Progress in developing personalised care for mental disorders is supported by numerous proof-of-concept machine learning studies in the area of risk assessment, diagnostics and precision prescribing. Most of these studies primarily use clinical data, but models might benefit from additional neuroimaging, blood and genetic data to improve accuracy. Combined, multimodal models might offer potential for stratification of patients for treatment. Clinical implementation of machine learning is impeded by a lack of wider generalisability, with efforts primarily focused on psychosis and dementia. Studies across all diagnostic groups should work to test the robustness of machine learning models, which is an essential first step to clinical implementation, and then move to prospective clinical validation. Models need to exceed clinicians’ heuristics to be useful, and safe, in routine decision-making. Engagement of clinicians, researchers and patients in digitalisation and ‘big data’ approaches are vital to allow the generation and accessibility of large, longitudinal, prospective data needed for precision psychiatry to be applied into real-world psychiatric care.

风险评估、诊断和精确处方领域的大量概念验证机器学习研究支持了精神障碍个性化护理的进展。大多数这些研究主要使用临床数据，但模型可能受益于额外的神经影像学、血液和遗传数据以提高准确性。组合的多模态模型可能为患者分层治疗提供潜力。机器学习的临床实施因缺乏更广泛的普遍性而受到阻碍，主要集中在精神病和痴呆症上。所有诊断组的研究都应该致力于测试机器学习模型的稳健性，这是临床实施必不可少的第一步，然后转向前瞻性临床验证。模型需要超越临床医生的启发式方法才能在常规决策中有用且安全。临床医生、研究人员和患者参与数字化和“大数据”方法对于生成和获取精准精神病学所需的大型、纵向、前瞻性数据以应用于现实世界的精神病治疗至关重要。