- AI technologies, including machine learning (ML) and large language models (LLMs), offer potential advancements in diagnostics, clinical decision-making, and personalized patient care.
- The integration of AI into medical education is also being explored to enhance learning outcomes.
Purpose of the Study:
- To provide a comprehensive review of AI's impact on healthcare, focusing on its applications in orthopedic medicine.
- To evaluate the performance of AI-driven tools in orthopedic diagnostics and identify areas for improvement.
- To discuss the potential and limitations of LLMs in medical contexts and address ethical considerations for AI adoption.
Main Methods:
- Review of current literature on AI applications in healthcare and orthopedics.
- Analysis of AI-driven imaging tools, deep learning techniques, and ML algorithms in orthopedic diagnostics.
- Evaluation of LLMs in standardized medical examinations and discussion of implementation challenges.
Main Results:
- AI, particularly deep learning, shows high accuracy in orthopedic imaging tasks like fracture detection and cartilage segmentation, often matching expert clinicians.
- AI models demonstrate potential in automating tasks like medical history taking and supporting diagnostic workflows.
- LLMs show promise but also limitations in clinical reasoning, as evidenced in medical examinations.
Conclusions:
- AI offers significant potential to enhance orthopedic medicine through improved diagnostics, personalized care, and medical education.
- Key challenges for AI adoption include data quality, system integration, ethical concerns (bias, privacy, accountability), and transparency.
- Future advancements require multidisciplinary collaboration, robust validation, and multimodal AI models for precision medicine and better patient outcomes.
