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  1. Article: Snowfall and Carbon Monoxide Poisoning in Murree Pakistan.

    Meo, Muhammad Zain Sultan / Meo, Muhammad Omair Sultan / Meo, Anusha Sultan

    Pakistan journal of medical sciences

    2022  Volume 38, Issue 8, Page(s) 2386

    Language English
    Publishing date 2022-11-11
    Publishing country Pakistan
    Document type Journal Article
    ZDB-ID 2032827-8
    ISSN 1681-715X ; 1682-024X ; 1017-4699
    ISSN (online) 1681-715X
    ISSN 1682-024X ; 1017-4699
    DOI 10.12669/pjms.38.8.6936
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 3948: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  2. Article: ChatGPT Knowledge Evaluation in Basic and Clinical Medical Sciences: Multiple Choice Question Examination-Based Performance.

    Meo, Sultan Ayoub / Al-Masri, Abeer A / Alotaibi, Metib / Meo, Muhammad Zain Sultan / Meo, Muhammad Omair Sultan

    Healthcare (Basel, Switzerland)

    2023  Volume 11, Issue 14

    Abstract: The Chatbot Generative Pre-Trained Transformer (ChatGPT) has garnered great attention from the public, academicians and science communities. It responds with appropriate and articulate answers and explanations across various disciplines. For the use of ... ...

    Abstract The Chatbot Generative Pre-Trained Transformer (ChatGPT) has garnered great attention from the public, academicians and science communities. It responds with appropriate and articulate answers and explanations across various disciplines. For the use of ChatGPT in education, research and healthcare, different perspectives exist with some level of ambiguity around its acceptability and ideal uses. However, the literature is acutely lacking in establishing a link to assess the intellectual levels of ChatGPT in the medical sciences. Therefore, the present study aimed to investigate the knowledge level of ChatGPT in medical education both in basic and clinical medical sciences, multiple-choice question (MCQs) examination-based performance and its impact on the medical examination system. In this study, initially, a subject-wise question bank was established with a pool of multiple-choice questions (MCQs) from various medical textbooks and university examination pools. The research team members carefully reviewed the MCQ contents and ensured that the MCQs were relevant to the subject's contents. Each question was scenario-based with four sub-stems and had a single correct answer. In this study, 100 MCQs in various disciplines, including basic medical sciences (50 MCQs) and clinical medical sciences (50 MCQs), were randomly selected from the MCQ bank. The MCQs were manually entered one by one, and a fresh ChatGPT session was started for each entry to avoid memory retention bias. The task was given to ChatGPT to assess the response and knowledge level of ChatGPT. The first response obtained was taken as the final response. Based on a pre-determined answer key, scoring was made on a scale of 0 to 1, with zero representing incorrect and one representing the correct answer. The results revealed that out of 100 MCQs in various disciplines of basic and clinical medical sciences, ChatGPT attempted all the MCQs and obtained 37/50 (74%) marks in basic medical sciences and 35/50 (70%) marks in clinical medical sciences, with an overall score of 72/100 (72%) in both basic and clinical medical sciences. It is concluded that ChatGPT obtained a satisfactory score in both basic and clinical medical sciences subjects and demonstrated a degree of understanding and explanation. This study's findings suggest that ChatGPT may be able to assist medical students and faculty in medical education settings since it has potential as an innovation in the framework of medical sciences and education.
    Language English
    Publishing date 2023-07-17
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2721009-1
    ISSN 2227-9032
    ISSN 2227-9032
    DOI 10.3390/healthcare11142046
    Database MEDical Literature Analysis and Retrieval System OnLINE

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