LLM-Based Education for Cataract Surgery
What You Need to Know Before You Apply
What is the purpose of this trial?
This trial tests whether an AI-powered educational tool (LLM-based Education) can help patients better understand their options for artificial lenses before cataract surgery. Patients often struggle to choose between standard lenses, typically covered by insurance, and premium lenses, which might reduce the need for glasses but cost extra. The study compares two groups: one receiving the usual care and another receiving an AI-guided explanation about lens choices before their doctor's visit. This trial suits English-speaking individuals who are being evaluated for cataract surgery and have not undergone this surgery before. As an unphased trial, it offers the opportunity to contribute to innovative research that could enhance patient education and decision-making.
Will I have to stop taking my current medications?
The trial information does not specify whether you need to stop taking your current medications. It seems focused on providing educational support for lens choices, so it's unlikely to require changes to your medication.
What prior data suggests that this LLM-based education is safe for patients?
Studies have shown that using large language models (LLMs) for patient education is quite safe. One study found that LLMs, like the AI tool used here, passed safety tests for most questions with flying colors, suggesting they are reliable and safe for providing information.
Another study examined the readability and comprehension of AI-generated educational materials. It found these materials simple to understand and effective for teaching patients. This indicates the AI tool is both safe and easy to use.
Overall, these findings suggest that the LLM-based education tool in this study is well-received and poses minimal risk when helping patients learn about their lens options before cataract surgery.12345Why are researchers excited about this trial?
Researchers are excited about using LLM-based education for cataract surgery because it introduces a personalized, AI-driven educational experience for patients. Unlike the standard approach, where patients receive information directly from medical staff, this method uses an AI-powered session to explain various intraocular lens options, including their benefits, trade-offs, and costs. This interactive session allows patients to ask questions, which could lead to better understanding and decision-making. By improving patient education, this approach aims to enhance patient satisfaction and engagement in their own care.
What evidence suggests that this LLM-based education is effective for improving patient understanding of lens options?
Research has shown that large language models (LLMs), like the one used in this trial, can help patients better understand their healthcare choices. In this trial, participants in the LLM-based Education arm will receive an AI-delivered educational session before their clinical visit. One study found that LLMs assist healthcare teams by providing clearer information, which may help patients grasp complex decisions, such as choosing the right lens for cataract surgery. Another study showed that AI-powered tools can make doctor visits more efficient and improve how patients engage with their care. While the AI tool in this trial is still being tested, early results suggest it could help patients make better-informed decisions about their lens options by providing personalized and easy-to-understand information.46789
Who Is on the Research Team?
Robert T Chang, MD
Principal Investigator
Stanford University
Are You a Good Fit for This Trial?
This trial is for patients with cataracts who need to choose an artificial lens before surgery. It's open to those who want additional information on their lens options, including the benefits of premium lenses that may cost extra.Inclusion Criteria
Timeline for a Trial Participant
Screening
Participants are screened for eligibility to participate in the trial
Intervention
Participants receive a short, structured LLM powered AI-delivered educational session about intraocular lens options
Consultation
Participants have a consultation with a fellow and attending physician to discuss lens options
Follow-up
Participants are monitored for satisfaction and lens choice post-surgery
What Are the Treatments Tested in This Trial?
Interventions
- LLM-based Education
Trial Overview
The study tests if a Large Language Model (LLM)-based educational tool can help patients understand their intraocular lens choices better compared to standard care alone. Patients will be divided into two groups: one receiving usual pre-surgery info and the other also getting AI-powered education.
How Is the Trial Designed?
2
Treatment groups
Experimental Treatment
Active Control
* Before seeing the fellow, the participant will listen to a short, structured LLM powered AI-delivered educational session with Custom GPT (10 minutes or less). The intractable AI script explains standard monofocal IOLs and premium options (toric, extended depth of focus, multifocal, light adjustable lens), including benefits, trade-offs, and out-of-pocket costs. * The AI module may allow the patient to ask clarifying questions within scope of that script. This AI session is not currently part of standard care and is considered the experimental intervention. * The participant takes a patient satisfaction (CSQ-8) after their clinical visit with the fellow and attending
* The participant skips the AI module and proceeds directly to routine fellow and attending counseling, which reflects current standard of care practice. * The participant takes a patient satisfaction (CSQ-8) after their clinical visit with the fellow and attending physician
Find a Clinic Near You
Who Is Running the Clinical Trial?
Stanford University
Lead Sponsor
Citations
The effectiveness of large language models in medical AI ...
The results of this study indicate that LLMs have substantial potential in supporting physicians and clinical teams to gain a deeper ...
NCT07317661 | Effectiveness of a Large Language Model- ...
Therefore in the present study, we want to test whether giving patients a short LLM powered AI-guided explanation from Custom GPT from OpenAI of ...
Assessing Demographic Variation in Large Language ...
This study seeks to explore whether the LLM-generated responses about cataract surgery differ based on a patient's race, gender, geographic ...
Development of an AI-Powered Agent for Cataract Surgery ...
This study demonstrates the potential of LLM-based technology to improve patient engagement, decision-making, and healthcare efficiency. This abstract was ...
Leveraging Large Language Models to Generate Multiple ...
Meaning LLMs have the potential to enhance ophthalmology resident education through high-quality examination content generation. Abstract.
Evaluating the reliability of large language models in ...
These findings demonstrate the ability of LLMs to function effectively in providing education to cataract patients. The FAQs evaluated in this ...
Evaluating the quality and readability of AI-generated ...
To address this gap, the present study evaluates the quality and readability of patient education materials generated by several state-of-the-art AI language ...
Artificial intelligence chatbots as sources of patient ...
Objective To conduct a head-to-head comparative analysis of cataract surgery patient education material generated by Chat Generative ...
9.
frontiersin.org
frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1639221/fullTransforming cataract care through artificial intelligence
Regarding harmlessness, LLM-Chatbots achieved perfect scores for the majority of questions, indicating the safety of using LLM-Chatbots for ...
Unbiased Results
We believe in providing patients with all the options.
Your Data Stays Your Data
We only share your information with the clinical trials you're trying to access.
Verified Trials Only
All of our trials are run by licensed doctors, researchers, and healthcare companies.