Penn Artificial Intelligence and Technology Collaboratory for Healthy Aging

University Of Pennsylvania, Philadelphia PA

Investigators

Linked publications & trials

Abstract

Project Summary (Abstract) Successful aging in the home can greatly improve quality of life, improve health outcomes, and reduce burden on the healthcare system. The goal of the Parent P30 is to establish a national collaboratory, named PennAITech, for the development, evaluation, and implementation of artificial intelligence software and new technologies to facilitate health aging in the home. Recent advances in AI have led to the development of highly notable Large Language Models (LLMs) such as OpenAI’s ChatGPT. These models have showcased exceptional abilities in comprehending and producing text that resembles human language to a remarkable extent, leading to a great potential to reshape the AI assistance research in caring for aging population. In this supplement, we propose a pilot project to develop and explore powerful AI assisted tools using LLMs to support caring for the aging population. We focus our study on family caregivers of patients with Alzheimer’s Disease and Related Dementias (ADRD) and examine whether LLMs can be developed to answer questions that caregivers have. Since most LLMs are trained on text data from various domains, their ability for specific domains may not be optimized. Thus, the overarching goal of this supplement is to collect high-quality data in our domain, and use that to finetune the LLMs to make them more powerful to answer domain specific questions. Furthermore, this work will highlight future directions for research of LLM specifically in the context of ADRD care. To achieve this goal, we have two aims. In Aim 1, we will create a conversational data repository specific to behavior intervention for family caregivers of persons with dementia to improve their quality of life. We will generate, clean and preprocess the interview transcripts from behavior intervention sessions for family caregivers of persons with dementia from an ongoing qualitative data repository, which includes sessions among caregivers and therapists (N= 3,000 as of 6/1/23). In Aim 2, we will build a large language model (LLM) to provide an AI assisted, efficient and scalable approach in supporting behavior intervention for dementia caregivers. We propose to use the high-quality data from the conversational database generated in Aim 1 to finetune the existing powerful LLMs, and build an LLM suitable for answering questions from dementia caregivers to help reduce their anxiety and depression, improve their mental status and quality of life. The resulting LLM is expected to provide answers that closely align with those of human experts, offering an AI-assisted, efficient, and scalable approach to behavioral interventions for family caregivers of dementia patients. Also, this approach can be extended to develop LLMs for other relevant applications, such as addressing clinical questions related to ADRD. By doing so, this could provide valuable AI- enabled services to the aging care industry, contributing to the overall improvement of public health.