European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
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Integrating machine learning models into electronic medical record systems can greatly enhance decision-making, patient outcomes, and value-based care in healthcare systems. Challenges related to data accessibility, privacy, and sharing can impede the development and deployment of effective predictive models in spine surgery. Federated learning (FL) offers a decentralized approach to machine learning that allows local model training while preserving data privacy, making it well-suited for healthcare settings. Our objective was to describe federated learning solutions for enhanced predictive modeling in spine surgery. ⋯ Federated learning shows great promise in revolutionizing predictive modeling in spine surgery by addressing the challenges of data privacy, accessibility, and sharing. The applications of FL in telesurgery, AI-based predictive models, and medical image segmentation have demonstrated their potential to enhance patient outcomes and value-based care.
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Comparative Study
ChatGPT versus NASS clinical guidelines for degenerative spondylolisthesis: a comparative analysis.
Clinical guidelines, developed in concordance with the literature, are often used to guide surgeons' clinical decision making. Recent advancements of large language models and artificial intelligence (AI) in the medical field come with exciting potential. OpenAI's generative AI model, known as ChatGPT, can quickly synthesize information and generate responses grounded in medical literature, which may prove to be a useful tool in clinical decision-making for spine care. The current literature has yet to investigate the ability of ChatGPT to assist clinical decision making with regard to degenerative spondylolisthesis. ⋯ This study sheds light on the duality of LLM applications within clinical settings: one of accuracy and utility in some contexts versus inaccuracy and risk in others. ChatGPT was concordant for most clinical questions NASS offered recommendations for. However, for questions NASS did not offer best practices, ChatGPT generated answers that were either too general or inconsistent with the literature, and even fabricated data/citations. Thus, clinicians should exercise extreme caution when attempting to consult ChatGPT for clinical recommendations, taking care to ensure its reliability within the context of recent literature.
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Comparative Study
Deep-learning reconstructed lumbar spine 3D MRI for surgical planning: pedicle screw placement and geometric measurements compared to CT.
To test equivalency of deep-learning 3D lumbar spine MRI with "CT-like" contrast to CT for virtual pedicle screw planning and geometric measurements in robotic-navigated spinal surgery. ⋯ Deep-learning 3D MRI facilitates equivalent virtual pedicle screw placements and geometric assessments for most lumbar vertebrae, with the exception of vertebral body length at L1, L2, and L4, compared to CT for pre-operative planning in patients considered for robotic-navigated spine surgery.
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Large language models (LLM) have recently attracted attention because of their enormous performance. Based on artificial intelligence, LLM enable dialogic communication using quasi-natural language that approximates the quality of human communication. Thus, LLM could play an important role for patients to become informed. To evaluate the validity of an LLM in providing medical information, we used one of the first high-performance LLM (ChatGPT) on the clinical example of acute lumbar disc herniation (LDH). ⋯ With the incipient use of artificial intelligence in communication, LLM will certainly become increasingly important to patients. Even if LLM are unlikely to play a role in clinical communication between physicians and patients at the moment, the opportunities-but also the risks-of this novel technology should be alertly monitored.