人工智能能否成为解决精神分裂症带来的巨大挑战和痛苦的未来方案?

IF 3 Q2 PSYCHIATRY
Shijie Jiang, Qiyu Jia, Zhenlei Peng, Qixuan Zhou, Zhiguo An, Jianhua Chen, Qizhong Yi
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摘要

本研究评估了人工智能(AI)在精神分裂症(SZ)的诊断、治疗和预后评估中的潜力,并探索了AI在未来医学创新中的应用协同方向。SZ是一种严重的精神障碍,会给患者带来巨大的痛苦和挑战。随着机器学习和深度学习技术的快速发展,人工智能在高危人群的早期诊断方面已经显示出显著的优势。通过整合患者的多维生物标志物和语言行为数据,人工智能可以进一步提供客观、精确的诊断标准。此外,它有助于制定个性化的治疗方案,提高治疗效果,并为难治性SZ患者提供新的治疗策略。此外,人工智能擅长制定个性化的预后计划,可以快速识别疾病进展,准确预测疾病轨迹,及时调整治疗策略,从而改善预后,促进康复。尽管人工智能在SZ管理中的巨大潜力,但必须强调其作为辅助工具的作用,医疗保健专业人员的临床判断和富有同情心的护理仍然至关重要。未来的研究应侧重于优化人机交互,以实现人工智能在深圳管理中的高效应用。人工智能技术与临床实践的深度融合将推动SZ领域的发展,最终改善患者的生活质量和治疗效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Can artificial intelligence be the future solution to the enormous challenges and suffering caused by Schizophrenia?

This study evaluated the potential of artificial intelligence (AI) in the diagnosis, treatment, and prognostic assessment of schizophrenia (SZ) and explored collaborative directions for AI applications in future medical innovations. SZ is a severe mental disorder that causes significant suffering and imposes challenges on patients. With the rapid advancement of machine learning and deep learning technologies, AI has demonstrated notable advantages in the early diagnosis of high-risk populations. By integrating multidimensional biomarkers and linguistic behavior data of patients, AI can provide further objective and precise diagnostic criteria. Moreover, it aids in formulating personalized treatment plans, enhancing therapeutic outcomes, and offering new therapeutic strategies for patients with treatment-resistant SZ. Furthermore, AI excels in developing individualized prognostic plans, which enables the rapid identification of disease progression, accurate prediction of disease trajectory, and timely adjustment of treatment strategies, thereby improving prognosis and facilitating recovery. Despite the immense potential of AI in SZ management, its role as an auxiliary tool must be emphasized, with clinical judgment and compassionate care from healthcare professionals remaining crucial. Future research should focus on optimizing human-machine interactions to achieve efficient AI application in SZ management. The in-depth integration of AI technology into clinical practice will advance the field of SZ, ultimately improving the quality of life and treatment outcomes of patients.

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