ChatGPT, et al … Artificial Intelligence, Authorship, and Medical Publishing.
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ABSTRACT:
SUBMITTER: Solomon DH
PROVIDER: S-EPMC10267801 | biostudies-literature | 2023 Jun
REPOSITORIES: biostudies-literature
Publications
ChatGPT, et al … Artificial Intelligence, Authorship, and Medical Publishing.
Solomon Daniel H DH Allen Kelli D KD Katz Patricia P Sawalha Amr H AH Yelin Ed E
ACR open rheumatology 20230410 6
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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.
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Project description:BackgroundArtificial intelligence (AI) has the potential to revolutionize the way medicine is learned, taught, and practiced, and medical education must prepare learners for these inevitable changes. Academic medicine has, however, been slow to embrace recent AI advances. Since its launch in November 2022, ChatGPT has emerged as a fast and user-friendly large language model that can assist health care professionals, medical educators, students, trainees, and patients. While many studies focus on the technology's capabilities, potential, and risks, there is a gap in studying the perspective of end users.ObjectiveThe aim of this study was to gauge the experiences and perspectives of graduating medical students on ChatGPT and AI in their training and future careers.MethodsA cross-sectional web-based survey of recently graduated medical students was conducted in an international academic medical center between May 5, 2023, and June 13, 2023. Descriptive statistics were used to tabulate variable frequencies.ResultsOf 325 applicants to the residency programs, 265 completed the survey (an 81.5% response rate). The vast majority of respondents denied using ChatGPT in medical school, with 20.4% (n=54) using it to help complete written assessments and only 9.4% using the technology in their clinical work (n=25). More students planned to use it during residency, primarily for exploring new medical topics and research (n=168, 63.4%) and exam preparation (n=151, 57%). Male students were significantly more likely to believe that AI will improve diagnostic accuracy (n=47, 51.7% vs n=69, 39.7%; P=.001), reduce medical error (n=53, 58.2% vs n=71, 40.8%; P=.002), and improve patient care (n=60, 65.9% vs n=95, 54.6%; P=.007). Previous experience with AI was significantly associated with positive AI perception in terms of improving patient care, decreasing medical errors and misdiagnoses, and increasing the accuracy of diagnoses (P=.001, P<.001, P=.008, respectively).ConclusionsThe surveyed medical students had minimal formal and informal experience with AI tools and limited perceptions of the potential uses of AI in health care but had overall positive views of ChatGPT and AI and were optimistic about the future of AI in medical education and health care. Structured curricula and formal policies and guidelines are needed to adequately prepare medical learners for the forthcoming integration of AI in medicine.
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Project description:ObjectiveInvestigate the use of advanced natural language processing models to streamline the time-consuming process of writing and revising scholarly manuscripts.Materials and methodsFor this purpose, we integrate large language models into the Manubot publishing ecosystem to suggest revisions for scholarly texts. Our AI-based revision workflow employs a prompt generator that incorporates manuscript metadata into templates, generating section-specific instructions for the language model. The model then generates revised versions of each paragraph for human authors to review. We evaluated this methodology through 5 case studies of existing manuscripts, including the revision of this manuscript.ResultsOur results indicate that these models, despite some limitations, can grasp complex academic concepts and enhance text quality. All changes to the manuscript are tracked using a version control system, ensuring transparency in distinguishing between human- and machine-generated text.ConclusionsGiven the significant time researchers invest in crafting prose, incorporating large language models into the scholarly writing process can significantly improve the type of knowledge work performed by academics. Our approach also enables scholars to concentrate on critical aspects of their work, such as the novelty of their ideas, while automating tedious tasks like adhering to specific writing styles. Although the use of AI-assisted tools in scientific authoring is controversial, our approach, which focuses on revising human-written text and provides change-tracking transparency, can mitigate concerns regarding AI's role in scientific writing.
