Singh Satneet, Gambill Jade L, Attalla Mary, Fatima Rida, Gill Amna R, Siddiqui Humza F
Psychiatry, Hampshire and Isle of Wight Healthcare NHS Foundation Trust, Southampton, GBR.
Neuroscience, Parker University, Dallas, USA.
Cureus. 2024 Oct 16;16(10):e71651. doi: 10.7759/cureus.71651. eCollection 2024 Oct.
Neuropsychiatric disorders (NPDs) pose a substantial burden on the healthcare system. The major challenge in diagnosing NPDs is the subjective assessment by the physician which can lead to inaccurate and delayed diagnosis. Recent studies have depicted that the integration of artificial intelligence (AI) in neuropsychiatry could potentially revolutionize the field by precisely diagnosing complex neurological and mental health disorders in a timely fashion and providing individualized management strategies. In this narrative review, the authors have examined the current status of AI tools in assessing neuropsychiatric disorders and evaluated their validity and reliability in the existing literature. The analysis of various datasets including MRI scans, EEG, facial expressions, social media posts, texts, and laboratory samples in the accurate diagnosis of neuropsychiatric conditions using machine learning has been profoundly explored in this article. The recent trials and tribulations in various neuropsychiatric disorders encouraging future scope in the utility and application of AI have been discussed. Overall machine learning has proved to be feasible and applicable in the field of neuropsychiatry and it is about time that research translates to clinical settings for favorable patient outcomes. Future trials should focus on presenting higher quality evidence for superior adaptability and establish guidelines for healthcare providers to maintain standards.
神经精神疾病（NPDs）给医疗系统带来了沉重负担。诊断神经精神疾病的主要挑战在于医生的主观评估，这可能导致诊断不准确和延误。最近的研究表明，将人工智能（AI）整合到神经精神病学中，有可能通过及时准确地诊断复杂的神经和精神健康疾病并提供个性化管理策略，给该领域带来变革。在这篇叙述性综述中，作者研究了人工智能工具在评估神经精神疾病方面的现状，并评估了它们在现有文献中的有效性和可靠性。本文深入探讨了利用机器学习对包括磁共振成像（MRI）扫描、脑电图（EEG）、面部表情、社交媒体帖子、文本和实验室样本等各种数据集进行分析，以准确诊断神经精神疾病的情况。文中还讨论了各种神经精神疾病近期的试验和困难，以及人工智能在实用性和应用方面令人鼓舞的未来前景。总体而言，机器学习已证明在神经精神病学领域是可行且适用的，现在是时候将研究转化为临床实践以实现良好的患者预后了。未来的试验应专注于提供更高质量的证据以证明更好地适应性，并为医疗保健提供者制定维持标准的指南。
