Abstract Artificial intelligence (AI) is increasingly influencing the practice of clinical neuropsychology, offering new tools to enhance efficiency, organization, and precision across every stage of the neuropsychological workflow. This chapter explores how AI can be applied responsibly from pre-evaluation preparation through data management, interpretation, and report writing. Examples illustrate how, under the clinician’s supervision, AI supports the synthesis of extensive records, the structuring of interviews, the organization of test data, and the refinement of report language. Throughout, attention is given to ethical and professional safeguards, including confidentiality, transparency, bias mitigation, informed consent, and documentation of AI-assisted contributions. The chapter emphasizes that AI must remain an augmentative instrument rather than an interpretive authority, with human expertise guiding all analytic and clinical decisions. Collectively, these considerations demonstrate how AI can ethically extend, rather than erode, the neuropsychologist’s role. Effective use of AI can enhance clarity, efficiency, and scientific rigor while preserving the discipline’s defining commitment to human judgment and individualized care.
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations of such models.
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
Empirically, PRISM reduces the end-to-end time for data selection and model tuning to just 30% of conventional pipelines, and achieves this efficiency while simultaneously enhancing performance, surpassing models fine-tuned on the full dataset across eight multimodal and three language understanding benchmarks.
Jinhe Bi, Yifan Wang, Danqi Yan et al.· arXiv.org· 73 citations· ⚡4
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
This paper designs Markov decision processes (MDPs) for different combinatorial problems and proposes to train conditional GFlowNets to sample from the solution space and demonstrates that GFlowNet policies can efficiently find high-quality solutions.
Dinghuai Zhang, H. Dai, Esmeralda S. Whitammer et al.· Advances in Neural Informati...· 59 citations· ⚡8
An empirical study on the current state of practice in artificial intelligence ethics is conducted by means of a multiple case study of five case companies, which indicates a gap between research and practice in the area.
Ville Vakkuri, Kai-Kristian Kemell, Joni Kultanen et al.· arXiv.org· 56 citations· ⚡6