Chang Min Cheol, Kim Jeoung Kun, Park Donghwi, Kim Jang Hwan, Kim Chung Reen, Choo Yoo Jin
Department of Rehabilitation Medicine, College of Medicine, Yeungnam University, Daegu 42415, Republic of Korea.
Department of Business Administration, School of Business, Yeungnam University, Gyeongsan-si 38541, Republic of Korea.
Healthcare (Basel). 2023 Oct 6;11(19):2687. doi: 10.3390/healthcare11192687.
Applications of machine learning in the healthcare field have become increasingly diverse. In this review, we investigated the integration of artificial intelligence (AI) in predicting the prognosis of patients with central nervous system disorders such as stroke, traumatic brain injury, and spinal cord injury. AI algorithms have shown promise in prognostic assessment, but challenges remain in achieving a higher prediction accuracy for practical clinical use. We suggest that accumulating more diverse data, including medical imaging and collaborative efforts among hospitals, can enhance the predictive capabilities of AI. As healthcare professionals become more familiar with AI, its role in central nervous system rehabilitation is expected to advance significantly, revolutionizing patient care.
机器学习在医疗保健领域的应用日益多样化。在本综述中，我们研究了人工智能（AI）在预测中风、创伤性脑损伤和脊髓损伤等中枢神经系统疾病患者预后方面的整合情况。人工智能算法在预后评估中显示出了前景，但在实现更高的预测准确性以用于实际临床应用方面仍存在挑战。我们建议积累更多样化的数据，包括医学影像数据，并加强医院之间的合作，这可以提高人工智能的预测能力。随着医疗保健专业人员对人工智能越来越熟悉，预计其在中枢神经系统康复中的作用将显著推进，给患者护理带来变革。
