A prospective randomized study of patient-centered AI for clinical visit preparation
Authors: Xincheng Tan, Matthew Thompson, Xiang Ji, Angelica Willis, Will Vaughan, Roma Ruparel, Michael A. Sanchez, Sean Chang, Joseph Xu, Bhavna Daryani, Preeti Singh, Pinal Bavishi, Kejia Chen, Fan Zhang, Priya Gupta, Ernest Lim, Christina Chen, Bob Lou, Dimitrios Antos, Zach Wasson, Ray Luo, Fadi Yousif, Florence Thng, Mark Malhotra, Joseph Breda, Daniel McDuff, Kavi Goel, Katherine Chou, Dale R. Webster, Avinatan Hassidim, Yossi Matias, Po-Hsuan Cameron Chen, Yun Liu, Mike Schaekermann, Quang Duong, Jackie M. Barr
Abstract
Patients often arrive at brief, time-constrained clinical visits unsure of what to expect or ask, and millions of adults now turn to general-purpose AI chatbots to prepare beforehand. Yet whether conversational AI improves visit readiness remains unclear. We designed a large language model (LLM)-based visit preparation agent to guide patients before an appointment by gathering clinical context, setting visit expectations, providing relevant health information, building a prioritized list of questions for their clinician, and generating a summary. In a prospective consented randomized study in the Google Health app, 802 U.S. adults held 866 health-related conversations with the visit preparation agent (standard or concise variant) or a minimally prompted control chatbot built on the same LLM. The visit preparation agent significantly outperformed the control across blinded clinician evaluations and patient self-reports. In clinician reviews of completed sessions (n = 272), the agent achieved a 16.6% higher rate of comprehensive preparation (67.2% vs. 50.6%; P < 0.05) and an 8.5% higher rate of alignment with patients' health context (98.9% vs. 90.4%; P < 0.01), while maintaining high medical safety and appropriateness across all study arms. Patient post-surveys (n = 460) showed 16.8% higher visit preparedness (73.8% vs. 57.0%; P < 0.01), 14.3% higher confidence in knowing what to ask (81.4% vs. 67.1%; P < 0.01), and 12.0% higher overall satisfaction (74.0% vs. 62.0%; P < 0.05). Clinician and participant metrics were not significantly correlated, indicating that expert review and patient self-assessment capture complementary dimensions of patient-facing AI. These findings suggest that LLM chatbots can safely help patients prepare for clinical visits, with greater benefit when following a structured workflow, and motivate future studies on in-visit communication and clinical outcomes.
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Towards Conversational Diagnostic AITao Tu, Anil Palepu, Mike Schaekermann et al. · 2024 · Artificial Intelligence · preprint, not peer reviewed · cited 167 times · AI summary available
AMIE trains an LLM for diagnostic dialogue, whereas this study tests a structured workflow for preparing patients for clinical visits.
Abstract
At the heart of medicine lies the physician-patient dialogue, where skillful history-taking paves the way for accurate diagnosis, effective management, and enduring trust. Artificial Intelligence (AI) systems capable of diagnostic dialogue could increase accessibility, consistency, and quality of care. However, approximating clinicians' expertise is an outstanding grand challenge. Here, we introduce AMIE (Articulate Medical Intelligence Explorer), a Large Language Model (LLM) based AI system optimized for diagnostic dialogue. AMIE uses a novel self-play based simulated environment with automated feedback mechanisms for scaling learning across diverse disease conditions, specialties, and cont…
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This overview surveys LLM applications for patient engagement, including conversational analysis, chatbots, summarization, and patient engagement systems.
Abstract
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ProAI: Proactive Multi-Agent Conversational AI with Structured Knowledge Base for Psychiatric DiagnosisYuqi Wu, Guangya Wan, Jingjing Li et al. · 2025 · Artificial Intelligence · cited 9 times · AI summary available
ProAI uses proactive, knowledge-guided multi-agent dialogue for psychiatric diagnosis, rather than visit preparation.
