CataractBot: An LLM-Powered Expert-in-the-Loop Chatbot for Cataract Patients

• URL: http://arxiv.org/abs/2402.04620v5
• Date: Tue, 11 Feb 2025 06:48:43 GMT
• Title: CataractBot: An LLM-Powered Expert-in-the-Loop Chatbot for Cataract Patients
• Authors: Pragnya Ramjee, Bhuvan Sachdeva, Satvik Golechha, Shreyas Kulkarni, Geeta Fulari, Kaushik Murali, Mohit Jain,
• Abstract summary: CataractBot answers cataract surgery related questions instantly using an LLM to query a curated knowledge base, and provides expert-verified responses asynchronously.<n>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, and adding a privacy layer between patients and doctors.
• Score: 5.649965979758816
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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 designing expert-mediated LLM bots.

Related papers

• Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning [57.873833577058]
  We build a multimodal dataset enriched with extensive medical knowledge.<n>We then introduce our medical-specialized MLLM: Lingshu.<n>Lingshu undergoes multi-stage training to embed medical expertise and enhance its task-solving capabilities.
  arXiv  Detail & Related papers  (2025-06-08T08:47:30Z)
• Fact or Guesswork? Evaluating Large Language Model's Medical Knowledge with Structured One-Hop Judgment [108.55277188617035]
  Large language models (LLMs) have been widely adopted in various downstream task domains, but their ability to directly recall and apply factual medical knowledge remains under-explored. Most existing medical QA benchmarks assess complex reasoning or multi-hop inference, making it difficult to isolate LLMs' inherent medical knowledge from their reasoning capabilities. We introduce the Medical Knowledge Judgment, a dataset specifically designed to measure LLMs' one-hop factual medical knowledge.
  arXiv  Detail & Related papers  (2025-02-20T05:27:51Z)
• Effectiveness of ChatGPT in explaining complex medical reports to patients [3.8249624199073553]
  We explore whether ChatGPT (GPT 4) can help explain multidisciplinary team (MDT) reports to colorectal and prostate cancer patients. These reports are written in dense medical language and assume clinical knowledge, so they are a good test of the ability of ChatGPT to explain complex medical reports to patients.
  arXiv  Detail & Related papers  (2024-06-23T00:04:07Z)
• Leveraging Large Language Models for Patient Engagement: The Power of Conversational AI in Digital Health [1.8772687384996551]
  Large language models (LLMs) have opened up new opportunities for transforming patient engagement in healthcare through conversational AI. We showcase the power of LLMs in handling unstructured conversational data through four case studies.
  arXiv  Detail & Related papers  (2024-06-19T16:02:04Z)
• Assessing Empathy in Large Language Models with Real-World Physician-Patient Interactions [9.327472312657392]
  The integration of Large Language Models (LLMs) into the healthcare domain has the potential to significantly enhance patient care and support. This study investigates the question Can ChatGPT respond with a greater degree of empathy than those typically offered by physicians? We collect a de-identified dataset of patient messages and physician responses from Mayo Clinic and generate alternative replies using ChatGPT.
  arXiv  Detail & Related papers  (2024-05-26T01:58:57Z)
• MedInsight: A Multi-Source Context Augmentation Framework for Generating Patient-Centric Medical Responses using Large Language Models [3.0874677990361246]
  Large Language Models (LLMs) have shown impressive capabilities in generating human-like responses. We propose MedInsight:a novel retrieval framework that augments LLM inputs with relevant background information. Experiments on the MTSamples dataset validate MedInsight's effectiveness in generating contextually appropriate medical responses.
  arXiv  Detail & Related papers  (2024-03-13T15:20:30Z)
• MedKP: Medical Dialogue with Knowledge Enhancement and Clinical Pathway Encoding [48.348511646407026]
  We introduce the Medical dialogue with Knowledge enhancement and clinical Pathway encoding framework. The framework integrates an external knowledge enhancement module through a medical knowledge graph and an internal clinical pathway encoding via medical entities and physician actions.
