收稿日期: 2025-01-15
录用日期: 2025-04-07
网络出版日期: 2025-10-14
基金资助
国家自然科学基金(82130041)
Applications and challenges of generative artificial intelligence in psychiatry
Received date: 2025-01-15
Accepted date: 2025-04-07
Online published: 2025-10-14
Supported by
National Natural Science Foundation of China(82130041)
精神障碍是全球公共卫生领域的重要挑战，影响大量人群的生活质量，也给社会带来沉重的卫生负担；然而当前社会提供精神障碍预防、诊断和治疗的能力与需求之间仍存在较大差距。近年来，人工智能（artificial intelligence，AI）技术的发展与应用为提高人类精神卫生服务水平带来契机。生成式AI作为近年来发展最快的AI领域之一，在分析各种形式的数据方面发挥了至关重要的作用，包括医学图像处理、蛋白质结构预测、临床文档生成、辅助诊断判别、临床决策支持等，增强了临床诊断、数据重建和辅助治疗的能力。该综述重点介绍生成式AI技术在促进精神医学基础研究突破、识别精神障碍早期风险因素、辅助临床医师诊断与治疗精神障碍等方面的潜在应用前景，同时讨论当前条件下生成式AI技术应用于精神卫生领域的过程中面临的偏见、隐私泄露、可解释性不足等挑战与局限性。最后，该综述总结了提升AI精神健康服务能力的方法，让新技术服务于降低全球精神卫生疾病负担，并改善精神障碍患者的生存质量。
宋毅杰 , 陈天真 , 钟娜 , 赵敏 . 生成式人工智能在精神医学中的应用与挑战[J]. 上海交通大学学报（医学版）, 2025 , 45(10) : 1271 -1278 . DOI: 10.3969/j.issn.1674-8115.2025.10.002
Mental disorders pose a significant challenge to global public health, profoundly affecting the quality of life of a vast number of individuals and imposing a heavy health burden on society. Nonetheless, there remains a substantial gap between the current societal capacity to provide prevention, diagnosis, and treatment for mental disorders and the existing demand for such services. In recent years, the development and application of artificial intelligence (AI) technologies have provided unprecedented opportunities to enhance mental healthcare services. As one of the fastest-growing fields of AI, generative AI has played a pivotal role in analyzing diverse forms of data, including medical image processing, protein structure prediction, clinical document generation, auxiliary diagnostic discrimination, and clinical decision support. These advancements have significantly strengthened capabilities in clinical diagnosis, data reconstruction, and adjunctive therapeutic interventions. This review highlights the potential applications of generative AI in advancing fundamental psychiatric research, identifying early risk factors for mental disorders, and assisting clinicians in diagnosis and treatment. Additionally, it addresses the challenges and limitations currently facing the application of generative AI to mental healthcare, including biases, privacy breaches, and insufficient interpretability. Finally, the review summarizes strategies to enhance AI's capacity to deliver mental health services, aiming to leverage new technologies to reduce the global burden of mental disorders and improve the quality of life of affected individuals.
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