AI-Generated Summaries for Eye Disease

Overseen By Prashant Tailor, MD

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: University of California, Los Angeles

Disqualifiers: Cognitive impairments, Prisoners, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: 2

460 Participants Needed

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial explores whether AI-generated summaries in simple language help people better understand their eye care notes. Participants will receive either standard medical notes or notes with an added plain language summary (AI-Generated Plain Language Summaries). The researchers aim to determine if these summaries improve understanding and communication with eye doctors. The trial targets English-speaking adults receiving eye care at the Jules Stein Eye Institute who feel they might not fully grasp their current medical notes. As an unphased trial, it offers participants the chance to contribute to innovative research that could enhance patient-doctor communication.

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications.

What prior data suggests that AI-generated plain language summaries are safe for improving patient understanding?

Research has shown that AI-generated plain language summaries are safe and well-received. These summaries help people better understand their eye doctor's notes. Studies have demonstrated that these summaries assist healthcare professionals outside of eye care in understanding information more effectively and feeling more satisfied. This suggests that patients can also benefit from clearer communication without safety concerns. Since the treatment involves only adding a written summary, there are no physical risks. Overall, AI-generated summaries aim to make medical notes easier to understand, helping patients feel more confident about their care. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about AI-generated plain language summaries for eye disease because they have the potential to make complex medical information more understandable for patients. Unlike the standard ophthalmology notes that can be full of medical jargon, these AI-generated summaries simplify the information, making it easier for patients to grasp their condition and care plan. This approach could improve patient comprehension and satisfaction, empowering individuals to take a more active role in managing their eye health.

What evidence suggests that AI-generated plain language summaries are effective for improving understanding of eye care?

Research has shown that using AI to create easy-to-understand summaries can help people better comprehend medical notes. One study found that these summaries enabled non-eye doctors and other professionals to grasp medical information more clearly, leading to higher satisfaction. This trial will compare two approaches: participants in one arm will receive only the standard ophthalmology notes typically provided after their clinic visit, while participants in another arm will receive these notes plus an AI-generated plain language summary. This suggests that patients might also benefit from clearer communication. Simple language makes complex medical terms easier to understand, helping people feel more confident about managing their health. Although specific data on eye care patients is not yet available, early results are promising for improving understanding in medical settings. ^{1 2 6 7 8}

Who Is on the Research Team?

Prashant Tailor, MD

Principal Investigator

University of California, Los Angeles

Are You a Good Fit for This Trial?

This trial is for English-speaking adults who are receiving eye care at the Jules Stein Eye Institute. It's designed to help those who may struggle with understanding medical jargon, possibly including patients with aphasia or other communication difficulties.

Inclusion Criteria

Receiving ophthalmology care at the Jules Stein Eye Institute

Able to provide informed consent

Exclusion Criteria

Prisoners or wards of the state

I am unable to understand and agree to the study details on my own.

I do not have conditions like dementia that affect my understanding.

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

1-2 weeks

Intervention

Participants receive either standard ophthalmology notes or notes with an AI-generated plain language summary

1 day

1 visit (in-person)

Immediate Post-Visit Assessment

Participants complete a survey to assess comprehension and satisfaction immediately after their clinic visit

1 day

Follow-up

Participants receive a follow-up telephone interview to assess retention of information and provide additional feedback

1 week

1 call (telephone)

What Are the Treatments Tested in This Trial?

Interventions

• AI-Generated Plain Language Summaries

Trial Overview

The study is testing AI-generated summaries that translate ophthalmology notes into plain language. Participants will be randomly given either standard medical notes or ones with an AI summary, and their comprehension and confidence in care will be assessed through surveys and follow-up calls.

How Is the Trial Designed?

Group I: SON + AI-Generated Plain Language Summaries Experimental Treatment 1 Intervention

Participants in this arm receive the standard ophthalmology notes plus an AI-generated plain language summary, reviewed for accuracy before distribution. They will complete the same surveys to assess whether the additional summary improves their understanding and satisfaction compared to the control group.

Group II: Standard Ophthalmology Notes (SON) Only Active Control 1 Intervention

Participants in this arm receive the standard ophthalmology notes typically provided after their clinic visit, with no additional plain language summary. They will complete surveys that measure their comprehension and satisfaction with the visit notes.

Who Is Running the Clinical Trial?

University of California, Los Angeles

Lead Sponsor

Trials

1,594

Recruited

10,430,000+

Published Research Related to This Trial

The Total Visual Acuity Extraction Algorithm (TOVA) successfully automates the extraction of visual acuity data from unstructured clinical notes, achieving a 95% concordance rate with manual extraction in a validation study of 644 notes.

TOVA demonstrated high reliability and accuracy, with interrater reliability statistics indicating strong agreement and a Pearson correlation coefficient of 0.983, making it a valuable tool for efficiently processing large volumes of ophthalmology data.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Validation of the Total Visual Acuity Extraction Algorithm (TOVA) for Automated Extraction of Visual Acuity Data From Free Text, Unstructured Clinical Records. Baughman, DM., Su, GL., Tsui, I., et al.[2022]

The study analyzed 14,537 cataract surgeries over 32 months using electronic health record (EHR) data, demonstrating that EHR can effectively evaluate surgical outcomes for both resident and attending surgeons.

Results showed that 74% of resident surgeries had better postoperative visual acuity, with no significant difference in outcomes between residents and attending surgeons, indicating that residents can achieve comparable surgical results despite starting with poorer baseline visual acuity.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Assessing Resident Cataract Surgical Outcomes Using Electronic Health Record Data. Xiao, G., Srikumaran, D., Sikder, S., et al.[2023]

The AI-assisted diabetic retinopathy screening model demonstrated high diagnostic accuracy with a sensitivity of 96.9% and specificity of 87.7%, based on an analysis of 203 participants from various clinics.

