Possibilities to Utilize Large Language Models in Detection and Mitigation of Limitations of Currently Available Neurocognitive Assessment Batteries
Year: 2023
Type: article
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.
Author: Mirza Niaz Zaman Elin -
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4379207648