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Srinivasan M, Venugopal A, Venkatesan L, Kumar R. Navigating the Pedagogical Landscape: Exploring the Implications of AI and Chatbots in Nursing Education. JMIR Nurs 2024; 7:e52105. [PMID: 38870516 DOI: 10.2196/52105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 11/30/2023] [Accepted: 12/12/2023] [Indexed: 06/15/2024] Open
Abstract
This viewpoint paper explores the pedagogical implications of artificial intelligence (AI) and AI-based chatbots such as ChatGPT in nursing education, examining their potential uses, benefits, challenges, and ethical considerations. AI and chatbots offer transformative opportunities for nursing education, such as personalized learning, simulation and practice, accessible learning, and improved efficiency. They have the potential to increase student engagement and motivation, enhance learning outcomes, and augment teacher support. However, the integration of these technologies also raises ethical considerations, such as privacy, confidentiality, and bias. The viewpoint paper provides a comprehensive overview of the current state of AI and chatbots in nursing education, offering insights into best practices and guidelines for their integration. By examining the impact of AI and ChatGPT on student learning, engagement, and teacher effectiveness and efficiency, this review aims to contribute to the ongoing discussion on the use of AI and chatbots in nursing education and provide recommendations for future research and development in the field.
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Affiliation(s)
| | - Ambili Venugopal
- College of Nursing, All India Institute of Medical Sciences, Mangalagiri, India
| | - Latha Venkatesan
- College of Nursing, All India Institute of Medical Sciences, New Delhi, India
| | - Rajesh Kumar
- College of Nursing, All India Institute of Medical Sciences, Rishikesh, India
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Loughran E, Kane M, Wyatt TH, Kerley A, Lowe S, Li X. Using Large Language Models to Address Health Literacy in mHealth: Case Report. Comput Inform Nurs 2024:00024665-990000000-00193. [PMID: 38832874 DOI: 10.1097/cin.0000000000001152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2024]
Abstract
The innate complexity of medical topics often makes it challenging to produce educational content for the public. Although there are resources available to help authors appraise the complexity of their content, there are woefully few resources available to help authors reduce that complexity after it occurs. In this case study, we evaluate using ChatGPT to reduce the complex language used in health-related educational materials. ChatGPT adapted content from the SmartSHOTS mobile application, which is geared toward caregivers of children aged 0 to 24 months. SmartSHOTS helps reduce barriers and improve adherence to vaccination schedules. ChatGPT reduced complex sentence structure and rewrote content to align with a third-grade reading level. Furthermore, using ChatGPT to edit content already written removes the potential for unnoticed, artificial intelligence-produced inaccuracies. As an editorial tool, ChatGPT was effective, efficient, and free to use. This article discusses the potential of ChatGPT as an effective, time-efficient, and open-source method for editing health-related educational materials to reflect a comprehendible reading level.
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Hobensack M, von Gerich H, Vyas P, Withall J, Peltonen LM, Block LJ, Davies S, Chan R, Van Bulck L, Cho H, Paquin R, Mitchell J, Topaz M, Song J. A rapid review on current and potential uses of large language models in nursing. Int J Nurs Stud 2024; 154:104753. [PMID: 38560958 DOI: 10.1016/j.ijnurstu.2024.104753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 03/04/2024] [Accepted: 03/06/2024] [Indexed: 04/04/2024]
Abstract
BACKGROUND The application of large language models across commercial and consumer contexts has grown exponentially in recent years. However, a gap exists in the literature on how large language models can support nursing practice, education, and research. This study aimed to synthesize the existing literature on current and potential uses of large language models across the nursing profession. METHODS A rapid review of the literature, guided by Cochrane rapid review methodology and PRISMA reporting standards, was conducted. An expert health librarian assisted in developing broad inclusion criteria to account for the emerging nature of literature related to large language models. Three electronic databases (i.e., PubMed, CINAHL, and Embase) were searched to identify relevant literature in August 2023. Articles that discussed the development, use, and application of large language models within nursing were included for analysis. RESULTS The literature search identified a total of 2028 articles that met the inclusion criteria. After systematically reviewing abstracts, titles, and full texts, 30 articles were included in the final analysis. Nearly all (93 %; n = 28) of the included articles used ChatGPT as an example, and subsequently discussed the use and value of large language models in nursing education (47 %; n = 14), clinical practice (40 %; n = 12), and research (10 %; n = 3). While the most common assessment of large language models was conducted by human evaluation (26.7 %; n = 8), this analysis also identified common limitations of large language models in nursing, including lack of systematic evaluation, as well as other ethical and legal considerations. DISCUSSION This is the first review to summarize contemporary literature on current and potential uses of large language models in nursing practice, education, and research. Although there are significant opportunities to apply large language models, the use and adoption of these models within nursing have elicited a series of challenges, such as ethical issues related to bias, misuse, and plagiarism. CONCLUSION Given the relative novelty of large language models, ongoing efforts to develop and implement meaningful assessments, evaluations, standards, and guidelines for applying large language models in nursing are recommended to ensure appropriate, accurate, and safe use. Future research along with clinical and educational partnerships is needed to enhance understanding and application of large language models in nursing and healthcare.