Patient and clinician satisfaction was very high, with 93.7% of participants expressing satisfaction with the service, indicating that the AI system is not only effective but also well-accepted in real-world healthcare settings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Real-world artificial intelligence-based opportunistic screening for diabetic retinopathy in endocrinology and indigenous healthcare settings in Australia. Scheetz, J., Koca, D., McGuinness, M., et al.[2021]

The RetCAD AI software demonstrated high accuracy in identifying and grading age-related macular degeneration (AMD) and diabetic retinopathy (DR) from fundus photos, achieving a sensitivity of 97.3% and specificity of 73.3% for AMD, and 80.0% sensitivity and 90.1% specificity for DR at the patient level.

With a large sample size of 1245 eyes from 630 patients, the study shows that RetCAD can provide reliable automated grading, making it a cost-effective tool for screening in settings where expert evaluation is limited.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Simultaneous screening and classification of diabetic retinopathy and age-related macular degeneration based on fundus photos-a prospective analysis of the RetCAD system. Skevas, C., Weindler, H., Levering, M., et al.[2022]

The rise of big data in healthcare, driven by electronic health records and AI, has greatly enhanced the ability to analyze and understand diseases, particularly in ophthalmology, allowing for better diagnosis and treatment outcomes.

In ophthalmology, large datasets from clinical registries and studies are being utilized to improve understanding of corneal diseases and cataracts, although there are still barriers to fully realizing the potential of these resources in clinical practice.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Big data in corneal diseases and cataract: Current applications and future directions. Ting, DSJ., Deshmukh, R., Ting, DSW., et al.[2023]

A review of 26 websites on age-related macular degeneration (AMD) found that while many sites provide comprehensive information about AMD and emerging treatments, the overall quality of the information was disappointing, with average quality scores around 62%.

Most websites had high readability scores (SMOG ratings above 10), indicating that the information is difficult for the average person to understand, highlighting the need for physicians to guide patients to better resources.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Age-related macular degeneration: what do patients find on the internet? Rennie, CA., Hannan, S., Maycock, N., et al.[2021]

Artificial intelligence (AI) has the potential to significantly enhance the diagnosis and treatment of retinal diseases, which are a major cause of blindness, by providing decision support for conditions like diabetic retinopathy and age-related macular degeneration.

Integrating AI into clinical practice could improve healthcare efficiency, facilitate quicker access to vision-saving treatments, and allow clinicians to focus more on patient interactions and personalized care plans.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Artificial intelligence in retinal disease: clinical application, challenges, and future directions. Daich Varela, M., Sen, S., De Guimaraes, TAC., et al.[2023]

In a study of 1052 adult patients over 18 months, AI-based image analysis for diabetic retinopathy screening achieved 100% sensitivity and 89.2% specificity, indicating it is highly effective in identifying patients needing further evaluation.

The study found that 91.7% of patients had AI-gradable images, with the ability to improve image quality through reflex dilation for those with initially ungradable images, although older patients had lower image gradeability.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Diabetic Retinopathy Telemedicine Outcomes With Artificial Intelligence-Based Image Analysis, Reflex Dilation, and Image Overread. Mehra, AA., Softing, A., Guner, MK., et al.[2023]

ChatGPT-4.0 demonstrated an 85% accuracy rate in diagnosing corneal diseases from case reports, significantly outperforming ChatGPT-3.5, which had a 60% accuracy rate.

When compared to human experts, ChatGPT-4.0 showed a high interobserver agreement of 85% with one specialist, indicating its potential for clinical use in diagnosing corneal conditions.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Performance of ChatGPT in Diagnosis of Corneal Eye Diseases. Delsoz, M., Madadi, Y., Munir, WM., et al.[2023]

A survey of 466 patients with wet age-related macular degeneration revealed that the most important factors influencing their treatment preferences were clarity of vision, treatment effects on symptoms, and quality of vision, highlighting the significance of visual outcomes in their care.

The study demonstrated that patients actively engage in their treatment management, as indicated by their high levels of knowledge and confidence, which can guide future clinical development to better align with patient needs.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Patient Preferences in the Management of Wet Age-Related Macular Degeneration: A Conjoint Analysis. Skelly, A., Taylor, N., Fasser, C., et al.[2023]

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This clinical trial is testing whether plain language summaries made by artificial intelligence help people understand their eye doctor's notes better.

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In this study, use of LLM-generated PLSs was associated with enhanced comprehension and satisfaction among nonophthalmology clinicians and professionals.

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Evaluating AI-Generated Plain Language Summaries on ...

This clinical trial is testing whether plain language summaries made by artificial intelligence help people understand their eye doctor's notes better.

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Utilizing AI-Generated Plain Language Summaries to ...

The primary objectives of this study were to assess the efficacy of Plain Language. Summaries (PLSs) in enhancing ophthalmology note ...

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AI-Generated Summaries for Eye Disease

This clinical trial is testing whether plain language summaries made by artificial intelligence help people understand their eye doctor's notes better.

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medrxiv.org

medrxiv.org/content/10.1101/2024.09.12.24313551v1

Utilizing AI-Generated Plain Language Summaries to ...

Conclusions LLM-generated plain language summaries enhance accessibility and utility of ophthalmology notes for non-ophthalmology providers ...

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Integrating large language model (LLM)-generated plain language summaries into ophthalmology notes can improve interdisciplinary communication.

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Plain Language Summaries to Bolster Team-Based Care

Plain language summaries may aid in interdisciplinary communication between ophthalmologists and nonophthalmologist clinicians, which is a cornerstone of team- ...