Page 160 - Kaleidoscope Academic Conference Proceedings 2024

2024 ITU Kaleidoscope Academic Conference

explored to enable intelligent and personalized health Providing personalized health services requires the ability to
interventions across various domains, including disease retrieve relevant and trustworthy information based on an
diagnosis, treatment planning, health monitoring, and patient individual's specific context and needs. Traditional
engagement [8]. Machine learning models trained on large knowledge retrieval approaches based on keyword matching
medical datasets have shown promise in assisting clinicians or document similarity often fail to capture the nuanced
with diagnostic tasks, such as detecting cancers from medical semantics and reasoning required for health-related queries.
images [9], predicting adverse drug events from electronic Semantic search techniques that leverage knowledge graphs,
health records [10] and identifying mental health conditions ontologies and embeddings have shown promise in
from EEG data [11]. Chatbots and conversational agents improving the relevance and coverage of health information
powered by natural language processing are being developed retrieval [23], [24]. These approaches can enable more
to provide patient education, symptom assessment, and precise and comprehensive search results by mapping
treatment recommendations [12]. Computer vision queries and documents to structured representations that
techniques enable new assistive technologies for visually capture entities, relationships, and concepts. Retrieval-
impaired individuals [13] while AI-enabled robots support augmented generation (RAG) is an emerging paradigm
elder care and physical therapy [14]. However, current AI combining knowledge retrieval and generative AI to enable
applications in digital health largely rely on narrow, task- more informed and reliable language understanding and
specific models that are trained on limited, curated datasets. generation [25]. RAG models use a retriever component to
They often lack the breadth of knowledge, contextual find relevant context from an external knowledge source,
understanding, and reasoning capabilities needed to provide which is then passed to a generator component to produce a
truly personalized and engaging user experiences. contextually appropriate response.
Generative AI models that can leverage vast amounts of
general-purpose data offer a promising approach to bridge Recent work has demonstrated the potential of RAG for
this gap. improving the factual accuracy and consistency of generative
models in open-domain question answering [26] and
2.2 Generative AI and Large Language Models dialogue [27]. Applying RAG to personalized health
retrieval can enable generative models to access curated,
Generative AI refers to a class of AI models that can generate domain-specific knowledge sources, such as medical
new content, such as text, images, or audio, by learning ontologies, clinical guidelines, and patient education
patterns and representations from large datasets. Recent resources. By grounding generated content in verified health
advances in deep learning, particularly transformer information, RAG can help ensure the reliability and safety
architectures [15], have enabled the development of of AI-driven health services. However, designing effective
powerful generative language models that can understand retrieval mechanisms that can handle the complexity and
and generate human-like text with remarkable coherence and diversity of health queries, while preserving user privacy,
fluency. Models like GPT-3 [4], BERT [16] and T5 [17] have remains an open challenge.
been pre-trained on massive text corpora from the web,
books, and other sources, allowing them to capture rich 3. RESEARCH METHODOLOGY
knowledge about language, concepts, and reasoning patterns.
By fine-tuning these models on domain-specific data or 3.1 System Overview
providing them with contextual prompts, developers can
create intelligent applications that can engage in open-ended
conversations, answer questions, summarize documents, and
even write creative fiction. The potential of generative AI for
enabling personalized digital services has been demonstrated
in various domains, such as education [18], customer support
[19] and mental health [20].

Applying generative AI in high-stakes domains like
healthcare also raises important challenges around reliability,
safety, and ethical alignment. LLMs can sometimes generate
inaccurate, biased, or even harmful content, emphasizing the
need for careful prompt engineering, output filtering, and
human oversight [21]. Another critical consideration is
ensuring privacy and security of sensitive health data used to
train and deploy these models. Research on controllable and
safe generation techniques, model interpretability, and value
alignment is ongoing in the AI community [22].

2.3 Knowledge Retrieval for Personalized Health Figure 1 – System architecture diagram

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