Abstract
Most LLM-driven conversational AI systems operate reactively, responding to user prompts without guiding the interaction. Most LLM-driven conversational AI systems operate reactively, responding to user prompts without guiding the interaction. However, many real-world applications-such as psychiatric diagnosis, consulting, and interviews-require AI to take a proactive role, asking the right questions and steering conversations toward specific objectives. Using mental health differential diagnosis as an application context, we introduce ProAI, a goal-oriented, proactive conversational AI framework. ProAI integrates structured knowledge-guided memory, multi-agent proactive reasoning, and a mul…
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AI as a Medical Ally: Evaluating ChatGPT's Usage and Impact in Indian HealthcareAryaman Raina, Prateek Mishra, Harshit goyal et al. · 2024 · Human-Computer Interaction · preprint, not peer reviewed · cited 3 times · AI summary available
This study surveys Indian users and clinicians about ChatGPT in healthcare, including concerns about reliability and trust.
Abstract
This study investigates the integration and impact of Large Language Models (LLMs), like ChatGPT, in India's healthcare sector. Our research employs a dual approach, engaging both general users and medical professionals through surveys and interviews respectively. Our findings reveal that healthcare professionals value ChatGPT in medical education and preliminary clinical settings, but exercise caution due to concerns about reliability, privacy, and the need for cross-verification with medical references. General users show a preference for AI interactions in healthcare, but concerns regarding accuracy and trust persist. The study underscores the need for these technologies to complement, no…
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This study evaluates ChatGPT's answers to medical and USMLE questions, rather than its role in preparing patients for visits.
Abstract
Artificial intelligence is gaining traction in more ways than ever before. The popularity of language models and AI-based businesses has soared since ChatGPT was made available to the general public via OpenAI. It is becoming increasingly common for people to use ChatGPT both professionally and personally. Considering the widespread use of ChatGPT and the reliance people place on it, this study determined how reliable ChatGPT can be for answering complex medical and clinical questions. Harvard University gross anatomy along with the United States Medical Licensing Examination (USMLE) questionnaire were used to accomplish the objective. The paper evaluated the obtained results using a 2-way A…
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ChatDoctor fine-tunes LLaMA on patient-doctor dialogues and adds retrieval to generate medical advice, rather than guiding visit preparation.
Abstract
The primary aim of this research was to address the limitations observed in the medical knowledge of prevalent large language models (LLMs) such as ChatGPT, by creating a specialized language model with enhanced accuracy in medical advice. We achieved this by adapting and refining the large language model meta-AI (LLaMA) using a large dataset of 100,000 patient-doctor dialogues sourced from a widely used online medical consultation platform. These conversations were cleaned and anonymized to respect privacy concerns. In addition to the model refinement, we incorporated a self-directed information retrieval mechanism, allowing the model to access and utilize real-time information from online …
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CoachAI: A Conversational Agent Assisted Health Coaching PlatformAhmed Fadhil, Gianluca Schiavo, Yunlong Wang · 2019 · Artificial Intelligence · preprint, not peer reviewed · cited 10 times · Not summarized yet
CoachAI uses a conversational healthcare agent to guide users, applying chatbot-based interaction to health coaching rather than visit preparation.
Abstract
Poor lifestyle represents a health risk factor and is the leading cause of morbidity and chronic conditions. The impact of poor lifestyle can be significantly altered by individual behavior change. Although the current shift in healthcare towards a long lasting modifiable behavior, however, with increasing caregiver workload and individuals' continuous needs of care, there is a need to ease caregiver's work while ensuring continuous interaction with users. This paper describes the design and validation of CoachAI, a conversational agent assisted health coaching system to support health intervention delivery to individuals and groups. CoachAI instantiates a text based healthcare chatbot syste…
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Psy-LLM applies LLM-based question answering to psychological consultations, rather than helping patients prepare for clinical visits.
Abstract
The demand for psychological counselling has grown significantly in recent years, particularly with the global outbreak of COVID-19, which has heightened the need for timely and professional mental health support. Online psychological counselling has emerged as the predominant mode of providing services in response to this demand. In this study, we propose the Psy-LLM framework, an AI-based assistive tool leveraging Large Language Models (LLMs) for question-answering in psychological consultation settings to ease the demand for mental health professions. Our framework combines pre-trained LLMs with real-world professional Q\&A from psychologists and extensively crawled psychological articles…
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