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Affiliation(s)
- Mollie Hobensack
- Brookdale Department of Geriatrics and Palliative Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
| | | | - Pankaj Vyas
- College of Nursing, University of Arizona, Tucson, AZ, USA
| | - Jennifer Withall
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Laura-Maria Peltonen
- Department of Nursing Science, University of Turku, Research Services, Turku University Hospital, Finland
| | - Lorraine J Block
- School of Nursing, University of British Columbia, Vancouver, Canada
| | - Shauna Davies
- Faculty of Nursing, University of Regina, Regina, Canada
| | - Ryan Chan
- Arthur Labatt Family School of Nursing, Western University, London, ON, Canada
| | - Liesbet Van Bulck
- Department of Public Health and Primary Care, KU Leuven - University of Leuven, Leuven, Belgium
| | - Hwayoung Cho
- College of Nursing, University of Florida, Gainesville, FL, USA
| | - Robert Paquin
- Faculty of Nursing, Midwifery, and Palliative Care, King's College London, London, UK
| | - James Mitchell
- Department of Biomedical Informatics, University of Colorado School of Medicine, Denver, CO, USA
| | - Maxim Topaz
- Columbia University School of Nursing, Data Science Institute, Columbia University, VNS Health, New York, NY, USA
| | - Jiyoun Song
- Department of Biobehavioral Health Sciences, University of Pennsylvania School of Nursing, Philadelphia, PA, USA
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Zhou B, Mui LG. Utilising chatbots in clinical nursing education: Application and obstacles. J Clin Nurs 2024; 33:2362-2363. [PMID: 38407407 DOI: 10.1111/jocn.17089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2024] [Accepted: 02/20/2024] [Indexed: 02/27/2024]
Affiliation(s)
- Bo Zhou
- Faculty of Medicine, Bioscience and Nursing, MAHSA University, Jenjarom, Malaysia
| | - Lim Gek Mui
- Faculty of Medicine, Bioscience and Nursing, MAHSA University, Jenjarom, Malaysia
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Choudhury A, Shamszare H. The Impact of Performance Expectancy, Workload, Risk, and Satisfaction on Trust in ChatGPT: Cross-Sectional Survey Analysis. JMIR Hum Factors 2024; 11:e55399. [PMID: 38801658 PMCID: PMC11165287 DOI: 10.2196/55399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 03/25/2024] [Accepted: 04/07/2024] [Indexed: 05/29/2024] Open
Abstract
BACKGROUND ChatGPT (OpenAI) is a powerful tool for a wide range of tasks, from entertainment and creativity to health care queries. There are potential risks and benefits associated with this technology. In the discourse concerning the deployment of ChatGPT and similar large language models, it is sensible to recommend their use primarily for tasks a human user can execute accurately. As we transition into the subsequent phase of ChatGPT deployment, establishing realistic performance expectations and understanding users' perceptions of risk associated with its use are crucial in determining the successful integration of this artificial intelligence (AI) technology. OBJECTIVE The aim of the study is to explore how perceived workload, satisfaction, performance expectancy, and risk-benefit perception influence users' trust in ChatGPT. METHODS A semistructured, web-based survey was conducted with 607 adults in the United States who actively use ChatGPT. The survey questions were adapted from constructs used in various models and theories such as the technology acceptance model, the theory of planned behavior, the unified theory of acceptance and use of technology, and research on trust and security in digital environments. To test our hypotheses and structural model, we used the partial least squares structural equation modeling method, a widely used approach for multivariate analysis. RESULTS A total of 607 people responded to our survey. A significant portion of the participants held at least a high school diploma (n=204, 33.6%), and the majority had a bachelor's degree (n=262, 43.1%). The primary motivations for participants to use ChatGPT were for acquiring information (n=219, 36.1%), amusement (n=203, 33.4%), and addressing problems (n=135, 22.2%). Some participants used it for health-related inquiries (n=44, 7.2%), while a few others (n=6, 1%) used it for miscellaneous activities such as brainstorming, grammar verification, and blog content creation. Our model explained 64.6% of the variance in trust. Our analysis indicated a significant relationship between (1) workload and satisfaction, (2) trust and satisfaction, (3) performance expectations and trust, and (4) risk-benefit perception and trust. CONCLUSIONS The findings underscore the importance of ensuring user-friendly design and functionality in AI-based applications to reduce workload and enhance user satisfaction, thereby increasing user trust. Future research should further explore the relationship between risk-benefit perception and trust in the context of AI chatbots.
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Affiliation(s)
- Avishek Choudhury
- Industrial and Management Systems Engineering, Benjamin M. Statler College of Engineering and Mineral Resources, West Virginia University, Morgantown, WV, United States
| | - Hamid Shamszare
- Industrial and Management Systems Engineering, Benjamin M. Statler College of Engineering and Mineral Resources, West Virginia University, Morgantown, WV, United States
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Shen OY, Pratap JS, Li X, Chen NC, Bhashyam AR. How Does ChatGPT Use Source Information Compared With Google? A Text Network Analysis of Online Health Information. Clin Orthop Relat Res 2024; 482:578-588. [PMID: 38517757 PMCID: PMC10936961 DOI: 10.1097/corr.0000000000002995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Accepted: 01/08/2024] [Indexed: 03/24/2024]
Abstract
BACKGROUND The lay public is increasingly using ChatGPT (a large language model) as a source of medical information. Traditional search engines such as Google provide several distinct responses to each search query and indicate the source for each response, but ChatGPT provides responses in paragraph form in prose without providing the sources used, which makes it difficult or impossible to ascertain whether those sources are reliable. One practical method to infer the sources used by ChatGPT is text network analysis. By understanding how ChatGPT uses source information in relation to traditional search engines, physicians and physician organizations can better counsel patients on the use of this new tool. QUESTIONS/PURPOSES (1) In terms of key content words, how similar are ChatGPT and Google Search responses for queries related to topics in orthopaedic surgery? (2) Does the source distribution (academic, governmental, commercial, or form of a scientific manuscript) differ for Google Search responses based on the topic's level of medical consensus, and how is this reflected in the text similarity between ChatGPT and Google Search responses? (3) Do these results vary between different versions of ChatGPT? METHODS We evaluated three search queries relating to orthopaedic conditions: "What is the cause of carpal tunnel syndrome?," "What is the cause of tennis elbow?," and "Platelet-rich plasma for thumb arthritis?" These were selected because of their relatively high, medium, and low consensus in the medical evidence, respectively. Each question was posed to ChatGPT version 3.5 and version 4.0 20 times for a total of 120 responses. Text network analysis using term frequency-inverse document frequency (TF-IDF) was used to compare text similarity between responses from ChatGPT and Google Search. In the field of information retrieval, TF-IDF is a weighted statistical measure of the importance of a key word to a document in a collection of documents. Higher TF-IDF scores indicate greater similarity between two sources. TF-IDF scores are most often used to compare and rank the text similarity of documents. Using this type of text network analysis, text similarity between ChatGPT and Google Search can be determined by calculating and summing the TF-IDF for all keywords in a ChatGPT response and comparing it with each Google search result to assess their text similarity to each other. In this way, text similarity can be used to infer relative content similarity. To answer our first question, we characterized the text similarity between ChatGPT and Google Search responses by finding the TF-IDF scores of the ChatGPT response and each of the 20 Google Search results for each question. Using these scores, we could compare the similarity of each ChatGPT response to the Google Search results. To provide a reference point for interpreting TF-IDF values, we generated randomized text samples with the same term distribution as the Google Search results. By comparing ChatGPT TF-IDF to the random text sample, we could assess whether TF-IDF values were statistically significant from TF-IDF values obtained by random chance, and it allowed us to test whether text similarity was an appropriate quantitative statistical measure of relative content similarity. To answer our second question, we classified the Google Search results to better understand sourcing. Google Search provides 20 or more distinct sources of information, but ChatGPT gives only a single prose paragraph in response to each query. So, to answer this question, we used TF-IDF to ascertain whether the ChatGPT response was principally driven by one of four source categories: academic, government, commercial, or material that took the form of a scientific manuscript but was not peer-reviewed or indexed on a government site (such as PubMed). We then compared the TF-IDF similarity between ChatGPT responses and the source category. To answer our third research question, we repeated both analyses and compared the results when using ChatGPT 3.5 versus ChatGPT 4.0. RESULTS The ChatGPT response was dominated by the top Google Search result. For example, for carpal tunnel syndrome, the top result was an academic website with a mean TF-IDF of 7.2. A similar result was observed for the other search topics. To provide a reference point for interpreting TF-IDF values, a randomly generated sample of text compared with Google Search would have a mean TF-IDF of 2.7 ± 1.9, controlling for text length and keyword distribution. The observed TF-IDF distribution was higher for ChatGPT responses than for random text samples, supporting the claim that keyword text similarity is a measure of relative content similarity. When comparing source distribution, the ChatGPT response was most similar to the most common source category from Google Search. For the subject where there was strong consensus (carpal tunnel syndrome), the ChatGPT response was most similar to high-quality academic sources rather than lower-quality commercial sources (TF-IDF 8.6 versus 2.2). For topics with low consensus, the ChatGPT response paralleled lower-quality commercial websites compared with higher-quality academic websites (TF-IDF 14.6 versus 0.2). ChatGPT 4.0 had higher text similarity to Google Search results than ChatGPT 3.5 (mean increase in TF-IDF similarity of 0.80 to 0.91; p < 0.001). The ChatGPT 4.0 response was still dominated by the top Google Search result and reflected the most common search category for all search topics. CONCLUSION ChatGPT responses are similar to individual Google Search results for queries related to orthopaedic surgery, but the distribution of source information can vary substantially based on the relative level of consensus on a topic. For example, for carpal tunnel syndrome, where there is widely accepted medical consensus, ChatGPT responses had higher similarity to academic sources and therefore used those sources more. When fewer academic or government sources are available, especially in our search related to platelet-rich plasma, ChatGPT appears to have relied more heavily on a small number of nonacademic sources. These findings persisted even as ChatGPT was updated from version 3.5 to version 4.0. CLINICAL RELEVANCE Physicians should be aware that ChatGPT and Google likely use the same sources for a specific question. The main difference is that ChatGPT can draw upon multiple sources to create one aggregate response, while Google maintains its distinctness by providing multiple results. For topics with a low consensus and therefore a low number of quality sources, there is a much higher chance that ChatGPT will use less-reliable sources, in which case physicians should take the time to educate patients on the topic or provide resources that give more reliable information. Physician organizations should make it clear when the evidence is limited so that ChatGPT can reflect the lack of quality information or evidence.
