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Retrieval-augmented generation (RAG) has rapidly emerged as a transformative approach for integrating large language models into clinical and biomedical workflows. However, privacy risks, such as protected health information (PHI) exposure,…
The rise of Artificial Intelligence (AI) has impacted the development of mobile health (mHealth) apps, most notably with the advent of AI-based chatbots used as ubiquitous ``companions'' for various services, from fitness to mental health…
Individuals are increasingly relying on large language model (LLM)-enabled conversational agents for emotional support. While prior research has examined privacy and security issues in chatbots specifically designed for mental health…
The emergence of Generative AI (Gen AI) and Large Language Models (LLMs) has enabled more advanced chatbots capable of human-like interactions. However, these conversational agents introduce a broader set of operational risks that extend…
LLM-based Conversational AIs (CAIs), also known as GenAI chatbots, like ChatGPT, are increasingly used across various domains, but they pose privacy risks, as users may disclose personal information during their conversations with CAIs.…
As Conversational Artificial Intelligence (AI) becomes more integrated into everyday life, AI-powered chatbot mobile applications are increasingly adopted across industries, particularly in the healthcare domain. These chatbots offer…
Retrieval-augmented generation (RAG) is a powerful technique to facilitate language model with proprietary and private data, where data privacy is a pivotal concern. Whereas extensive research has demonstrated the privacy risks of large…
Large Language Models (LLMs) are increasingly deployed in mental health contexts, from structured therapeutic support tools to informal chat-based well-being assistants. While these systems increase accessibility, scalability, and…
Recent advances in Retrieval-Augmented Generation (RAG) have enabled large language models (LLMs) to ground outputs in clinical evidence. However, connecting LLMs with external databases introduces the risk of contextual leakage: a subtle…
Large language models (LLMs) in biomedicine face a fundamental conflict between static parameter knowledge and the dynamic nature of clinical evidence. Retrieval-Augmented Generation (RAG) addresses this by grounding generation in external…
The shortage of doctors is creating a critical squeeze in access to medical expertise. While conversational Artificial Intelligence (AI) holds promise in addressing this problem, its safe deployment in patient-facing roles remains largely…
Artificial Intelligence (AI) for supporting healthcare services has never been more necessitated than by the recent global pandemic. Here, we review the state-of-the-art in AI-enabled Chatbots in healthcare proposed during the last 10 years…
The widespread adoption of Retrieval-Augmented Generation (RAG) systems in real-world applications has heightened concerns about the confidentiality and integrity of their proprietary knowledge bases. These knowledge bases, which play a…
Trustworthy clinical advice is crucial but burdensome when seeking health support from professionals. Inaccessibility and financial burdens present obstacles to obtaining professional clinical advice, even when healthcare is available.…
While Large Language Models (LLMs) are rapidly integrating into daily life, research on their risks often remains lab-based and disconnected from the problems users encounter "in the wild." While recent HCI research has begun to explore…
AI powered health chatbot applications are increasingly utilized for personalized healthcare services, yet they pose significant challenges related to user data security and privacy. This study evaluates the effectiveness of automated…
Many people suffer from mental health problems but not everyone seeks professional help or has access to mental health care. AI chatbots have increasingly become a go-to for individuals who either have mental disorders or simply want…
As smart tourism evolves, AI-powered chatbots have become indispensable for delivering personalized, real-time assistance to travelers while promoting sustainability and efficiency. However, these systems are increasingly vulnerable to…
The widespread adoption of conversational AI platforms has introduced new security and privacy risks. While these risks and their mitigation strategies have been extensively researched from a technical perspective, users' perceptions of…
Access to mental health support remains limited, particularly in marginalized communities where structural and cultural barriers hinder timely care. This paper explores the potential of AI-enabled chatbots as a scalable solution, focusing…