Arjmandnia Fatemeh, Alimohammadi Ehsan
Department of Aneasthesiology, Kermanshah University of Medical Sciences, Kermanshah, Iran.
Department of Neurosurgery, Kermanshah University of Medical Sciences, Imam Reza Hospital, Kermanshah, Iran.
Patient Saf Surg. 2024 Mar 25;18(1):11. doi: 10.1186/s13037-024-00393-0.
Machine learning algorithms have the potential to significantly improve patient safety in spine surgeries by providing healthcare professionals with valuable insights and predictive analytics. These algorithms can analyze preoperative data, such as patient demographics, medical history, and imaging studies, to identify potential risk factors and predict postoperative complications. By leveraging machine learning, surgeons can make more informed decisions, personalize treatment plans, and optimize surgical techniques to minimize risks and enhance patient outcomes. Moreover, by harnessing the power of machine learning, healthcare providers can make data-driven decisions, personalize treatment plans, and optimize surgical interventions, ultimately enhancing the quality of care in spine surgery. The findings highlight the potential of integrating artificial intelligence in healthcare settings to mitigate risks and enhance patient safety in surgical practices. The integration of machine learning holds immense potential for enhancing patient safety in spine surgeries. By leveraging advanced algorithms and predictive analytics, healthcare providers can optimize surgical decision-making, mitigate risks, and personalize treatment strategies to improve outcomes and ensure the highest standard of care for patients undergoing spine procedures. As technology continues to evolve, the future of spine surgery lies in harnessing the power of machine learning to transform patient safety and revolutionize surgical practices. The present review article was designed to discuss the available literature in the field of machine learning techniques to enhance patient safety in spine surgery.
机器学习算法有潜力通过为医疗保健专业人员提供有价值的见解和预测分析,显著提高脊柱手术中的患者安全性。这些算法可以分析术前数据,如患者人口统计学信息、病史和影像学研究,以识别潜在风险因素并预测术后并发症。通过利用机器学习,外科医生可以做出更明智的决策,个性化治疗方案,并优化手术技术以降低风险并改善患者预后。此外,通过利用机器学习的力量,医疗保健提供者可以做出数据驱动的决策,个性化治疗方案,并优化手术干预,最终提高脊柱手术的护理质量。研究结果凸显了在医疗环境中整合人工智能以降低手术实践中的风险并提高患者安全性的潜力。机器学习的整合在提高脊柱手术患者安全性方面具有巨大潜力。通过利用先进算法和预测分析,医疗保健提供者可以优化手术决策、降低风险并个性化治疗策略,以改善预后并确保为接受脊柱手术的患者提供最高标准的护理。随着技术不断发展,脊柱手术的未来在于利用机器学习的力量来改变患者安全性并彻底改变手术实践。本综述文章旨在讨论机器学习技术领域中可用于提高脊柱手术患者安全性的现有文献。