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Affiliation(s)
- Oscar Y. Shen
- Department of Orthopaedic Surgery, Hand and Upper Extremity Service, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong
| | - Jayanth S. Pratap
- Department of Orthopaedic Surgery, Hand and Upper Extremity Service, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Harvard University, Cambridge, MA, USA
| | - Xiang Li
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Neal C. Chen
- Department of Orthopaedic Surgery, Hand and Upper Extremity Service, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Abhiram R. Bhashyam
- Department of Orthopaedic Surgery, Hand and Upper Extremity Service, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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Pinto VBP, de Azevedo MF, Wroclawski ML, Gentile G, Jesus VLM, de Bessa Junior J, Nahas WC, Sacomani CAR, Sandhu JS, Gomes CM. Conformity of ChatGPT recommendations with the AUA/SUFU guideline on postprostatectomy urinary incontinence. Neurourol Urodyn 2024; 43:935-941. [PMID: 38451040 DOI: 10.1002/nau.25442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 02/24/2024] [Accepted: 02/27/2024] [Indexed: 03/08/2024]
Abstract
INTRODUCTION Artificial intelligence (AI) shows immense potential in medicine and Chat generative pretrained transformer (ChatGPT) has been used for different purposes in the field. However, it may not match the complexity and nuance of certain medical scenarios. This study evaluates the accuracy of ChatGPT 3.5 and 4 in providing recommendations regarding the management of postprostatectomy urinary incontinence (PPUI), considering The Incontinence After Prostate Treatment: AUA/SUFU Guideline as the best practice benchmark. MATERIALS AND METHODS A set of questions based on the AUA/SUFU Guideline was prepared. Queries included 10 conceptual questions and 10 case-based questions. All questions were open and entered into the ChatGPT with a recommendation to limit the answer to 200 words, for greater objectivity. Responses were graded as correct (1 point); partially correct (0.5 point), or incorrect (0 point). Performances of versions 3.5 and 4 of ChatGPT were analyzed overall and separately for the conceptual and the case-based questions. RESULTS ChatGPT 3.5 scored 11.5 out of 20 points (57.5% accuracy), while ChatGPT 4 scored 18 (90.0%; p = 0.031). In the conceptual questions, ChatGPT 3.5 provided accurate answers to six questions along with one partially correct response and three incorrect answers, with a final score of 6.5. In contrast, ChatGPT 4 provided correct answers to eight questions and partially correct answers to two questions, scoring 9.0. In the case-based questions, ChatGPT 3.5 scored 5.0, while ChatGPT 4 scored 9.0. The domains where ChatGPT performed worst were evaluation, treatment options, surgical complications, and special situations. CONCLUSION ChatGPT 4 demonstrated superior performance compared to ChatGPT 3.5 in providing recommendations for the management of PPUI, using the AUA/SUFU Guideline as a benchmark. Continuous monitoring is essential for evaluating the development and precision of AI-generated medical information.
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Affiliation(s)
- Vicktor B P Pinto
- Division of Urology, University of Sao Paulo School of Medicine, Sao Paulo, Brazil
| | - Matheus F de Azevedo
- Division of Urology, University of Sao Paulo School of Medicine, Sao Paulo, Brazil
| | - Marcelo L Wroclawski
- Division of Urology, ABC Medical School, Sao Paulo, Brazil
- Department of Urology, Albert Einstein Jewish Hospital, Sao Paulo, Brazil
- Department of Urologic Oncology, BP-a Beneficência Portuguesa de São Paulo, Sao Paulo, Brazil
| | - Guilherme Gentile
- Division of Urology, University of Sao Paulo School of Medicine, Sao Paulo, Brazil
| | - Vinicius L M Jesus
- Division of Urology, University of Sao Paulo School of Medicine, Sao Paulo, Brazil
| | | | - William C Nahas
- Division of Urology, University of Sao Paulo School of Medicine, Sao Paulo, Brazil
| | - Carlos A R Sacomani
- Innovation and Information Technology Sector, AC Camargo Cancer Hospital, Sao Paulo, Brazil
| | - Jaspreet S Sandhu
- Department of Surgery/Urology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Cristiano M Gomes
- Division of Urology, University of Sao Paulo School of Medicine, Sao Paulo, Brazil
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Shorey S, Mattar C, Pereira TLB, Choolani M. A scoping review of ChatGPT's role in healthcare education and research. NURSE EDUCATION TODAY 2024; 135:106121. [PMID: 38340639 DOI: 10.1016/j.nedt.2024.106121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 01/05/2024] [Accepted: 02/04/2024] [Indexed: 02/12/2024]
Abstract
OBJECTIVES To examine and consolidate literature regarding the advantages and disadvantages of utilizing ChatGPT in healthcare education and research. DESIGN/METHODS We searched seven electronic databases (PubMed/Medline, CINAHL, Embase, PsycINFO, Scopus, ProQuest Dissertations and Theses Global, and Web of Science) from November 2022 until September 2023. This scoping review adhered to Arksey and O'Malley's framework and followed reporting guidelines outlined in the PRISMA-ScR checklist. For analysis, we employed Thomas and Harden's thematic synthesis framework. RESULTS A total of 100 studies were included. An overarching theme, "Forging the Future: Bridging Theory and Integration of ChatGPT" emerged, accompanied by two main themes (1) Enhancing Healthcare Education, Research, and Writing with ChatGPT, (2) Controversies and Concerns about ChatGPT in Healthcare Education Research and Writing, and seven subthemes. CONCLUSIONS Our review underscores the importance of acknowledging legitimate concerns related to the potential misuse of ChatGPT such as 'ChatGPT hallucinations', its limited understanding of specialized healthcare knowledge, its impact on teaching methods and assessments, confidentiality and security risks, and the controversial practice of crediting it as a co-author on scientific papers, among other considerations. Furthermore, our review also recognizes the urgency of establishing timely guidelines and regulations, along with the active engagement of relevant stakeholders, to ensure the responsible and safe implementation of ChatGPT's capabilities. We advocate for the use of cross-verification techniques to enhance the precision and reliability of generated content, the adaptation of higher education curricula to incorporate ChatGPT's potential, educators' need to familiarize themselves with the technology to improve their literacy and teaching approaches, and the development of innovative methods to detect ChatGPT usage. Furthermore, data protection measures should be prioritized when employing ChatGPT, and transparent reporting becomes crucial when integrating ChatGPT into academic writing.
