Topical review: Incorporating generative artificial intelligence into neuropsychology training: best practices, pitfalls, and recommendations for effective implementation.

IF 2.1 3区 心理学 Q2 PSYCHOLOGY, DEVELOPMENTAL
Carly L Clark, Beth A Jerskey, Jason M Fogler
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引用次数: 0

Abstract

Objective: Generative artificial intelligence (AI) is rapidly transforming the field of neuropsychology by offering innovative opportunities to enhance clinical assessment precision, improve diagnostic accuracy, and streamline administrative duties. AI tools have the potential to enrich trainee education by supporting case conceptualization, personalizing treatment recommendations, assisting with report writing, and simulating complex clinical scenarios. Despite these benefits, there remains a lack of standardized guidelines for how neuropsychology training programs should responsibly and effectively integrate AI into supervision and educational practice.

Method: This topical review integrates emerging best practices, current challenges, and future directions for AI integration into neuropsychology training. We adapt the Integrative Developmental Model (IDM) of supervision, which conceptualizes trainee growth across progressive levels of motivation, autonomy, and professional identity.

Results: This review highlights the importance of establishing ethical safeguards, supervisor training, curriculum development, and developmentally appropriate implementation to ensure that technology supports, rather than replaces, clinical judgement and practice. By applying IDM principles, AI can be introduced in a developmentally appropriate manner, balancing the need for structured guidance, ethical safeguards, and flexibility in supervision.

Conclusions: This structured approach promotes both skill acquisition and responsible professional growth while aligning with broader ethical standards in psychology. When operationalized thoughtfully, these principles enable neuropsychology training programs to harness the potential benefits of AI while maintaining clinical rigor, professional standards, and ethical integrity. Developmentally informed supervision, grounded in the IDM, provides a flexible framework to ensure that AI strengthens rather than undermines the preparation of future neuropsychologists.

专题综述:将生成式人工智能纳入神经心理学训练:最佳实践、陷阱和有效实施的建议。
目的:生成式人工智能(AI)正在迅速改变神经心理学领域,为提高临床评估精度、提高诊断准确性和简化行政职责提供了创新机会。人工智能工具有可能通过支持病例概念化、个性化治疗建议、协助撰写报告和模拟复杂的临床场景来丰富培训生教育。尽管有这些好处,神经心理学培训项目如何负责任地、有效地将人工智能整合到监督和教育实践中,仍然缺乏标准化的指导方针。方法:本专题综述整合了人工智能融入神经心理学训练的新兴最佳实践、当前挑战和未来方向。我们采用监督的综合发展模型(IDM),该模型将受训者的成长概念化,包括动机、自主性和职业认同的渐进水平。结果:本综述强调了建立道德保障、主管培训、课程开发和发展适当实施的重要性,以确保技术支持而不是取代临床判断和实践。通过应用IDM原则,可以以适合发展的方式引入人工智能,平衡对结构化指导、道德保障和监督灵活性的需求。结论:这种结构化的方法促进了技能获得和负责任的专业成长,同时符合心理学中更广泛的道德标准。经过深思熟虑的操作,这些原则使神经心理学培训计划能够利用人工智能的潜在好处,同时保持临床严谨性、专业标准和道德诚信。以IDM为基础的发展知情监督提供了一个灵活的框架,以确保人工智能加强而不是破坏未来神经心理学家的准备。
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来源期刊
Journal of Pediatric Psychology
Journal of Pediatric Psychology PSYCHOLOGY, DEVELOPMENTAL-
CiteScore
6.00
自引率
11.10%
发文量
89
期刊介绍: The Journal of Pediatric Psychology is the official journal of the Society of Pediatric Psychology, Division 54 of the American Psychological Association. The Journal of Pediatric Psychology publishes articles related to theory, research, and professional practice in pediatric psychology. Pediatric psychology is an integrated field of science and practice in which the principles of psychology are applied within the context of pediatric health. The field aims to promote the health and development of children, adolescents, and their families through use of evidence-based methods.
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