Possibilities to Utilize Large Language Models in Detection and Mitigation of Limitations of Currently Available Neurocognitive Assessment Batteries

Author(s)	Mirza Niaz Zaman Elin
Country	Bangladesh
Abstract	Neurocognitive assessment batteries play a crucial role in evaluating cognitive abilities and identifying potential impairments or cognitive decline. However, these assessments may suffer from limitations and biases associated with specific tasks, such as drawing a clock, copying a cube, and recalling words. In this research paper, we explore the potential utilization of large language models in identifying and mitigating these limitations. We discuss the biases introduced by these tasks and propose the incorporation of alternative assessment methods. Furthermore, we examine the feasibility of utilizing large language models, such as the ChatGPT, to address these limitations and enhance the inclusivity and accuracy of cognitive evaluations. By leveraging the capabilities of large language models, we aim to provide a comprehensive framework for improving neurocognitive assessment batteries.
Keywords	LLMs, AI, Neurocognitive, ChatGPT, Assessment
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 5, Issue 3, May-June 2023
Published On	2023-05-31
DOI	https://doi.org/10.36948/ijfmr.2023.v05i03.3335

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research