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Affiliation(s)
- Shefaly Shorey
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Citra Mattar
- Division of Maternal Fetal Medicine, Department of Obstetrics and Gynaecology, National University Health Systems, Singapore; Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Travis Lanz-Brian Pereira
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Mahesh Choolani
- Division of Maternal Fetal Medicine, Department of Obstetrics and Gynaecology, National University Health Systems, Singapore; Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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Sharma A, Medapalli T, Alexandrou M, Brilakis E, Prasad A. Exploring the Role of ChatGPT in Cardiology: A Systematic Review of the Current Literature. Cureus 2024; 16:e58936. [PMID: 38800264 PMCID: PMC11124467 DOI: 10.7759/cureus.58936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/23/2024] [Indexed: 05/29/2024] Open
Abstract
Chat Generative Pre-Trained Transformer (ChatGPT) is a chatbot based on a large language model that has gained public interest since its release in November 2022. This systematic review examines the current literature on the potential applications of ChatGPT in cardiology. A systematic literature search was conducted to retrieve all publications on ChatGPT in PubMed, Scopus, MedRxiv, and the Cochrane Library published on or before September 30, 2023. Search terms relating to ChatGPT and cardiology were used. Publications without relevance to ChatGPT and cardiology were excluded. The included publications were divided into cohorts. Cohort A examined ChatGPT's role in improving patient health literacy. Cohort B explored ChatGPT's role in clinical care. Cohort C examined ChatGPT's role in future literature and research. Cohort D included case reports that used ChatGPT. A total of 115 publications were found across all databases. Twenty-four publications met the inclusion criteria and were included in the review. Cohort A-C included a total of 14 records comprised of editorials/letters to the editor (29%), research letters/correspondence (21%), review papers (21%), observational studies (7%), research studies (7%), and short reports (7%). Cohort D included 10 case reports. No relevant systematic literature reviews, meta-analyses, or randomized controlled trials were identified in the search. Based on this review of the literature, ChatGPT has the potential to enhance patient education, support clinicians providing clinical care, and enhance the development of future literature. However, further studies are needed to understand the potential applications of ChatGPT in cardiology and to address ethical concerns regarding the delivery of medical advice and the authoring of manuscripts.
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Affiliation(s)
- Aditi Sharma
- Department of Medicine, Division of Cardiology, University of Texas (UT) Health San Antonio, San Antonio, USA
| | - Tejas Medapalli
- Department of Medicine, Division of Cardiology, University of Texas (UT) Health San Antonio, San Antonio, USA
| | | | | | - Anand Prasad
- Department of Medicine, Division of Cardiology, University of Texas (UT) Health San Antonio, San Antonio, USA
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Moons P, Van De Bruaene A, Van Bulck L. Letters to the editor: questionable publishing practices in the ChatGPT era. Eur J Cardiovasc Nurs 2024; 23:e15-e16. [PMID: 37530474 DOI: 10.1093/eurjcn/zvad073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 07/31/2023] [Indexed: 08/03/2023]
Affiliation(s)
- Philip Moons
- KU Leuven Department of Public Health and Primary Care, KU Leuven-University of Leuven, Kapucijnenvoer 35 PB7001, Leuven 3000, Belgium
- Institute of Health and Care Sciences, University of Gothenburg, Arvid Wallgrens backe 1, Gothenburg 413 46, Sweden
- Department of Paediatrics and Child Health, University of Cape Town, Klipfontein Rd, Rondebosch, Cape Town 7700, South Africa
| | - Alexander Van De Bruaene
- Department of Cardiovascular Sciences, KU Leuven, University of Leuven, Kapucijnenvoer 35 PB7001, Leuven 3000, Belgium
- Division of Congenital and Structural Cardiology, University Hospitals Leuven, Herestraat 49, Leuven 3000, Belgium
| | - Liesbet Van Bulck
- KU Leuven Department of Public Health and Primary Care, KU Leuven-University of Leuven, Kapucijnenvoer 35 PB7001, Leuven 3000, Belgium
- Research Foundation Flanders (FWO), Leuvenseweg 38, Brussels 1000, Belgium
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Moons P, Van Bulck L. Using ChatGPT and Google Bard to improve the readability of written patient information: a proof of concept. Eur J Cardiovasc Nurs 2024; 23:122-126. [PMID: 37603843 DOI: 10.1093/eurjcn/zvad087] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 08/16/2023] [Accepted: 08/17/2023] [Indexed: 08/23/2023]
Abstract
Patient information materials often tend to be written at a reading level that is too advanced for patients. In this proof-of-concept study, we used ChatGPT and Google Bard to reduce the reading level of three selected patient information sections from scientific journals. ChatGPT successfully improved readability. However, it could not achieve the recommended 6th-grade reading level. Bard reached the reading level of 6th graders but oversimplified the texts by omitting up to 83% of the content. Despite the present limitations, developers of patient information are encouraged to employ large language models, preferably ChatGPT, to optimize their materials.
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Affiliation(s)
- Philip Moons
- KU Leuven Department of Public Health and Primary Care, KU Leuven-University of Leuven, Kapucijnenvoer 35 PB7001, 3000 Leuven, Belgium
- Institute of Health and Care Sciences, University of Gothenburg, Arvid Wallgrens backe 1, 413 46 Gothenburg, Sweden
- Department of Paediatrics and Child Health, University of Cape Town, Klipfontein Rd, Rondebosch, 7700 Cape Town, South Africa
| | - Liesbet Van Bulck
- KU Leuven Department of Public Health and Primary Care, KU Leuven-University of Leuven, Kapucijnenvoer 35 PB7001, 3000 Leuven, Belgium
- Research Foundation Flanders (FWO), Leuvenseweg 38, 1000 Brussels, Belgium
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12
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Mu Y, He D. The Potential Applications and Challenges of ChatGPT in the Medical Field. Int J Gen Med 2024; 17:817-826. [PMID: 38476626 PMCID: PMC10929156 DOI: 10.2147/ijgm.s456659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 02/26/2024] [Indexed: 03/14/2024] Open
Abstract
ChatGPT, an AI-driven conversational large language model (LLM), has garnered significant scholarly attention since its inception, owing to its manifold applications in the realm of medical science. This study primarily examines the merits, limitations, anticipated developments, and practical applications of ChatGPT in clinical practice, healthcare, medical education, and medical research. It underscores the necessity for further research and development to enhance its performance and deployment. Moreover, future research avenues encompass ongoing enhancements and standardization of ChatGPT, mitigating its limitations, and exploring its integration and applicability in translational and personalized medicine. Reflecting the narrative nature of this review, a focused literature search was performed to identify relevant publications on ChatGPT's use in medicine. This process was aimed at gathering a broad spectrum of insights to provide a comprehensive overview of the current state and future prospects of ChatGPT in the medical domain. The objective is to aid healthcare professionals in understanding the groundbreaking advancements associated with the latest artificial intelligence tools, while also acknowledging the opportunities and challenges presented by ChatGPT.
