Artificial Intelligence Enabled Mobile Chatbot Psychologist using AIML and Cognitive Behavioral Therapy

Omarov, Batyrkhan; Zhumanov, Zhandos; Gumar, Aidana; Kuntunova, Leilya

doi:10.14569/ijacsa.2023.0140616

Cited by 11 publications

(1 citation statement)

References 49 publications

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“…Furthermore, cognitive behavioral therapy has shown promising results in treating anxiety and depression in dementia (Tay et al, 2019 ). LLMs can potentially help administer cognitive behavioral therapy via phone apps (Denecke et al, 2022 ) or in the shape of conversational chatbots (Patel et al, 2019 ; Omarov et al, 2023 ). In an analysis of social media posts on an LLM-powered mental health app (not specifically aimed toward PwD), Ma et al ( 2024 ) reported on-demand and non-judgmental support, the development confidence and self-discovery as the App's benefits.…”

Section: Methodsmentioning

confidence: 99%

Introduction to Large Language Models (LLMs) for dementia care and research

Treder,

Lee,

Tsvetanov

2024

Front. Dement.

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IntroductionDementia is a progressive neurodegenerative disorder that affects cognitive abilities including memory, reasoning, and communication skills, leading to gradual decline in daily activities and social engagement. In light of the recent advent of Large Language Models (LLMs) such as ChatGPT, this paper aims to thoroughly analyse their potential applications and usefulness in dementia care and research.MethodTo this end, we offer an introduction into LLMs, outlining the key features, capabilities, limitations, potential risks, and practical considerations for deployment as easy-to-use software (e.g., smartphone apps). We then explore various domains related to dementia, identifying opportunities for LLMs to enhance understanding, diagnostics, and treatment, with a broader emphasis on improving patient care. For each domain, the specific contributions of LLMs are examined, such as their ability to engage users in meaningful conversations, deliver personalized support, and offer cognitive enrichment. Potential benefits encompass improved social interaction, enhanced cognitive functioning, increased emotional well-being, and reduced caregiver burden. The deployment of LLMs in caregiving frameworks also raises a number of concerns and considerations. These include privacy and safety concerns, the need for empirical validation, user-centered design, adaptation to the user's unique needs, and the integration of multimodal inputs to create more immersive and personalized experiences. Additionally, ethical guidelines and privacy protocols must be established to ensure responsible and ethical deployment of LLMs.ResultsWe report the results on a questionnaire filled in by people with dementia (PwD) and their supporters wherein we surveyed the usefulness of different application scenarios of LLMs as well as the features that LLM-powered apps should have. Both PwD and supporters were largely positive regarding the prospect of LLMs in care, although concerns were raised regarding bias, data privacy and transparency.DiscussionOverall, this review corroborates the promising utilization of LLMs to positively impact dementia care by boosting cognitive abilities, enriching social interaction, and supporting caregivers. The findings underscore the importance of further research and development in this field to fully harness the benefits of LLMs and maximize their potential for improving the lives of individuals living with dementia.

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Section: Methodsmentioning

confidence: 99%

Introduction to Large Language Models (LLMs) for dementia care and research

Treder,

Lee,

Tsvetanov

2024

Front. Dement.

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Enhancing mental health with Artificial Intelligence: Current trends and future prospects

Olawade,

Wada,

Odetayo

et al. 2024

Journal of Medicine, Surgery, and Public Health

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MentalQLM: A lightweight large language model for mental healthcare based on instruction tuning and dual LoRA modules

Shi,

Wang,

Zhou

et al. 2024

Preprint

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Mental disorders present significant challenges to healthcare systems and carry profound social consequences. The rapid development of large language model (LLM) has opened new avenues for enhancing mental healthcare. However, existing approaches primarily rely on instruction tuning and few-shot in-context learning with massive datasets and large-scale backbone models, resulting in significant computational costs. To address these challenges, we propose MentalQLM, a novel lightweight LLM by developing a new dual Low-rank Adaptation (LoRA) approach. The development of MentalQLM consists of two key stages. Firstly, datasets are pruned based on perplexity and diversity analysis to reduce computational requirements. The first LoRA module is instruction-tuned to adapt the LLM for downstream mental health classification tasks. Secondly, a dense layer augmented with a second LoRA module is fine-tuned to enhance performance on complex multi-class classification tasks. Extensive experiments validate the effectiveness of the proposed MentalQLM on five benchmark datasets. Despite having only 0.5B parameters, the model outperforms or demonstrates comparable performance to larger counterparts in both classification and reasoning tasks. This establishes MentalQLM as a promising solution for lightweight and efficient deployment in real-world mental healthcare applications. All the code will be released at https://github.com/tortorish/MentalQLM.

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Artificial Intelligence Enabled Mobile Chatbot Psychologist using AIML and Cognitive Behavioral Therapy

Cited by 11 publications

References 49 publications

Introduction to Large Language Models (LLMs) for dementia care and research

Introduction to Large Language Models (LLMs) for dementia care and research

Enhancing mental health with Artificial Intelligence: Current trends and future prospects

MentalQLM: A lightweight large language model for mental healthcare based on instruction tuning and dual LoRA modules

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