Hasta Bilgilendirmesinde Yapay Zeka Robotları: Hazır mı?

Year 2024, Volume: 5 Issue: 3, 137 - 149, 29.07.2024

İbrahim Edhem Yılmaz

https://doi.org/10.46871/eams.1456744

Abstract

Bu çalışma, hasta bilgilendirilmesinde büyük dil modellerinin (LLM'ler) potansiyelini değerlendirmeyi amaçladı. Gemini ve ChatGPT 3.5 adlı iki LLM, yaygın ve kronik bir göz rahatsızlığı olan blefarit konusunda net ve anlaşılır bilgi sağlama yetenekleri açısından analiz edildi. LLM'ler tarafından bir dizi soruya yanıt olarak sağlanan bilgilerin anlaşılabilirliği ve eyleme geçirilebilirliği, eğitim materyallerini değerlendirmek için standartlaştırılmış bir araç olan PEMAT kullanılarak değerlendirildi. Yanıtlar blefaritin önemli yönlerini içeriyordu, ancak Flesch-Kincaid okunabilirlik skorları hasta eğitim materyalleri için önerilen 60-70 aralığının altındaydı. Gemini 38,75 puan alırken, ChatGPT 3.5 26,35 puan aldı, bu da içeriğin hedef kitle için çok karmaşık olabileceğini düşündürdü. Bu bulgular, LLM'lerin bilgilendirici kaynaklar olma potansiyeline sahip olmasına rağmen, mevcut okunabilirlik seviyelerinin hastalara erişilebilir sağlık bilgisi sağlamadaki etkinliklerini sınırlayabileceğini göstermektedir. Hasta eğitimine uygun açık ve özlü iletişimi sağlamak için LLM çıktılarını uyarlama yöntemlerini araştırmak için daha fazla araştırmaya ihtiyaç vardır.

Keywords

Büyük Dil Modelleri (LLM), Hasta Eğitimi, Okunabilirlik, Blefarit, Doğal Dil İşleme (NLP), Sağlık İletişimi

The Promise and the Challenge: Large Language Models for Patient Education - Are We There Yet?

Abstract

This study aimed to evaluate the potential of large language models (LLMs) for delivering patient education materials. Two LLMs, Gemini and ChatGPT 3.5, were analysed for their ability to provide clear and understandable information on the topic of blepharitis, a common eye condition.
The understandability and actionability of the information provided by the LLMs in response to a set of questions were evaluated using PEMAT, a standardised tool for assessing educational materials. The responses included the important aspects of blepharitis, yet the Flesch-Kincaid readability scores were below the suggested range of 60-70 for patient education materials. Gemini received a score of 38.75, whereas ChatGPT 3.5 earned 26.35, suggesting that the content might be too intricate for the target audience..
These findings suggest that while LLMs have the potential to be informative resources, their current readability levels may limit their effectiveness in providing accessible health information to patients. Further research is needed to explore methods for adapting LLM outputs to ensure clear and concise communication suitable for patient education.

Keywords

Large Language Models (LLMs), Patient Education, Readability, Blepharitis, Natural Language Processing (NLP), Health Communication

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