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Affiliation(s)
- Yonglin Mu
- Department of Urology, Children’s Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Dawei He
- Department of Urology, Children’s Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
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13
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Mese I. Letter to the editor - Leveraging virtual reality-augmented reality technologies to complement artificial intelligence-driven healthcare: the future of patient-doctor consultations. Eur J Cardiovasc Nurs 2024; 23:e9-e10. [PMID: 37160759 DOI: 10.1093/eurjcn/zvad043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 05/02/2023] [Accepted: 05/06/2023] [Indexed: 05/11/2023]
Affiliation(s)
- Ismail Mese
- Department of Radiology, Health Sciences University Erenkoy Mental Health and Neurology Training and Research Hospital, 19 mayis neighborhood, Sinan Ercan Road, No:23, Kadikoy/Istanbul, 34736, Turkey
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14
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Van Bulck L, Moons P. Response to the letter to the editor - Dr. ChatGPT in cardiovascular nursing: a deeper dive into trustworthiness, value, and potential risk. Eur J Cardiovasc Nurs 2024; 23:e13-e14. [PMID: 37194466 DOI: 10.1093/eurjcn/zvad049] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 05/15/2023] [Indexed: 05/18/2023]
Affiliation(s)
- Liesbet Van Bulck
- Center for Health Services & Nursing Research, KU Leuven Biomedical Sciences Group: Katholieke Universiteit Leuven Groep Biomedische Wetenschappen, Kapucijnenvoer 35 PB7001, Leuven BE-3000, Belgium
| | - Philip Moons
- Center for Health Services & Nursing Research, KU Leuven Biomedical Sciences Group: Katholieke Universiteit Leuven Groep Biomedische Wetenschappen, Kapucijnenvoer 35 PB7001, Leuven BE-3000, Belgium
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15
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Ray PP. How true is the role of large language models in nursing? Eur J Cardiovasc Nurs 2024:zvad123. [PMID: 38195878 DOI: 10.1093/eurjcn/zvad123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Accepted: 11/27/2023] [Indexed: 01/11/2024]
Affiliation(s)
- Partha Pratim Ray
- Department of Computer Applications, Sikkim University, Gangtok 737102, India
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16
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Woo B, Huynh T, Tang A, Bui N, Nguyen G, Tam W. Transforming nursing with large language models: from concept to practice. Eur J Cardiovasc Nurs 2024:zvad120. [PMID: 38178303 DOI: 10.1093/eurjcn/zvad120] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 11/19/2023] [Indexed: 01/06/2024]
Abstract
Large language models (LLMs) such as ChatGPT have emerged as potential game-changers in nursing, aiding in patient education, diagnostic assistance, treatment recommendations, and administrative task efficiency. While these advancements signal promising strides in healthcare, integrated LLMs are not without challenges, particularly artificial intelligence hallucination and data privacy concerns. Methodologies such as prompt engineering, temperature adjustments, model fine-tuning, and local deployment are proposed to refine the accuracy of LLMs and ensure data security. While LLMs offer transformative potential, it is imperative to acknowledge that they cannot substitute the intricate expertise of human professionals in the clinical field, advocating for a synergistic approach in patient care.
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Affiliation(s)
- Brigitte Woo
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Tom Huynh
- School of Science, Engineering and Technology, RMIT University, 702 Nguyen Van Linh Blvd., District 7, Ho Chin Minh 756000, Ho Chin Minh City, Vietnam
| | - Arthur Tang
- School of Science, Engineering and Technology, RMIT University, 702 Nguyen Van Linh Blvd., District 7, Ho Chin Minh 756000, Ho Chin Minh City, Vietnam
| | - Nhat Bui
- School of Science, Engineering and Technology, RMIT University, 702 Nguyen Van Linh Blvd., District 7, Ho Chin Minh 756000, Ho Chin Minh City, Vietnam
| | - Giang Nguyen
- School of Science, Engineering and Technology, RMIT University, 702 Nguyen Van Linh Blvd., District 7, Ho Chin Minh 756000, Ho Chin Minh City, Vietnam
| | - Wilson Tam
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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Almeida LC, Farina EMJM, Kuriki PEA, Abdala N, Kitamura FC. Performance of ChatGPT on the Brazilian Radiology and Diagnostic Imaging and Mammography Board Examinations. Radiol Artif Intell 2024; 6:e230103. [PMID: 38294325 PMCID: PMC10831524 DOI: 10.1148/ryai.230103] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 09/06/2023] [Accepted: 10/23/2023] [Indexed: 02/01/2024]
Abstract
This prospective exploratory study conducted from January 2023 through May 2023 evaluated the ability of ChatGPT to answer questions from Brazilian radiology board examinations, exploring how different prompt strategies can influence performance using GPT-3.5 and GPT-4. Three multiple-choice board examinations that did not include image-based questions were evaluated: (a) radiology and diagnostic imaging, (b) mammography, and (c) neuroradiology. Five different styles of zero-shot prompting were tested: (a) raw question, (b) brief instruction, (c) long instruction, (d) chain-of-thought, and (e) question-specific automatic prompt generation (QAPG). The QAPG and brief instruction prompt strategies performed best for all examinations (P < .05), obtaining passing scores (≥60%) on the radiology and diagnostic imaging examination when testing both versions of ChatGPT. The QAPG style achieved a score of 60% for the mammography examination using GPT-3.5 and 76% using GPT-4. GPT-4 achieved a score up to 65% in the neuroradiology examination. The long instruction style consistently underperformed, implying that excessive detail might harm performance. GPT-4's scores were less sensitive to prompt style changes. The QAPG prompt style showed a high volume of the "A" option but no statistical difference, suggesting bias was found. GPT-4 passed all three radiology board examinations, and GPT-3.5 passed two of three examinations when using an optimal prompt style. Keywords: ChatGPT, Artificial Intelligence, Board Examinations, Radiology and Diagnostic Imaging, Mammography, Neuroradiology © RSNA, 2023 See also the commentary by Trivedi and Gichoya in this issue.
