"Enhancing post-craniotomy recovery: leveraging AI and network analysis for improved outcomes".

Abstract

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.