Hector Institute for Artificial Intelligence in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.
Am J Med Genet B Neuropsychiatr Genet. 2024 Dec;195(8):e32997. doi: 10.1002/ajmg.b.32997. Epub 2024 Jun 21.
Psychiatric disorders have a complex biological underpinning likely involving an interplay of genetic and environmental risk contributions. Substantial efforts are being made to use artificial intelligence approaches to integrate features within and across data types to increase our etiological understanding and advance personalized psychiatry. Network science offers a conceptual framework for exploring the often complex relationships across different levels of biological organization, from cellular mechanistic to brain-functional and phenotypic networks. Utilizing such network information effectively as part of artificial intelligence approaches is a promising route toward a more in-depth understanding of illness biology, the deciphering of patient heterogeneity, and the identification of signatures that may be sufficiently predictive to be clinically useful. Here, we present examples of how network information has been used as part of artificial intelligence within psychiatry and beyond and outline future perspectives on how personalized psychiatry approaches may profit from a closer integration of psychiatric research, artificial intelligence development, and network science.
精神障碍具有复杂的生物学基础，可能涉及遗传和环境风险因素的相互作用。目前正在大力利用人工智能方法整合不同数据类型内部和之间的特征，以提高我们对病因的理解并推进个性化精神病学的发展。网络科学为探索不同生物学组织层次（从细胞机制到大脑功能和表型网络）之间的复杂关系提供了一个概念框架。有效地利用此类网络信息作为人工智能方法的一部分是深入了解疾病生物学、解析患者异质性以及确定可能具有足够预测性以用于临床的特征的有前途的途径。在这里，我们介绍了网络信息如何作为人工智能的一部分在精神病学内外得到应用的实例，并概述了个性化精神病学方法如何从精神病学研究、人工智能开发和网络科学的更紧密结合中受益的未来展望。