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Affiliation(s)
- Leonardo C. Almeida
- From the Department of Artificial Intelligence and Management (L.C.A., E.M.J.M.F., N.A., F.C.K.), Graduate Program in Medicine (Clinical Radiology), Universidade Federal de São Paulo (UNIFESP), Rua Botucatu, 740, 04023-062, São Paulo, São Paulo, Brazil; AI Lab (L.C.A., E.M.J.M.F., P.E.A.K., F.C.K.), Dasa, São Paulo, São Paulo, Brazil
| | - Eduardo M. J. M. Farina
- From the Department of Artificial Intelligence and Management (L.C.A., E.M.J.M.F., N.A., F.C.K.), Graduate Program in Medicine (Clinical Radiology), Universidade Federal de São Paulo (UNIFESP), Rua Botucatu, 740, 04023-062, São Paulo, São Paulo, Brazil; AI Lab (L.C.A., E.M.J.M.F., P.E.A.K., F.C.K.), Dasa, São Paulo, São Paulo, Brazil
| | - Paulo E. A. Kuriki
- From the Department of Artificial Intelligence and Management (L.C.A., E.M.J.M.F., N.A., F.C.K.), Graduate Program in Medicine (Clinical Radiology), Universidade Federal de São Paulo (UNIFESP), Rua Botucatu, 740, 04023-062, São Paulo, São Paulo, Brazil; AI Lab (L.C.A., E.M.J.M.F., P.E.A.K., F.C.K.), Dasa, São Paulo, São Paulo, Brazil
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Huang X, Estau D, Liu X, Yu Y, Qin J, Li Z. Evaluating the performance of ChatGPT in clinical pharmacy: A comparative study of ChatGPT and clinical pharmacists. Br J Clin Pharmacol 2024; 90:232-238. [PMID: 37626010 DOI: 10.1111/bcp.15896] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 08/01/2023] [Accepted: 08/14/2023] [Indexed: 08/27/2023] Open
Abstract
AIMS To evaluate the performance of chat generative pretrained transformer (ChatGPT) in key domains of clinical pharmacy practice, including prescription review, patient medication education, adverse drug reaction (ADR) recognition, ADR causality assessment and drug counselling. METHODS Questions and clinical pharmacist's answers were collected from real clinical cases and clinical pharmacist competency assessment. ChatGPT's responses were generated by inputting the same question into the 'New Chat' box of ChatGPT Mar 23 Version. Five licensed clinical pharmacists independently rated these answers on a scale of 0 (Completely incorrect) to 10 (Completely correct). The mean scores of ChatGPT and clinical pharmacists were compared using a paired 2-tailed Student's t-test. The text content of the answers was also descriptively summarized together. RESULTS The quantitative results indicated that ChatGPT was excellent in drug counselling (ChatGPT: 8.77 vs. clinical pharmacist: 9.50, P = .0791) and weak in prescription review (5.23 vs. 9.90, P = .0089), patient medication education (6.20 vs. 9.07, P = .0032), ADR recognition (5.07 vs. 9.70, P = .0483) and ADR causality assessment (4.03 vs. 9.73, P = .023). The capabilities and limitations of ChatGPT in clinical pharmacy practice were summarized based on the completeness and accuracy of the answers. ChatGPT revealed robust retrieval, information integration and dialogue capabilities. It lacked medicine-specific datasets as well as the ability for handling advanced reasoning and complex instructions. CONCLUSIONS While ChatGPT holds promise in clinical pharmacy practice as a supplementary tool, the ability of ChatGPT to handle complex problems needs further improvement and refinement.
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Affiliation(s)
- Xiaoru Huang
- Department of Pharmacy, Peking University Third Hospital, Beijing, China
- Department of Pharmaceutical Management and Clinical Pharmacy, College of Pharmacy, Peking University, Beijing, China
| | - Dannya Estau
- Department of Pharmacy, Peking University Third Hospital, Beijing, China
- Department of Pharmaceutical Management and Clinical Pharmacy, College of Pharmacy, Peking University, Beijing, China
| | - Xuening Liu
- Department of Pharmacy, Peking University Third Hospital, Beijing, China
- Department of Pharmaceutical Management and Clinical Pharmacy, College of Pharmacy, Peking University, Beijing, China
| | - Yang Yu
- Department of Pharmacy, Peking University Third Hospital, Beijing, China
- Department of Pharmaceutical Management and Clinical Pharmacy, College of Pharmacy, Peking University, Beijing, China
| | - Jiguang Qin
- Department of Pharmacy, Peking University Third Hospital, Beijing, China
- Department of Pharmaceutical Management and Clinical Pharmacy, College of Pharmacy, Peking University, Beijing, China
| | - Zijian Li
- Department of Pharmacy, Peking University Third Hospital, Beijing, China
- Department of Pharmaceutical Management and Clinical Pharmacy, College of Pharmacy, Peking University, Beijing, China
- Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Beijing Key Laboratory of Cardiovascular Receptors Research, Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Ministry of Health, State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing, China
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Hillmann HAK, Angelini E, Karfoul N, Feickert S, Mueller-Leisse J, Duncker D. Accuracy and comprehensibility of chat-based artificial intelligence for patient information on atrial fibrillation and cardiac implantable electronic devices. Europace 2023; 26:euad369. [PMID: 38127304 PMCID: PMC10824484 DOI: 10.1093/europace/euad369] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 12/19/2023] [Indexed: 12/23/2023] Open
Abstract
AIMS Natural language processing chatbots (NLPC) can be used to gather information for medical content. However, these tools contain a potential risk of misinformation. This study aims to evaluate different aspects of responses given by different NLPCs on questions about atrial fibrillation (AF) and clinical implantable electronic devices (CIED). METHODS AND RESULTS Questions were entered into three different NLPC interfaces. Responses were evaluated with regard to appropriateness, comprehensibility, appearance of confabulation, absence of relevant content, and recommendations given for clinically relevant decisions. Moreover, readability was assessed by calculating word count and Flesch Reading Ease score. 52, 60, and 84% of responses on AF and 16, 72, and 88% on CIEDs were evaluated to be appropriate for all responses given by Google Bard, (GB) Bing Chat (BC) and ChatGPT Plus (CGP), respectively. Assessment of comprehensibility showed that 96, 88, and 92% of responses on AF and 92 and 88%, and 100% on CIEDs were comprehensible for all responses created by GB, BC, and CGP, respectively. Readability varied between different NLPCs. Relevant aspects were missing in 52% (GB), 60% (BC), and 24% (CGP) for AF, and in 92% (GB), 88% (BC), and 52% (CGP) for CIEDs. CONCLUSION Responses generated by an NLPC are mostly easy to understand with varying readability between the different NLPCs. The appropriateness of responses is limited and varies between different NLPCs. Important aspects are often missed to be mentioned. Thus, chatbots should be used with caution to gather medical information about cardiac arrhythmias and devices.
