Personalized psychiatry has recently become an important component of the overall shift towards personalized medicine, aiming to address unmet medical needs in the field of mental health.

Mental disorders, particularly depression and anxiety disorders, are major contributors to the global health burden. Although various treatment options are available, they often lead to unsatisfactory outcomes. This is mainly because it is difficult to find the most effective treatment for each patient. While there are evidence-based guidelines for clinical practice, treatment recommendations are often based on the average response observed in clinical populations that participated in randomized clinical trials and do not consider specific individual characteristics.

The realization that people sharing a given diagnosis differ in several respects that are relevant to treatment response has led to a necessary move away from a one-size-fits-all approach to clinical care. Personalized psychiatry strives to integrate various patient-specific characteristics – symptoms, clinical features, neurobiological markers, genetics, epigenetics, brain imaging, environmental factors, and lifestyle – to predict susceptibility, aid diagnosis and optimize treatment to maximize efficacy and minimize adverse effects.

Recent technological innovations offer significant potential to advance the goals of personalized psychiatry. The introduction of electronic medical records simplifies the creation of extensive databases (big data). Real-time data collection via smart, wearable devices enables the recording of mental states as well as behavioral and physiological signals (digital phenotyping). In addition, the development of advanced artificial intelligence tools, such as machine learning methods, allows the recognition of intricate patterns in huge and complex data sets and thus predictions that go beyond human capabilities¹.

However, despite the promising potential, the development of a personalized approach in psychiatry has been slow, mainly due to several daunting challenges. These arise from the intricate and diverse nature of psychiatric disorders, which are characterized by considerable phenomenological complexity and heterogeneity. In addition, there are no established and clear pathophysiological pathways, and psychiatric disorders exhibit multilevel dynamics that encompass biological, psychological, behavioral, social and cultural dimensions. Thus, despite the emergence of numerous predictive models with potential utility for psychiatric clinical practice, minimal progress has been made in their real-world clinical application over the past two decades², underscoring the need for additional research and large-scale efforts.

Within this framework, the WPA launched in 2014 an innovative Scientific Section on Personalized Psychiatry, under the leadership of G. Perna, C.B. Nemeroff and A.F. Schatzberg. This pioneering initiative has attracted distinguished international experts who are active in the field as committee members, members, and speakers. Central to the Section's mandate is a commitment to harnessing advances in neuroscience, brain imaging, genetics and technology to promote the adoption of personalized approaches in all areas of mental health care.

By fostering interdisciplinary networks and collaborative research, the Section aims to unravel the complex interplay of genetic, environmental and neurobiological factors that underlie mental illness. Ultimately, the overarching goal is to equip clinicians with the tools necessary to deliver targeted psychiatric clinical interventions that benefit both professionals and patients.

Section members have conducted valuable research that contributes to this goal. Examples include identifying potential brain circuit-based biotypes for personalized treatment selection in mood disorders; translating individual-level brain circuit function into predictive markers for clinical practice³; and providing evidence that pharmacogenomics may be promising, but currently has no utility for treatment selection in major depressive disorder⁴. In addition, the immune system has emerged as a promising therapeutic target for certain sub-populations of people with major depression. Research is actively exploring features of immunometabolic depression as potential predictors of antidepressant treatment outcomes⁵, as well as proposed peripheral inflammatory biomarkers aimed at defining biotypes of unipolar and bipolar depression. An initial proposal for an evidence-based personalized therapy for panic disorder that takes into account individual phenomenological profiles and physiological patterns has been presented⁶, with ongoing clinical research projects actively exploring this area.

Artificial intelligence, a remarkably burgeoning field in all branches of medicine, has the capacity to assess the myriad of factors that have been found to contribute to treatment response in common mental disorders, leading to the development of reliable prediction models. Expert panels have recently published consensus guidelines for the definition of treatment resistance in anxiety disorders⁷ and major depressive disorder⁸. However, the effectiveness of these definitions for clinical decision making and health outcomes is still limited, and improvements in the therapeutic management of these disorders are needed⁹.

Through the promotion of international symposia, workshops and publications, our Section aims to promote knowledge exchange and collaboration among experts worldwide. In addition, it aims to collect and disseminate scientific knowledge in the field of personalized psychiatric care. These efforts have resulted in significant contributions to various scientific books. Notable works include Anxiety Disorders. Rethinking and Understanding Recent Discoveries (edited by Y.-K. Kim); Precision Psychiatry. Using Neuroscience Insights to Inform Personally Tailored, Measurement-Based Care (edited by L.M. Williams and L.M. Hack); The American Psychiatric Association Publishing Textbook of Psychopharmacology, 6th edition (edited by A.F. Schatzberg and C.B. Nemeroff); and Personalized Integrative Treatment for Depression (edited by Y.-K. Kim).

Since 2017, C.B. Nemeroff and G. Perna have been co-editors of the scientific journal Personalized Medicine in Psychiatry, a platform that grew out of their vision to create an editorial forum that allows mental health clinicians and researchers to contribute to and stay abreast of the latest advances in personalized approaches to mental health care.

In summary, by acknowledging the inherent heterogeneity and complexity of mental illness and advocating personalized approaches, this Section has attempted to lay the groundwork for a future in which each individual receives tailored treatment that addresses his/her individual needs.

However, it is clear that much more work is needed to achieve this goal. The Section will continue to advance this mission and further raise awareness of the importance of personalized approaches in the broader psychiatric community, aiming to promote implementation in practice and thus actively shaping the future landscape of psychiatric care.

