11 Clinical Trials for Various Conditions
The goal of this clinical trial is to find out whether using an artificial intelligence (AI) tool called a Large Language Model (LLM) can help patients think more clearly about their symptoms and improve their trust and experience during a visit to a musculoskeletal specialist. The study will answer two main questions: 1. Does an LLM-guided checklist that encourages patients to reflect on their beliefs about their symptoms improve their trust in the clinician (measured using the TRECS-7 scale)? 2. Does the checklist improve how patients feel about their consultation overall? Participants will be randomly assigned to one of two groups: * One group will receive an LLM-guided checklist that helps them think more flexibly about their condition. * The other group will receive an LLM-generated likely diagnosis and brief explanation of their symptoms. In both groups, the information from the AI tool will be shared with both the patient and the clinician before the consultation. Patients in the debiasing (intervention) group will: * Complete a short set of questions with help from a researcher * Receive a simple summary from the AI that reflects their beliefs and gently challenges any unhelpful thinking * Attend their regular specialist appointment * Complete a short survey afterwards capturing their thoughts, experience and basic demographics Patients in the diagnosis-only (control) group will: * Describe their symptoms to the AI LLM * Receive a likely diagnosis and short explanation based on this description * Attend their regular specialist appointment * Complete a short survey afterwards capturing their thoughts, experience and basic demographics
This study evaluates the impact of large language models (LLMs) versus traditional decision support tools on clinical decision-making in cardiology. General cardiologists will be randomized to manage real patient cases from a cardiovascular genetic cardiomyopathy clinic, with or without AI assistance. Each case will be assessed by two cardiologists, and their responses will be graded by blinded subspecialty experts using a standardized evaluation rubric.
This prospective, randomized controlled trial evaluated the efficacy of adding Large Language model (LLM)-generated Plain Language Summaries (PLSs) to Standard Ophthalmology Notes (SONs) in enhancing comprehension among non-ophthalmology providers. The study utilized surveys to assess non-ophthalmology providers\' comprehension and satisfaction with the notes and ophthalmologists\' evaluation of PLS accuracy, safety, and time burden. An objective semantic and linguistic analysis of the PLSs was also conducted.
A prospective randomized controlled trial comparing manual review and AI screening for patient eligibility determination and enrollments. A structured query will identify potentially eligible patients from the Mass General Brigham Electronic Data Warehouse (EDW), who will then be randomized into either the manual review arm or the AI-assisted review arm.
This pilot study aims to assess the feasibility of carrying out a full-scale pragmatic, cluster-randomized controlled trial which will investigate whether discharge summary writing assisted by a large language model (LLM), called CURE (Checker for Unvalidated Response Errors), improves care delivery without adversely impacting patient outcomes.
This study will evaluate the effect of providing access to GPT-4, a large language model, compared to traditional management decision support tools on performance on case-based management reasoning tasks.
This study will evaluate the effect of providing access to GPT-4, a large language model, compared to traditional diagnostic decision support tools on performance on case-based diagnostic reasoning tasks.
This study will evaluate the utility of ChatGPT in recommending treatment plans for patients with gastrointestinal cancers, using both retrospective and prospective data.
Clinical decision support tools powered by artificial intelligence are being rapidly integrated into medical practice. Two leading systems currently available to clinicians are OpenEvidence, which uses retrieval-augmented generation to access medical literature, and GPT-4, a large language model. While both tools show promise, their relative effectiveness in supporting clinical decision-making has not been directly compared. This study aims to evaluate how these tools influence diagnostic reasoning and management decisions among internal medicine physicians.
This clinical trial is testing whether plain language summaries made by artificial intelligence help people understand their eye doctor's notes better. Adults receiving eye care at the Jules Stein Eye Institute will get either the usual medical notes or a note with the addition of an AI-generated summary that explains the information in simple, everyday words. Participants will then answer a short survey and receive a follow-up call to share how clear the information was, how well they understood their diagnosis and treatment, and whether they feel more confident about their care. The goal is to find out if these plain language summaries can make it easier for people to understand their eye care and improve communication between patients and health care providers.
This study will assess the impact of immediate access to a customized version of GPT-4, a large language model, on performance in case-based diagnostic reasoning tasks. Specifically, it will compare this approach to a two-step process where participants first use traditional diagnostic decision support tools to support their diagnostic reasoning before gaining access to the customized GPT-4 model.