Reem S. AlAhmed’s research while affiliated with King Faisal Specialist Hospital and Research Centre and other places

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Publications (1)


Accuracy score
Box plot showing the distribution of accuracy scores for each question. Graph shows the interquartile range (box), median (horizontal line), mean (dot), and outliers (whiskers).
Completeness score
Box plot showing the distribution of completeness scores for each question. Graph shows the interquartile range (box), median (horizontal line), mean (dot), and outliers (whiskers).
Comprehensibility score
Box plot showing the distribution of comprehensibility scores for each question. Graph shows the interquartile range (box), median (horizontal line), mean (dot), and outliers (whiskers).
Comparing the mean score result between the Arabic and English responses [13]
Comparing domains mean score result between the Arabic and English responses [13]
Assessment of ChatGPT-generated medical Arabic responses for patients with metabolic dysfunction–associated steatotic liver disease
  • Article
  • Full-text available

February 2025

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33 Reads

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2 Citations

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Reem S. AlAhmed

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Waleed S. AlOmaim

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[...]

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Faisal A. Abaalkhail

Background and aim Artificial intelligence (AI)-powered chatbots, such as Chat Generative Pretrained Transformer (ChatGPT), have shown promising results in healthcare settings. These tools can help patients obtain real-time responses to queries, ensuring immediate access to relevant information. The study aimed to explore the potential use of ChatGPT-generated medical Arabic responses for patients with metabolic dysfunction–associated steatotic liver disease (MASLD). Methods An English patient questionnaire on MASLD was translated to Arabic. The Arabic questions were then entered into ChatGPT 3.5 on November 12, 2023. The responses were evaluated for accuracy, completeness, and comprehensibility by 10 Saudi MASLD experts who were native Arabic speakers. Likert scales were used to evaluate: 1) Accuracy, 2) Completeness, and 3) Comprehensibility. The questions were grouped into 3 domains: (1) Specialist referral, (2) Lifestyle, and (3) Physical activity. Results Accuracy mean score was 4.9 ± 0.94 on a 6-point Likert scale corresponding to “Nearly all correct.” Kendall’s coefficient of concordance (KCC) ranged from 0.025 to 0.649, with a mean of 0.28, indicating moderate agreement between all 10 experts. Mean completeness score was 2.4 ± 0.53 on a 3-point Likert scale corresponding to “Comprehensive” (KCC: 0.03–0.553; mean: 0.22). Comprehensibility mean score was 2.74 ± 0.52 on a 3-point Likert scale, which indicates the responses were “Easy to understand” (KCC: 0.00–0.447; mean: 0.25). Conclusion MASLD experts found that ChatGPT responses were accurate, complete, and comprehensible. The results support the increasing trend of leveraging the power of AI chatbots to revolutionize the dissemination of information for patients with MASLD. However, many AI-powered chatbots require further enhancement of scientific content to avoid the risks of circulating medical misinformation.

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Citations (1)


... These conversational tools, such as ChatGPT, are trained on large language datasets and can generate new content by identifying and replicating patterns from their training data [17,38]. ChatGPT, for example, is based on OpenAI's Generative Pretrained Transformer (GPT) model and has demonstrated its effectiveness in providing answers to a wide range of queries in a variety of healthcare settings, including mental health support and chronic disease management [17,39]. Despite their potential, LLMs also raise concerns, especially around privacy, the adequacy of their training, and the reliability of their output [17,38]. ...

Reference:

Revolutionizing MASLD: How Artificial Intelligence Is Shaping the Future of Liver Care
Assessment of ChatGPT-generated medical Arabic responses for patients with metabolic dysfunction–associated steatotic liver disease