Künstliche Intelligenz und Maschinelles
                    Lernen

Hahn, Peter

doi:10.1055/a-0826-4789

Cited by 10 publications

(1 citation statement)

References 15 publications

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“…The main principles are based on the claim that computers could accurately mimic human cognitive functions such as learning and problem-solving [5,6]. Machine learning, a form of AI that uses computerized algorithms that learn and improve with experience, will find increasing application in medicine in the coming years [7]. The AI concepts of deep learning and artificial neural networks have become the cornerstones of significant achievements in image processing [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Does the Information Quality of ChatGPT Meet the Requirements of Orthopedics and Trauma Surgery?

Kasapovic,

Ali,

Babasiz

et al. 2024

Cureus

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Background: The integration of artificial intelligence (AI) in medicine, particularly through AI-based language models like ChatGPT, offers a promising avenue for enhancing patient education and healthcare delivery. This study aims to evaluate the quality of medical information provided by Chat Generative Pretrained Transformer (ChatGPT) regarding common orthopedic and trauma surgical procedures, assess its limitations, and explore its potential as a supplementary source for patient education.Methods: Using the GPT-3.5-Turbo version of ChatGPT, simulated patient information was generated for 20 orthopedic and trauma surgical procedures. The study utilized standardized information forms as a reference for evaluating ChatGPT's responses. The accuracy and quality of the provided information were assessed using a modified DISCERN instrument, and a global medical assessment was conducted to categorize the information's usefulness and reliability.Results: ChatGPT mentioned an average of 47% of relevant keywords across procedures, with a variance in the mention rate between 30.5% and 68.6%. The average modified DISCERN (mDISCERN) score was 2.4 out of 5, indicating a moderate to low quality of information. None of the ChatGPT-generated fact sheets were rated as "very useful," with 45% deemed "somewhat useful," 35% "not useful," and 20% classified as "dangerous." A positive correlation was found between higher mDISCERN scores and better physician ratings, suggesting that information quality directly impacts perceived utility. Conclusion: While AI-based language models like ChatGPT hold significant promise for medical education and patient care, the current quality of information provided in the field of orthopedics and trauma surgery is suboptimal. Further development and refinement of AI sources and algorithms are necessary to improve the accuracy and reliability of medical information. This study underscores the need for ongoing research and development in AI applications in healthcare, emphasizing the critical role of accurate, high-quality information in patient education and informed consent processes.

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