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Alzheimer's Disease (AD), a neurodegenerative disorder, is pathologically identified by amyloid plaques and neurofibrillary tangles composed of tau protein. AD pharmacotherapy aims to manage cognitive symptoms, delay disease progression, and treat behavioral symptoms. The treatment is primarily symptomatic and palliative, with no definitive disease-modifying therapy available. Cholinesterase inhibitors, including donepezil (Aricept), rivastigmine (Exelon), and galantamine (Razadyne), are...
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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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Transforming Alzheimer's Digital Caregiving through Large Language Models.

Sujin Kim1, Dong Y Han2, Jihye Bae3

  • 1Division of Biomedical Informatics, Department of Internal Medicine, College of Medicine University of Kentucky, Lexington, 230F, Multidisciplinary Science Building, 725 Rose Street, KY40536, USA.

Current Alzheimer Research
|November 26, 2024
PubMed
Summary
This summary is machine-generated.

This study explores using AI Large Language Models (LLMs) to create digital caregiving strategies for Alzheimer's Disease and Related Dementias (AD/ADRD). Findings suggest LLMs can personalize support for caregivers, improving patient care.

Keywords:
Alzheimer's diseasecaregiversdigital caregivingdigital literacyglobal positioning system (GPS).large language models

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Area of Science:

  • Gerontology
  • Artificial Intelligence
  • Digital Health

Background:

  • Alzheimer's Disease and Related Dementias (AD/ADRD) impose significant burdens on informal caregivers.
  • Existing caregiving education materials (CEMs) and mobile application descriptions (MADs) offer a foundation for digital interventions.
  • The need for adaptive and evolving support systems for AD/ADRD caregivers is increasingly recognized.

Purpose of the Study:

  • To investigate the potential of AI-driven Large Language Models (LLMs) in developing digital caregiving strategies for AD/ADRD.
  • To analyze existing CEMs and MADs to identify key caregiving tasks and digital functionalities.
  • To align caregiving tasks with digital solutions across different stages of AD progression.

Main Methods:

  • Analysis of 38 CEMs from MedlinePlus and 57 AD digital caregiving MADs.
  • Utilized ChatGPT 3.5 to extract essential caregiving tasks and match them with appropriate digital functionalities for each AD stage.
  • Identified digital literacy requirements for caregivers supporting individuals with AD/ADRD.

Main Results:

  • AD caregiving was categorized into four stages (Pre-Clinical, Mild, Moderate, Severe) with associated key tasks.
  • Digital aids identified include memory-enhancing apps, GPS tracking, and voice-controlled devices.
  • Six essential digital literacy skills were identified: basic digital skills, communication, information management, safety/privacy, healthcare knowledge, and caregiver coordination.

Conclusions:

  • Advocates for an LLM-driven strategy to design digital caregiving interventions for AD/ADRD.
  • Proposes a novel paradigm for AD/ADRD support offering adaptive assistance tailored to caregiver needs.
  • Aims to enhance caregiver shared decision-making and patient care capabilities through AI-powered digital tools.