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Affiliation(s)
- Henrike A K Hillmann
- Hannover Heart Rhythm Center, Department of Cardiology and Angiology, Hannover Medical School, Carl-Neuberg-Str. 1, 30625 Hannover, Germany
| | - Eleonora Angelini
- Hannover Heart Rhythm Center, Department of Cardiology and Angiology, Hannover Medical School, Carl-Neuberg-Str. 1, 30625 Hannover, Germany
| | - Nizar Karfoul
- Hannover Heart Rhythm Center, Department of Cardiology and Angiology, Hannover Medical School, Carl-Neuberg-Str. 1, 30625 Hannover, Germany
| | - Sebastian Feickert
- Department of Cardiology and Internal Intensive Care Unit, Vivantes Clinic Am Urban, Dieffenbachstraße 1, 10967 Berlin, Germany
- Department of Cardiology, University Medical Center Rostock, Ernst-Heydemann-Straße 6, 18057 Rostock, Germany
| | - Johanna Mueller-Leisse
- Hannover Heart Rhythm Center, Department of Cardiology and Angiology, Hannover Medical School, Carl-Neuberg-Str. 1, 30625 Hannover, Germany
| | - David Duncker
- Hannover Heart Rhythm Center, Department of Cardiology and Angiology, Hannover Medical School, Carl-Neuberg-Str. 1, 30625 Hannover, Germany
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Harrington L. ChatGPT Is Trending: Trust but Verify. AACN Adv Crit Care 2023; 34:280-286. [PMID: 37619604 DOI: 10.4037/aacnacc2023129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/26/2023]
Affiliation(s)
- Linda Harrington
- Linda Harrington is an Independent Consultant, Health Informatics and Digital Strategy, and Adjunct Faculty at Texas Christian University, 2800 South University Drive, Fort Worth, TX 76109
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Bagde H, Dhopte A, Alam MK, Basri R. A systematic review and meta-analysis on ChatGPT and its utilization in medical and dental research. Heliyon 2023; 9:e23050. [PMID: 38144348 PMCID: PMC10746423 DOI: 10.1016/j.heliyon.2023.e23050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Revised: 10/24/2023] [Accepted: 11/24/2023] [Indexed: 12/26/2023] Open
Abstract
Since its release, ChatGPT has taken the world by storm with its utilization in various fields of life. This review's main goal was to offer a thorough and fact-based evaluation of ChatGPT's potential as a tool for medical and dental research, which could direct subsequent research and influence clinical practices. METHODS Different online databases were scoured for relevant articles that were in accordance with the study objectives. A team of reviewers was assembled to devise a proper methodological framework for inclusion of articles and meta-analysis. RESULTS 11 descriptive studies were considered for this review that evaluated the accuracy of ChatGPT in answering medical queries related to different domains such as systematic reviews, cancer, liver diseases, diagnostic imaging, education, and COVID-19 vaccination. The studies reported different accuracy ranges, from 18.3 % to 100 %, across various datasets and specialties. The meta-analysis showed an odds ratio (OR) of 2.25 and a relative risk (RR) of 1.47 with a 95 % confidence interval (CI), indicating that the accuracy of ChatGPT in providing correct responses was significantly higher compared to the total responses for queries. However, significant heterogeneity was present among the studies, suggesting considerable variability in the effect sizes across the included studies. CONCLUSION The observations indicate that ChatGPT has the ability to provide appropriate solutions to questions in the medical and dentistry areas, but researchers and doctors should cautiously assess its responses because they might not always be dependable. Overall, the importance of this study rests in shedding light on ChatGPT's accuracy in the medical and dentistry fields and emphasizing the need for additional investigation to enhance its performance. © 2017 Elsevier Inc. All rights reserved.
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Affiliation(s)
- Hiroj Bagde
- Department of Periodontology, Chhattisgarh Dental College and Research Institute, Rajnandgaon, Chhattisgarh, India
| | - Ashwini Dhopte
- Department of Oral Medicine and Radiology, Chhattisgarh Dental College and Research Institute, Rajnandgaon, Chhattisgarh, India
| | - Mohammad Khursheed Alam
- Preventive Dentistry Department, College of Dentistry, Jouf University, Sakaka, 72345, Saudi Arabia
- Department of Dental Research Cell, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Chennai, India
- Department of Public Health, Faculty of Allied Health Sciences, Daffodil International University, Dhaka, Bangladesh
| | - Rehana Basri
- Department of Internal Medicine, College of Medicine, Jouf University, Sakaka, 72345, Saudi Arabia
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Wang X, Sanders HM, Liu Y, Seang K, Tran BX, Atanasov AG, Qiu Y, Tang S, Car J, Wang YX, Wong TY, Tham YC, Chung KC. ChatGPT: promise and challenges for deployment in low- and middle-income countries. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2023; 41:100905. [PMID: 37731897 PMCID: PMC10507635 DOI: 10.1016/j.lanwpc.2023.100905] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 08/14/2023] [Accepted: 09/03/2023] [Indexed: 09/22/2023]
Abstract
In low- and middle-income countries (LMICs), the fields of medicine and public health grapple with numerous challenges that continue to hinder patients' access to healthcare services. ChatGPT, a publicly accessible chatbot, has emerged as a potential tool in aiding public health efforts in LMICs. This viewpoint details the potential benefits of employing ChatGPT in LMICs to improve medicine and public health encompassing a broad spectrum of domains ranging from health literacy, screening, triaging, remote healthcare support, mental health support, multilingual capabilities, healthcare communication and documentation, medical training and education, and support for healthcare professionals. Additionally, we also share potential concerns and limitations associated with the use of ChatGPT and provide a balanced discussion on the opportunities and challenges of using ChatGPT in LMICs.
