EQIP Quality Assessment of Refractive Surgery Resources on YouTube

Vought, Rita; Vought, Victoria; Herzog, Isabel; Greenstein, Steven A.

doi:10.1080/08820538.2023.2209168

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…Each item is evaluated with a simple yes-or-no decision, leading to a maximum possible score of 100. Although the category to be deducted according to the score is not specified exactly, such as DISCERN, the scores found in various studies are described as fair, moderate, or excellent [ 21 , 22 ].…”

Section: Methodsmentioning

confidence: 99%

“…The first part of the tool evaluates the resource's reliability through eight questions; the second part assesses the specifics of the treatment for the disease through seven questions, and the final question evaluates the overall quality. The total score, excluding the last question, ranges from 16 to 75 and is used to categorize the quality into five levels: excellent (63-75), good (51-62), moderate (39-50), poor (27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38), or very poor (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26). The EQIP tool, developed by health professionals and patient information managers, provides a comprehensive framework for assessing the quality of health information resources, such as websites and patient leaflets [20].…”

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confidence: 99%

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Exploring the Role of ChatGPT-4, BingAI, and Gemini as Virtual Consultants to Educate Families about Retinopathy of Prematurity

Durmaz Engin,

Karatas,

Ozturk

2024

Children

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Background: Large language models (LLMs) are becoming increasingly important as they are being used more frequently for providing medical information. Our aim is to evaluate the effectiveness of electronic artificial intelligence (AI) large language models (LLMs), such as ChatGPT-4, BingAI, and Gemini in responding to patient inquiries about retinopathy of prematurity (ROP). Methods: The answers of LLMs for fifty real-life patient inquiries were assessed using a 5-point Likert scale by three ophthalmologists. The models’ responses were also evaluated for reliability with the DISCERN instrument and the EQIP framework, and for readability using the Flesch Reading Ease (FRE), Flesch-Kincaid Grade Level (FKGL), and Coleman-Liau Index. Results: ChatGPT-4 outperformed BingAI and Gemini, scoring the highest with 5 points in 90% (45 out of 50) and achieving ratings of “agreed” or “strongly agreed” in 98% (49 out of 50) of responses. It led in accuracy and reliability with DISCERN and EQIP scores of 63 and 72.2, respectively. BingAI followed with scores of 53 and 61.1, while Gemini was noted for the best readability (FRE score of 39.1) but lower reliability scores. Statistically significant performance differences were observed particularly in the screening, diagnosis, and treatment categories. Conclusion: ChatGPT-4 excelled in providing detailed and reliable responses to ROP-related queries, although its texts were more complex. All models delivered generally accurate information as per DISCERN and EQIP assessments.

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Section: Methodsmentioning

confidence: 99%