Purpose: ChatGPT has a wide range of applications in the medical field. Therefore, this review aims to define the key issues and provide a comprehensive view of the literature based on the application of ChatGPT in medicine.
Methods: This scope follows Arksey and O'Malley's five-stage framework. A comprehensive literature search of publications (30 November 2022 to 16 August 2023) was conducted. Six databases were searched and relevant references were systematically catalogued. Attention was focused on the general characteristics of the articles, their fields of application, and the advantages and disadvantages of using ChatGPT. Descriptive statistics and narrative synthesis methods were used for data analysis.
Results: Of the 3426 studies, 247 met the criteria for inclusion in this review. The majority of articles (31.17%) were from the United States. Editorials (43.32%) ranked first, followed by experimental studys (11.74%). The potential applications of ChatGPT in medicine are varied, with the largest number of studies (45.75%) exploring clinical practice, including assisting with clinical decision support and providing disease information and medical advice. This was followed by medical education (27.13%) and scientific research (16.19%). Particularly noteworthy in the discipline statistics were radiology, surgery and dentistry at the top of the list. However, ChatGPT in medicine also faces issues of data privacy, inaccuracy and plagiarism.
Conclusion: The application of ChatGPT in medicine focuses on different disciplines and general application scenarios. ChatGPT has a paradoxical nature: it offers significant advantages, but at the same time raises great concerns about its application in healthcare settings. Therefore, it is imperative to develop theoretical frameworks that not only address its widespread use in healthcare but also facilitate a comprehensive assessment. In addition, these frameworks should contribute to the development of strict and effective guidelines and regulatory measures.
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http://dx.doi.org/10.2147/JMDH.S463128 | DOI Listing |
Endocrine
January 2025
Department of Family Medicine, Yongin Severance Hospital, Gyeonggi-do, Republic of Korea.
Purpose: Early detection and intervention are vital for managing type 2 diabetes mellitus (T2DM) effectively. However, it's still unclear which risk factors for T2DM onset are most significant. This study aimed to use cluster analysis to categorize individuals based on six known risk factors, helping to identify high-risk groups requiring early intervention to prevent T2DM onset.
View Article and Find Full Text PDFAm J Ophthalmol
December 2024
Department of Ophthalmology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China. Electronic address:
Purpose: The integration of generative artificial intelligence (GAI) into scientific research and academic writing has generated considerable controversy. Currently, standards for using GAI in academic medicine remain undefined. This study aims to conduct a comprehensive analysis of the guidance provided for authors regarding the use of GAI in ophthalmology scientific journals.
View Article and Find Full Text PDFNat Sci Sleep
December 2024
Department of Otorhinolaryngology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany.
Purpose: Obstructive sleep apnoea (OSA) is a common disease that benefits from early treatment and patient support in order to prevent secondary illnesses. This study assesses the capability of the large language model (LLM) ChatGPT-4o to offer patient support regarding first line positive airway pressure (PAP) and second line hypoglossal nerve stimulation (HGNS) therapy.
Methods: Seventeen questions, each regarding PAP and HGNS therapy, were posed to ChatGPT-4o.
Cureus
November 2024
Emergency Medicine, Valaichchenai Base Hospital, Valaichchenai, LKA.
Introduction: Artificial intelligence (AI) plays a significant role in creating brochures on radiological procedures for patient education. Thus, this study aimed to evaluate the responses generated by ChatGPT (San Francisco, CA: OpenAI) and Google Gemini (Mountain View, CA: Google LLC) on abdominal ultrasound, abdominal CT scan, and abdominal MRI.
Methodology: A cross-sectional original research was conducted over one week in June 2024 to evaluate the quality of patient information brochures produced by ChatGPT 3.
Cureus
November 2024
Department of Orthopedic Surgery, Stony Brook University, Stony Brook, USA.
Background The generation of innovative research ideas is crucial to advancing the field of medicine. As physicians face increasingly demanding clinical schedules, it is important to identify tools that may expedite the research process. Artificial intelligence may offer a promising solution by enabling the efficient generation of novel research ideas.
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