Objective: Appropriate screening is integral to the early diagnosis and management of Alzheimer’s Dementia (AD). The Paired Associates Learning (PAL) task is a digital cognitive task that is free of cultural, language, and educational biases. This study examined the association between the PAL task performance and global cognition and the usefulness of the PAL task as a screening tool for AD. Design: Cross-sectional. Setting: Academic hospital. Methods: Twenty-five participants with AD and 22 healthy comparators (HC) were included. The Cambridge Neuropsychological Test Automated Battery PAL task and the Montreal Cognitive Assessment (MoCA) were used to assess cognition. We assessed the relationship between the PAL task and MoCA performance using Pearson correlation and linear regression. We also examined the PAL task’s ability to distinguish between AD and HC participants using Receiver Operating Characteristic curve (ROC) analysis. Measurements: MoCA Total Score had a strong positive correlation with PAL Stages Completed score (r = 0.8, p < 0.001), and a strong negative correlation with PAL Total Errors (adjusted) score (r = −0.9, p < 0.001). Further, PAL Total Errors (adjusted) score predicted the MoCA Total Score (F (4, 46) = 37.2, p < 0.001). On ROC analysis, PAL Total Errors (adjusted) score cut-off of 54 errors had 92% sensitivity and 86% specificity to detect AD. Conclusions: Performance on the PAL task is highly associated with global cognition. Further, the PAL task can differentiate patients with AD from HCs with high sensitivity and specificity. Thus, the PAL task may hold potential usage as an easy-to-administer screening tool for AD.

provided only if the student requests them through free-form, usergenerated commands. The system then shows the requested clinical assessment findings as high-definition photos, sounds and/or text.The student determines whether the assessment finding is normal or abnormal and reports it, resembling a CS session. Diverse patient profiles have been included in the tool, such as BIPOC, LGBTQ+ and other marginalised populations, to expose students to a wider range of assessment findings than would typically be available in pre-clinical settings. This is critically important for developing a culturally responsive and inclusive tool that promotes socially responsible education. | WHAT LESSONS WERE LEARNED?NLP AI was successfully leveraged to develop a clinical skills digital solution, and the multiprofessional team was the keystone in the accomplishment. Student feedback collected during alpha testing indicated that they appreciated the opportunity for repeated practice and the use of multimedia to learn CS concepts. Medical programme faculty endorsed the innovation and expressed interest in the potential benefits of student performance diagnostics and customised evaluations.This innovation has many long-term benefits that will continue to be valuable beyond the COVID-19 pandemic. Students can use this innovation to practice what they learn during in-person CS sessions by customising their experience to their learning needs and receiving immediate feedback on their performance. Ongoing development of this innovation will allow students to explore various pathologies and patient presentations through different media, enabling students to use cognitive patterns that more closely align with in-person practice.The team plans to explore future opportunities including conversational history taking, voice-to-text AI and richer multimedia, such as video and 3D imaging.

The above article (Hicks et al., 2020) published with an incorrect affiliation for Wei Wang.