  arXiv  Detail & Related papers  (2024-03-11T10:57:45Z)
• Yes, this is what I was looking for! Towards Multi-modal Medical Consultation Concern Summary Generation [46.42604861624895]
  We propose a new task of multi-modal medical concern summary generation. Nonverbal cues, such as patients' gestures and facial expressions, aid in accurately identifying patients' concerns. We construct the first multi-modal medical concern summary generation corpus.
  arXiv  Detail & Related papers  (2024-01-10T12:56:47Z)
• Language models are susceptible to incorrect patient self-diagnosis in medical applications [0.0]
  We present a variety of LLMs with multiple-choice questions from U.S. medical board exams modified to include self-diagnostic reports from patients. Our findings highlight that when a patient proposes incorrect bias-validating information, the diagnostic accuracy of LLMs drop dramatically.
  arXiv  Detail & Related papers  (2023-09-17T19:56:39Z)
• SPeC: A Soft Prompt-Based Calibration on Performance Variability of Large Language Model in Clinical Notes Summarization [50.01382938451978]
  We introduce a model-agnostic pipeline that employs soft prompts to diminish variance while preserving the advantages of prompt-based summarization. Experimental findings indicate that our method not only bolsters performance but also effectively curbs variance for various language models.
  arXiv  Detail & Related papers  (2023-03-23T04:47:46Z)
• Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes [50.8044927215346]
  We consider a comorbidity risk prediction scenario and focus on contexts regarding the patients clinical state. We employ several state-of-the-art LLMs to present contexts around risk prediction model inferences and evaluate their acceptability. Our paper is one of the first end-to-end analyses identifying the feasibility and benefits of contextual explanations in a real-world clinical use case.
  arXiv  Detail & Related papers  (2023-02-11T18:07:11Z)
• Retrieval-Augmented and Knowledge-Grounded Language Models for Faithful Clinical Medicine [68.7814360102644]
  We propose the Re$3$Writer method with retrieval-augmented generation and knowledge-grounded reasoning. We demonstrate the effectiveness of our method in generating patient discharge instructions.
  arXiv  Detail & Related papers  (2022-10-23T16:34:39Z)
• Doctor Recommendation in Online Health Forums via Expertise Learning [12.264865055778388]
  This paper studies a novel task of doctor recommendation to enable automatic pairing of a patient to a doctor with relevant expertise. We study the joint effects of their profiles and previous dialogues with other patients and explore their interactions via self-learning. For experiments, a large-scale dataset is collected from Chunyu Yisheng, a Chinese online health forum.
  arXiv  Detail & Related papers  (2022-03-06T10:39:48Z)
• Self-Supervised Knowledge Assimilation for Expert-Layman Text Style Transfer [63.72621204057025]
  Expert-layman text style transfer technologies have the potential to improve communication between scientific communities and the general public. High-quality information produced by experts is often filled with difficult jargon laypeople struggle to understand. This is a particularly notable issue in the medical domain, where layman are often confused by medical text online.
  arXiv  Detail & Related papers  (2021-10-06T17:57:22Z)
• Auto Response Generation in Online Medical Chat Services [0.0]
  We develop a smart auto-response generation mechanism for medical conversations that helps doctors respond to consultation requests efficiently. We explore over 900,000 anonymous, historical online messages between doctors and patients collected over nine months.
  arXiv  Detail & Related papers  (2021-04-26T17:45:10Z)
• Assessing the Severity of Health States based on Social Media Posts [62.52087340582502]
  We propose a multiview learning framework that models both the textual content as well as contextual-information to assess the severity of the user's health state. The diverse NLU views demonstrate its effectiveness on both the tasks and as well as on the individual disease to assess a user's health.
  arXiv  Detail & Related papers  (2020-09-21T03:45:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.