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Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
During an ultrasonography procedure, a handheld device called...
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Clinical Applications, Challenges & Pitfalls, and Recommendations for Large Language Model and Generative AI in

Jiwoo Park, Ji Hyun Lee, Min A Yoon

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    Generative AI shows promise in musculoskeletal imaging and reporting, but challenges like hallucination and bias require careful management for safe clinical use.

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    Area of Science:

    • Artificial Intelligence in Medicine
    • Radiology and Medical Imaging
    • Musculoskeletal Imaging

    Background:

    • Generative AI, encompassing models like GANs, diffusion models, and LLMs, is increasingly applied in clinical settings.
    • Applications include disease diagnosis, image enhancement, reconstruction, EHR summarization, and report generation in musculoskeletal imaging.
    • Integration into radiology workflows offers potential for improved reporting and patient communication.

    Purpose of the Study:

    • To review the current evidence on generative AI's utility in musculoskeletal imaging and radiologic reporting.
    • To identify and discuss the challenges and pitfalls associated with generative AI implementation.
    • To provide recommendations for future advancements and clinical translation.

    Main Methods:

    • Systematic review of existing literature on generative AI in musculoskeletal radiology.
    • Analysis of applications, benefits, and limitations of various generative AI models.
    • Synthesis of findings to inform future research and clinical practice.

    Main Results:

    • Generative AI demonstrates significant potential in enhancing diagnostic accuracy, image quality, and reporting efficiency.
    • Key challenges include AI hallucination, data bias, performance drift, and data privacy concerns.
    • Domain-specific training and robust validation are crucial for reliable LLM application in radiology.

    Conclusions:

    • Generative AI offers transformative potential for musculoskeletal imaging and radiology reporting.
    • Addressing challenges through rigorous validation, domain-specific training, and privacy measures is essential for safe adoption.
    • Further research and development are needed to optimize generative AI for widespread clinical translation.