个性化精神病学最近已成为向个性化医疗整体转变的重要组成部分，旨在解决心理健康领域未满足的医疗需求。

精神障碍，特别是抑郁症和焦虑症，是全球健康负担的主要原因。尽管有多种治疗方案可供选择，但它们往往会导致不令人满意的结果。这主要是因为很难为每个患者找到最有效的治疗方法。虽然临床实践有基于证据的指南，但治疗建议通常基于参与随机临床试验的临床人群中观察到的平均反应，并且不考虑具体的个体特征。

认识到同一诊断的人们在与治疗反应相关的几个方面存在差异，导致有必要放弃一刀切的临床护理方法。个性化精神病学致力于整合患者的各种具体特征——症状、临床特征、神经生物学标志物、遗传学、表观遗传学、脑成像、环境因素和生活方式——以预测易感性、帮助诊断和优化治疗，以最大限度地提高疗效并最大限度地减少不良反应。

最近的技术创新为推进个性化精神病学的目标提供了巨大的潜力。电子病历的引入简化了广泛数据库（大数据）的创建。通过智能可穿戴设备进行实时数据收集可以记录精神状态以及行为和生理信号（数字表型）。此外，先进人工智能工具（例如机器学习方法）的发展可以识别庞大而复杂的数据集中的复杂模式，从而做出超出人类能力的预测¹ 。

然而，尽管潜力巨大，但精神病学的个性化方法的发展却进展缓慢，这主要是由于一些艰巨的挑战。这些源于精神疾病的复杂性和多样性，其特点是相当大的现象学复杂性和异质性。此外，没有既定且明确的病理生理学途径，精神疾病表现出涵盖生物、心理、行为、社会和文化维度的多层次动态。因此，尽管出现了许多对精神病学临床实践具有潜在实用性的预测模型，但在过去二十年²中，它们在实际临床应用中取得的进展甚微，这凸显了需要进行更多研究和大规模努力。

在此框架内，WPA 于 2014 年在 G. Perna、CB Nemeroff 和 A​​F Schatzberg 的领导下成立了一个创新的个性化精神病学科学部门。这一开创性举措吸引了活跃在该领域的杰出国际专家作为委员会成员、成员和发言人。该科的核心任务是致力于利用神经科学、脑成像、遗传学和技术的进步来促进在精神卫生保健的所有领域采用个性化方法。

通过促进跨学科网络和合作研究，该科旨在揭示导致精神疾病的遗传、环境和神经生物学因素之间复杂的相互作用。最终，总体目标是为临床医生提供必要的工具，以提供有针对性的精神科临床干预措施，使专业人士和患者受益。

部门成员进行了有价值的研究，有助于实现这一目标。例子包括识别潜在的基于大脑回路的生物型，以用于情绪障碍的个性化治疗选择；将个体水平的脑回路功能转化为临床实践的预测标记³ ;并提供证据表明药物基因组学可能有希望，但目前对重度抑郁症的治疗选择没有用处⁴ 。此外，免疫系统已成为重度抑郁症患者某些亚群的有希望的治疗靶点。研究正在积极探索免疫代谢抑郁的特征作为抗抑郁治疗结果的潜在预测因素⁵ ，以及旨在定义单相和双相抑郁生物型的外周炎症生物标志物。已经提出了针对恐慌症的基于证据的个性化治疗的初步建议，该建议考虑了个体现象学特征和生理模式⁶ ，正在进行的临床研究项目正在积极探索这一领域。

人工智能是所有医学分支中一个非常新兴的领域，它有能力评估已发现的多种有助于常见精神疾病治疗反应的因素，从而开发出可靠的预测模型。专家小组最近发布了关于焦虑症⁷和重度抑郁症⁸治疗抵抗定义的共识指南。然而，这些定义对临床决策和健康结果的有效性仍然有限，需要改进这些疾病的治疗管理⁹ 。

通过推广国际研讨会、讲习班和出版物，我们的部门旨在促进世界各地专家之间的知识交流和合作。此外，它还旨在收集和传播个性化精神病护理领域的科学知识。这些努力为各种科学书籍做出了重大贡献。著名的著作包括《焦虑症》。重新思考和理解最近的发现（由 Y.-K. Kim 编辑）；精准精神病学。利用神经科学见解为个人定制、基于测量的护理提供信息（由 LM Williams 和 LM Hack 编辑）；美国精神病学协会出版的精神药理学教科书，第 6 版（AF Schatzberg 和 CB Nemeroff 编辑）；和抑郁症的个性化综合治疗（由 Y.-K. Kim 编辑）。

自 2017 年以来，CB Nemeroff 和 G. Perna 一直是科学期刊《精神病学个性化医学》的联合编辑，该平台源于他们创建一个编辑论坛的愿景，让心理健康临床医生和研究人员能够为精神健康临床医生和研究人员做出贡献并及时了解最新进展。个性化心理保健方法的最新进展。

总之，通过承认精神疾病固有的异质性和复杂性并倡导个性化方法，本节试图为未来奠定基础，让每个人接受满足其个人需求的定制治疗。

然而，很明显，要实现这一目标还需要做更多的工作。该科将继续推进这一使命，并进一步提高更广泛的精神病学界对个性化方法重要性的认识，旨在促进实践中的实施，从而积极塑造精神病学护理的未来格局。