Section for Precision Psychiatry, Department of Psychiatry and Psychotherapy, Ludwig Maximilian University, Munich, Germany; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Max Planck Institute of Psychiatry, Munich, Germany.
Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London, UK; Wellcome Centre for Human Neuroimaging, University College London, London, UK.
Lancet Digit Health. 2022 Nov;4(11):e829-e840. doi: 10.1016/S2589-7500(22)00153-4. Epub 2022 Oct 10.
In this Series paper, we explore the promises and challenges of artificial intelligence (AI)-based precision medicine tools in mental health care from clinical, ethical, and regulatory perspectives. The real-world implementation of these tools is increasingly considered the prime solution for key issues in mental health, such as delayed, inaccurate, and inefficient care delivery. Similarly, machine-learning-based empirical strategies are becoming commonplace in psychiatric research because of their potential to adequately deconstruct the biopsychosocial complexity of mental health disorders, and hence to improve nosology of prognostic and preventive paradigms. However, the implementation steps needed to translate these promises into practice are currently hampered by multiple interacting challenges. These obstructions range from the current technology-distant state of clinical practice, over the lack of valid real-world databases required to feed data-intensive AI algorithms, to model development and validation considerations being disconnected from the core principles of clinical utility and ethical acceptability. In this Series paper, we provide recommendations on how these challenges could be addressed from an interdisciplinary perspective to pave the way towards a framework for mental health care, leveraging the combined strengths of human intelligence and AI.
在本系列论文中，我们从临床、伦理和监管的角度探讨了基于人工智能 (AI) 的精准医学工具在精神卫生保健方面的前景和挑战。这些工具的实际应用越来越被认为是解决精神卫生领域关键问题的主要方案，例如治疗延迟、不准确和效率低下等问题。同样，基于机器学习的经验策略在精神科研究中也越来越普遍，因为它们有可能充分解构精神障碍的生物心理社会复杂性，从而改善预后和预防范式的分类学。然而，将这些前景转化为实践所需的实施步骤目前受到多种相互作用的挑战的阻碍。这些障碍包括临床实践的当前技术落后状态、缺乏为数据密集型人工智能算法提供数据的有效真实世界数据库，以及模型开发和验证考虑因素与临床实用性和伦理可接受性的核心原则脱节。在本系列论文中，我们提供了一些建议，从跨学科的角度探讨如何解决这些挑战，为利用人类智能和人工智能的综合优势，为精神卫生保健制定一个框架铺平道路。