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Project description:BackgroundArtificial intelligence (AI) application is increasingly used in all fields, especially, in medicine. However, for the successful incorporation of AI-driven tools into medicine, healthcare professional should be equipped with the necessary knowledge. From that, we aimed to assess the AI readiness among medical students in Jordan.MethodsA cross-sectional survey was conducted among medical students across 6 Jordanian universities. Prevalidated Medical Artificial Intelligence Readiness Scale for Medical Students questionnaire was used. The questionnaire was distributed through social media groups of students. SPSS v.27 was used for analysis.ResultsA total of 858 responses were collected. The mean AI readiness score was 64.2%. Students scored more in the ability domain with a mean of 22.57. We found that academic performance (Grade point average) positively associated with overall AI readiness (P = .023), and prior exposure to AI through formal education or experience significantly enhances readiness (P = .009). In contrast, AI readiness levels did not significantly vary across different medical schools in Jordan. Notably, most students (84%) did not receive a formal education about AI from their schools.ConclusionIncorporation of AI education in medical curricula is crucial to close knowledge gaps and ensure that students are prepared for the use of AI in their future career. Our findings highlight the importance of preparing students to engage with AI technologies, and to be equipped with the necessary knowledge about its aspect.
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Project description:ObjectiveThis study aims at evaluating if ChatGPT-based artificial intelligence (AI) models are effective in interpreting electrocardiograms (ECGs) and determine their accuracy as compared to those of cardiologists. The purpose is therefore to explore if ChatGPT can be employed for clinical setting, particularly where there are no available cardiologists.MethodsA total of 107 ECG cases classified according to difficulty (simple, intermediate, complex) were analyzed using three AI models (GPT-ECGReader, GPT-ECGAnalyzer, GPT-ECGInterpreter) and compared with the performance of two cardiologists. The statistical analysis was conducted using chi-square and Fisher exact tests using scikit-learn library in Python 3.8.ResultsCardiologists demonstrated superior accuracy (92.52%) compared to ChatGPT-based models (GPT-ECGReader: 57.94%, GPT-ECGInterpreter: 62.62%, GPT-ECGAnalyzer: 62.62%). Statistically significant differences were observed between cardiologists and AI models (p < 0.05). ChatGPT models exhibited enhanced performance with female patients; however, the differences found were not statistically significant. Cardiologists significantly outperformed AI models across all difficulty levels. When it comes to diagnosing patients with arrhythmia (A) and cardiac structural disease ECG patterns, cardiologists gave the best results though there was no statistical difference between them and AI models in diagnosing people with normal (N) ECG patterns.ConclusionsChatGPT-based models have potential in ECG interpretation; however, they currently lack adequate reliability beyond oversight from a doctor. Additionally, further studies that would improve the accuracy of these models, especially in intricate diagnoses are needed.
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Project description:IntroductionOptic disc drusen (ODD) are acellular deposits in the optic nerve head, which are most often benign and asymptomatic. Patients may develop visual field defects and be at increased risk of ischemic co-morbidities. As ODD can be difficult to distinguish from papilledema, patients are at risk of unnecessary clinical workups. Patient information is a key aspect of ODD management. In this study, we explored the accuracy of ChatGPT responses for typical patient questions on ODD.MethodsTwo content experts reached consensus on 20 typical patient questions. We retrieved five separate responses for each question from ChatGPT, totaling 100 responses. Each response was evaluated on a 5-point Likert-scale on accuracy by each content expert in an individual fashion.ResultsThe two experts were in fair/substantial agreement in the evaluation of responses (Cronbach's alpha: 0.64). Of the 100 responses, 17 were relevant and without any inaccuracies, 78 were relevant and with inaccuracies not being harmful, and five were relevant and with inaccuracies potentially harmful. The lowest accuracy scores were obtained for questions dealing with treatment and prognosis.ConclusionsChatGPT often provides relevant answers for patient questions on ODD, but inaccuracies become potentially harmful when questions deal with treatment and prognosis.
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Project description:Understanding published unstructured textual data using traditional text mining approaches and tools is becoming a challenging issue due to the rapid increase in electronic open-source publications. The application of data mining techniques in the medical sciences is an emerging trend; however, traditional text-mining approaches are insufficient to cope with the current upsurge in the volume of published data. Therefore, artificial intelligence-based text mining tools are being developed and used to process large volumes of data and to explore the hidden features and correlations in the data. This review provides a clear-cut and insightful understanding of how artificial intelligence-based data-mining technology is being used to analyze medical data. We also describe a standard process of data mining based on CRISP-DM (Cross-Industry Standard Process for Data Mining) and the most common tools/libraries available for each step of medical data mining.
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