CataractBot: An LLM-Powered Expert-in-the-Loop Chatbot for Cataract Patients
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Abstract (arXiv)
The healthcare landscape is evolving, with patients seeking reliable information about their health conditions and available treatment options. Despite the abundance of information sources, the digital age overwhelms individuals with excess, often inaccurate information. Patients primarily trust medical professionals, highlighting the need for expert-endorsed health information. However, increased patient loads on experts has led to reduced communication time, impacting information sharing. To address this gap, we developed CataractBot. CataractBot answers cataract surgery related questions instantly using an LLM to query a curated knowledge base, and provides expert-verified responses asynchronously. It has multimodal and multilingual capabilities. In an in-the-wild deployment study with 49 patients and attendants, 4 doctors, and 2 patient coordinators, CataractBot demonstrated potential, providing anytime accessibility, saving time, accommodating diverse literacy levels, alleviating power differences, and adding a privacy layer between patients and doctors. Users reported that their trust in the system was established through expert verification. Broadly, our results could inform future work on expert-mediated LLM bots.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 9 of its 135 references that are in the map. First posted on arXiv 7 Feb 2024.
Builds on
9 of 135 references.
References · 9
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Built on by
Citing papers per year: 2024: 2; 2025: 5; 2026: 2.
9 papers.
- 2024STACKFEED: Structured Textual Actor-Critic Knowledge Base Editing with FeedBackcited by 4
- 2024HEALTH-PARIKSHA: Assessing RAG Models for Health Chatbots in Real-World Multilingual Settingscited by 4
- 2025Toward Cybersecurity-Expert Small Language Modelscited by 3
- 2025Generative AI for Healthcare: Fundamentals, Challenges, and Perspectivescited by 3
- 2026SoulSeek: Exploring the Use of Social Cues in LLM-based Information Seekingcited by 1
- 2025"It Felt Like I Was Left in the Dark": Exploring Information Needs and Design Opportunities for Family Caregivers of Older Adult Patients in Critical Care Settingscited by 0
- 2025Match Chat: Real Time Generative AI and Generative Computing for Tenniscited by 0
- 2025Living the Novel: A System for Generating Self-Training Timeline-Aware Conversational Agents from Novelscited by 0
- 2026ProcAgent: An Agentic Framework for Procedural Task Guidance on Edge with Human-in-the-Loopcited by 0
- 2026ProcAgent: An Agentic Framework for Procedural Task Guidance on Edge with Human-in-the-Loop9 Jun 2026
- 2026SoulSeek: Exploring the Use of Social Cues in LLM-based Information Seeking3 Jan 2026
- 2025Living the Novel: A System for Generating Self-Training Timeline-Aware Conversational Agents from Novels8 Dec 2025
- 2025Generative AI for Healthcare: Fundamentals, Challenges, and Perspectives28 Oct 2025
- 2025Toward Cybersecurity-Expert Small Language Models15 Oct 2025
- 2025Match Chat: Real Time Generative AI and Generative Computing for Tennis16 Sep 2025
- 2025"It Felt Like I Was Left in the Dark": Exploring Information Needs and Design Opportunities for Family Caregivers of Older Adult Patients in Critical Care Settings7 Feb 2025
- 2024HEALTH-PARIKSHA: Assessing RAG Models for Health Chatbots in Real-World Multilingual Settings17 Oct 2024
- 2024STACKFEED: Structured Textual Actor-Critic Knowledge Base Editing with FeedBack14 Oct 2024