Islamic International Medical College, Rawalpindi, MBBS, Pakistan.
Neurosurg Rev. 2024 Oct 8;47(1):753. doi: 10.1007/s10143-024-03020-9.
This letter addresses the importance of enhancing post-craniotomy care for primary brain tumor patients by leveraging insights from Rongqing Li et al.'s study on symptom networks. The study identified key central and bridge symptoms, such as sadness and difficulty understanding, which influence post-surgical recovery and quality of life. It also highlighted that patients with noninvasive tumors showed more cohesive symptom networks compared to those with invasive tumors. However, the study had limitations, including a short observation period and reliance on self-reported data, which restricted the depth of the findings.To optimize recovery, integrating artificial intelligence (AI) and machine learning (ML) could revolutionize post-craniotomy care. AI can assist with surgical planning, predict complications, and monitor recovery through wearable devices and real-time alerts. Natural Language Processing (NLP) can improve symptom detection from electronic health records, enhancing clinical decision-making. Despite the potential of these technologies, ethical concerns regarding data privacy and AI-generated report accuracy must be addressed. Future research should focus on long-term outcomes and refining AI applications to improve post-craniotomy symptom management and overall patient outcomes.
这封信强调了利用 Rongqing Li 等人关于症状网络的研究来加强原发性脑肿瘤患者开颅术后护理的重要性。该研究确定了关键的核心和桥梁症状，如悲伤和理解困难，这些症状会影响手术后的恢复和生活质量。它还强调，与侵袭性肿瘤患者相比，非侵袭性肿瘤患者的症状网络更具凝聚力。然而，该研究存在一些局限性，包括观察期短和依赖于自我报告数据，这限制了研究结果的深度。为了优化恢复，人工智能 (AI) 和机器学习 (ML) 的整合可以彻底改变开颅术后护理。AI 可以通过可穿戴设备和实时警报来辅助手术规划、预测并发症并监测恢复情况。自然语言处理 (NLP) 可以从电子健康记录中提高症状检测能力，从而增强临床决策。尽管这些技术具有潜力，但必须解决数据隐私和 AI 生成报告准确性方面的道德问题。未来的研究应侧重于长期结果，并改进 AI 应用程序，以改善开颅术后症状管理和整体患者结果。
