ChatGPT, authorship, and medical publishing.
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ABSTRACT:
SUBMITTER: Kleebayoon A
PROVIDER: S-EPMC10425586 | biostudies-literature | 2023 Aug
REPOSITORIES: biostudies-literature
Publications
ChatGPT, authorship, and medical publishing.
Kleebayoon Amnuay A Wiwanitkit Viroj V
ACR open rheumatology 20230704 8
Similar Datasets
Project description: Not available
| S-EPMC10267801 | biostudies-literature
Project description:Because they do not rank highly in the hierarchy of evidence and are not frequently cited, case reports describing the clinical circumstances of single patients are seldom published by medical journals. However, many clinicians argue that case reports have significant educational value, advance medical knowledge, and complement evidence-based medicine. Over the last several years, a vast number (∼160) of new peer-reviewed journals have emerged that focus on publishing case reports. These journals are typically open access and have relatively high acceptance rates. However, approximately half of the publishers of case reports journals engage in questionable or "predatory" publishing practices. Authors of case reports may benefit from greater awareness of these new publication venues as well as an ability to discriminate between reputable and non-reputable journal publishers.
| S-EPMC4816468 | biostudies-literature
Project description:BackgroundAcademic publishing is a cornerstone of scholarly communications, yet is unfortunately open to abuse, having given rise to 'predatory publishers'- groups that employ aggressive marketing tactics, are deficient in methods and ethics, and bypass peer review. Preventing these predatory publishers from infiltrating scholarly activity is of high importance, and students must be trained in this area to increase awareness and reduce use. The scope of this issue in the context of medical students remains unknown, and therefore this sought to examine the breadth of the current literature base.MethodsA rapid scoping review was undertaken, adhering to adapted PRISMA guidelines. Six databases (ASSIA, EBSCO, Ovid, PubMed, Scopus, Web of Science) were systematically searched for content related to predatory publishing and medical students. Results were single-screened, facilitated by online reviewing software. Resultant data were narratively described, with common themes identified.ResultsAfter searching and screening, five studies were included, representing a total of 1338 students. Two predominant themes- understanding, and utilisation- of predatory publishers was identified. These themes revealed that medical students were broadly unaware of the issue of predatory publishing, and that a small number have already, or would consider, using their services.ConclusionThere remains a lack of understanding of the threat that predatory publishers pose amongst medical students. Future research and education in this domain will be required to focus on informing medical students on the issue, and the implication of engaging with predatory publishers.
| S-EPMC10770935 | biostudies-literature
Project description:Importance:In peer-reviewed medical journals, authoring an invited commentary on an original article is a recognition of expertise. It has been documented that women author fewer invited publications than men do. However, it is unknown whether this disparity is due to gender differences in characteristics that are associated with invitations, such as field of expertise, seniority, and scientific output. Objective:To estimate the odds ratio (OR) of authoring an invited commentary for women compared with men who had similar expertise, seniority, and publication metrics. Design, Setting, and Participants:This matched case-control study included all medical invited commentaries published from January 1, 2013, through December 31, 2017, in English-language medical journals and multidisciplinary journals. Invited commentaries were defined as publications that cite another publication within the same journal volume and issue. Bibliometric data were obtained from Scopus. Cases were defined as corresponding authors of invited commentaries in a given journal during the study period. Controls were matched to cases based on scientific expertise by calculating a similarity index for abstracts published during the same period using natural language processing. Data analyses were conducted from March 13, 2019, through May 3, 2019. Exposure:Corresponding or sole author gender was predicted from author first name and country of origin using genderize.io. Main Outcomes and Measures:The OR for gender was estimated after adjusting for field of expertise, publication output, citation impact, and years active (ie, years since first publication), with an interaction between gender and years active. Results:The final data set included 43 235 cases across 2549 journals; there were 34 047 unique intraciting commentary authors, among whom 9072 (26.6%) were women. For researchers who had been active for the median of 19 years, the odds of invited commentary authorship were 21% lower for women (OR, 0.79 [95% CI, 0.77-0.81]; P < .001) compared with men who had similar scientific expertise, number of publications, and citation impact. For every decile increase in years active, the OR decreased by a factor of 0.97 (95% CI, 0.96-0.98; P < .001). Conclusions and Relevance:In this case-control study, women had lower odds of authoring invited commentaries than their male peers. This disparity was larger for senior researchers. Journal editors could use natural language processing of published research to widen and diversify the pool of experts considered for commentary invitations.
| S-EPMC6820037 | biostudies-literature
Project description:Chatbots powered by artificial intelligence have revolutionized many industries and fields of study, including medical education. Medical educators are increasingly asked to perform more administrative, written, and assessment functions with less time and resources. Safe use of chatbots, like ChatGPT, can help medical educators efficiently perform these functions. In this article, we provide medical educators with tips for the implementation of ChatGPT in medical education. Through creativity and careful construction of prompts, medical educators can use these and other implementations of chatbots, like ChatGPT, in their practice.
| S-EPMC10910173 | biostudies-literature
Project description:Color vision deficiency (CVD) affects a significant portion of the population, yet its impact is often overlooked in medical education, especially in visually demanding specialties like dermatology, pathology, and radiology. In this study, we investigated the potential of ChatGPT to comprehend CVD-simulated images in image-based diagnostic tasks. Notably, the model successfully adapted its diagnostic reasoning to match CVD-modified color perception while preserving high prediction accuracy. These findings highlight the potential of using ChatGPT to foster more inclusive learning environments for individuals with CVD in visually intensive medical specialties.
| S-EPMC11782439 | biostudies-literature
Project description:BackgroundThe chatbot application Bennie and the Chats was introduced due to the outbreak of COVID-19, which is aimed to provide substitution for teaching conventional clinical history-taking skills. It was implemented with DialogFlow with preset responses, which consists of a large constraint on responding to different conversations. The rapid advancement of artificial intelligence, such as the recent introduction of ChatGPT, offers innovative conversational experiences with computer-generated responses. It provides an idea to develop the second generation of Bennie and the Chats. As the epidemic slows, it can become an assisting tool for students as additional exercise. In this work, we present the second generation of Bennie and the Chats with ChatGPT, which provides room for flexible and expandable improvement.MethodsThe objective of this research is to examine the influence of the newly proposed chatbot on learning efficacy and experiences in bedside teaching, and its potential contributions to international teaching collaboration. This study employs a mixed-method design that incorporates both quantitative and qualitative approaches. From the quantitative approach, we launched the world's first cross-territory virtual bedside teaching with our proposed application and conducted a survey between the University of Hong Kong (HKU) and the National University of Singapore (NUS). Descriptive statistics and Spearman's Correlation were applied for data analysis. From the qualitative approach, a comparative analysis was conducted between the two versions of the chatbot. And, we discuss the interrelationship between the quantitative and qualitative results.ResultsFor the quantitative result, we collected a questionnaire from 45 students about the evaluation of virtual bedside teaching between territories. Over 75% of the students agreed that teaching can enhance learning effectiveness and experience. Moreover, by exchanging patients cases, 82.2% of students agreed that it helps to gain more experiences with diseases that may not be prevalent in their own locality. For the qualitative result, the new chatbot provides better usability and flexibility.ConclusionVirtual bedside teaching with chatbots has revolutionized conventional bedside teaching by its advantages and allowing international collaborations. We believe that the training of history taking skills by chatbot will be a feasible supplementary teaching tool to conventional bedside teaching.
| S-EPMC11806775 | biostudies-literature
Project description:BackgroundMedical student master's theses are often carried out as research projects, and some are published as research papers in journals. We investigated the percentage of master's theses conducted by 5th -year students at the Medical Degree Program at Lund University, Sweden, that subsequently served as the basis for research publications. In addition, we explored both student and supervisor experiences with the publishing process.MethodsA cohort of four semesters of student data covering the period from 2019 to 2020 (n = 446) was searched in PubMed, Embase and the Web of Science to assess whether they had been published as research papers. Surveys were sent to students (n = 121) and supervisors (n = 77) to explore their experiences with the publishing process.ResultsWe found that 33% (149 of 446) of the students in the 2019-2020 cohort subsequently published their theses, and 50% of these students were listed as first authors. Most students published original research. Students (n = 21) and supervisors (n = 44) reported that the publishing process was time-consuming and that students needed multilevel support from supervisors to achieve successful publication. The publishing process was reported by 79% of the students to have led to additional learning. Most of the papers (126 of 149, 85%) had a clinical or patient-oriented focus.ConclusionA high percentage of the student publications in which students are listed as first authors require engagement from both students and supervisors. Supervisors play an essential role in supporting students in a successful publication process. Most of the published papers were either clinical or patient-oriented research.
| S-EPMC10800057 | biostudies-literature
Project description:This article reports the results of an experiment conducted with ChatGPT to see how its performance compares to human performance on tests that require specific knowledge and skills, such as university admission tests. We chose a general undergraduate admission test and two tests for admission to biomedical programs: the Scholastic Assessment Test (SAT), the Cambridge BioMedical Admission Test (BMAT), and the Italian Medical School Admission Test (IMSAT). In particular, we looked closely at the difference in performance between ChatGPT-4 and its predecessor, ChatGPT-3.5, to assess its evolution. The performance of ChatGPT-4 showed a significant improvement over ChatGPT-3.5 and, compared to real students, was on average within the top 10% in the SAT test, while the score in the IMSAT test granted admission to the two highest ranked Italian medical schools. In addition to the performance analysis, we provide a qualitative analysis of incorrect answers and a classification of three different types of logical and computational errors made by ChatGPT-4, which reveal important weaknesses of the model. This provides insight into the skills needed to use these models effectively despite their weaknesses, and also suggests possible applications of our analysis in the field of education.
| S-EPMC11498684 | biostudies-literature
Project description:Large language models like ChatGPT are a type of machine learning model that can offer a positive paradigm shift in case-based/problem-based learning (CBL/PBL). ChatGPT may be able to augment the existing paradigm to work in conjunction with the clinical-teacher in PBL/CBL case generation. It can develop realistic patient cases that could be revised by clinical teachers to ensure accuracy and relevance. Further, it can be directed to include specific case content in order to facilitate the constructive alignment of the case with the broader learning objectives of the curriculum. There is also the possibility of improving engagement by 'gamifying' CBL/PBL.Supplementary informationThe online version contains supplementary material available at 10.1007/s40670-023-01934-5.
| S-EPMC10948641 | biostudies-literature