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Affiliation(s)
- Xiaofei Wang
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Hayley M. Sanders
- Section of Plastic Surgery, Department of Surgery, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Yuchen Liu
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Kennarey Seang
- Grant Management Office, University of Health Sciences, Phnom Penh, Cambodia
| | - Bach Xuan Tran
- Department of Health Economics, Institute for Preventive Medicine and Public Health, Hanoi Medical University, Hanoi, Vietnam
- Institute of Health Economics and Technology, Hanoi, Vietnam
| | - Atanas G. Atanasov
- Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria
- Institute of Genetics and Animal Biotechnology of the Polish Academy of Sciences, Jastrzebiec, 05-552, Magdalenka, Poland
| | - Yue Qiu
- Institute for Hospital Management, Tsinghua University, Beijing, China
| | - Shenglan Tang
- Duke Global Health Institute, Duke University, Durham, NC, USA
| | - Josip Car
- Centre for Population Health Sciences, Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore
- Department of Primary Care and Public Health, School of Public Health, Imperial College London, London, United Kingdom
| | - Ya Xing Wang
- Beijing Institute of Ophthalmology, Beijing Ophthalmology and Visual Science Key Lab, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Tsinghua Medicine, Tsinghua University, Beijing, China
- School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Beijing, China
| | - Yih-Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Centre for Innovation and Precision Eye Health, Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Ophthalmology and Visual Science Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Kevin C. Chung
- Section of Plastic Surgery, Department of Surgery, University of Michigan Medical School, Ann Arbor, MI, USA
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23
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Gödde D, Nöhl S, Wolf C, Rupert Y, Rimkus L, Ehlers J, Breuckmann F, Sellmann T. A SWOT (Strengths, Weaknesses, Opportunities, and Threats) Analysis of ChatGPT in the Medical Literature: Concise Review. J Med Internet Res 2023; 25:e49368. [PMID: 37865883 PMCID: PMC10690535 DOI: 10.2196/49368] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 09/26/2023] [Accepted: 09/27/2023] [Indexed: 10/23/2023] Open
Abstract
BACKGROUND ChatGPT is a 175-billion-parameter natural language processing model that is already involved in scientific content and publications. Its influence ranges from providing quick access to information on medical topics, assisting in generating medical and scientific articles and papers, performing medical data analyses, and even interpreting complex data sets. OBJECTIVE The future role of ChatGPT remains uncertain and a matter of debate already shortly after its release. This review aimed to analyze the role of ChatGPT in the medical literature during the first 3 months after its release. METHODS We performed a concise review of literature published in PubMed from December 1, 2022, to March 31, 2023. To find all publications related to ChatGPT or considering ChatGPT, the search term was kept simple ("ChatGPT" in AllFields). All publications available as full text in German or English were included. All accessible publications were evaluated according to specifications by the author team (eg, impact factor, publication modus, article type, publication speed, and type of ChatGPT integration or content). The conclusions of the articles were used for later SWOT (strengths, weaknesses, opportunities, and threats) analysis. All data were analyzed on a descriptive basis. RESULTS Of 178 studies in total, 160 met the inclusion criteria and were evaluated. The average impact factor was 4.423 (range 0-96.216), and the average publication speed was 16 (range 0-83) days. Among the articles, there were 77 editorials (48,1%), 43 essays (26.9%), 21 studies (13.1%), 6 reviews (3.8%), 6 case reports (3.8%), 6 news (3.8%), and 1 meta-analysis (0.6%). Of those, 54.4% (n=87) were published as open access, with 5% (n=8) provided on preprint servers. Over 400 quotes with information on strengths, weaknesses, opportunities, and threats were detected. By far, most (n=142, 34.8%) were related to weaknesses. ChatGPT excels in its ability to express ideas clearly and formulate general contexts comprehensibly. It performs so well that even experts in the field have difficulty identifying abstracts generated by ChatGPT. However, the time-limited scope and the need for corrections by experts were mentioned as weaknesses and threats of ChatGPT. Opportunities include assistance in formulating medical issues for nonnative English speakers, as well as the possibility of timely participation in the development of such artificial intelligence tools since it is in its early stages and can therefore still be influenced. CONCLUSIONS Artificial intelligence tools such as ChatGPT are already part of the medical publishing landscape. Despite their apparent opportunities, policies and guidelines must be implemented to ensure benefits in education, clinical practice, and research and protect against threats such as scientific misconduct, plagiarism, and inaccuracy.
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Affiliation(s)
- Daniel Gödde
- Department of Pathology and Molecularpathology, Helios University Hospital Wuppertal, Witten/Herdecke University, Witten, Germany
| | - Sophia Nöhl
- Faculty of Health, Witten/Herdecke University, Witten, Germany
| | - Carina Wolf
- Faculty of Health, Witten/Herdecke University, Witten, Germany
| | - Yannick Rupert
- Faculty of Health, Witten/Herdecke University, Witten, Germany
| | - Lukas Rimkus
- Faculty of Health, Witten/Herdecke University, Witten, Germany
| | - Jan Ehlers
- Department of Didactics and Education Research in the Health Sector, Faculty of Health, Witten/Herdecke University, Witten, Germany
| | - Frank Breuckmann
- Department of Cardiology and Vascular Medicine, West German Heart and Vascular Center Essen, University Duisburg-Essen, Essen, Germany
- Department of Cardiology, Pneumology, Neurology and Intensive Care Medicine, Klinik Kitzinger Land, Kitzingen, Germany
| | - Timur Sellmann
- Department of Anaesthesiology I, Witten/Herdecke University, Witten, Germany
- Department of Anaesthesiology and Intensive Care Medicine, Evangelisches Krankenhaus BETHESDA zu Duisburg, Duisburg, Germany
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Ray PP. Generative AI: a new dawn in cardiovascular study and research. Indian J Thorac Cardiovasc Surg 2023; 39:654-655. [PMID: 37885931 PMCID: PMC10597933 DOI: 10.1007/s12055-023-01592-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Accepted: 08/21/2023] [Indexed: 10/28/2023] Open
Affiliation(s)
- Partha Pratim Ray
- Department of Computer Applications, Sikkim University, 6th Mile, PO-Tadong, Gangtok, Sikkim 737102 India
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Abujaber AA, Abd-Alrazaq A, Al-Qudimat AR, Nashwan AJ. A Strengths, Weaknesses, Opportunities, and Threats (SWOT) Analysis of ChatGPT Integration in Nursing Education: A Narrative Review. Cureus 2023; 15:e48643. [PMID: 38090452 PMCID: PMC10711690 DOI: 10.7759/cureus.48643] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/11/2023] [Indexed: 03/25/2024] Open
Abstract
Amidst evolving healthcare demands, nursing education plays a pivotal role in preparing future nurses for complex challenges. Traditional approaches, however, must be revised to meet modern healthcare needs. The ChatGPT, an AI-based chatbot, has garnered significant attention due to its ability to personalize learning experiences, enhance virtual clinical simulations, and foster collaborative learning in nursing education. This review aims to thoroughly assess the potential impact of integrating ChatGPT into nursing education. The hypothesis is that valuable insights can be provided for stakeholders through a comprehensive SWOT analysis examining the strengths, weaknesses, opportunities, and threats associated with ChatGPT. This will enable informed decisions about its integration, prioritizing improved learning outcomes. A thorough narrative literature review was undertaken to provide a solid foundation for the SWOT analysis. The materials included scholarly articles and reports, which ensure the study's credibility and allow for a holistic and unbiased assessment. The analysis identified accessibility, consistency, adaptability, cost-effectiveness, and staying up-to-date as crucial factors influencing the strengths, weaknesses, opportunities, and threats associated with ChatGPT integration in nursing education. These themes provided a framework to understand the potential risks and benefits of integrating ChatGPT into nursing education. This review highlights the importance of responsible and effective use of ChatGPT in nursing education and the need for collaboration among educators, policymakers, and AI developers. Addressing the identified challenges and leveraging the strengths of ChatGPT can lead to improved learning outcomes and enriched educational experiences for students. The findings emphasize the importance of responsibly integrating ChatGPT in nursing education, balancing technological advancement with careful consideration of associated risks, to achieve optimal outcomes.
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Affiliation(s)
| | - Alaa Abd-Alrazaq
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, QAT
| | - Ahmad R Al-Qudimat
- Department of Public Health, Qatar University, Doha, QAT
- Surgical Research Section, Department of Surgery, Hamad Medical Corporation, Doha, QAT
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Lautrup AD, Hyrup T, Schneider-Kamp A, Dahl M, Lindholt JS, Schneider-Kamp P. Heart-to-heart with ChatGPT: the impact of patients consulting AI for cardiovascular health advice. Open Heart 2023; 10:e002455. [PMID: 37945282 PMCID: PMC10649823 DOI: 10.1136/openhrt-2023-002455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 10/26/2023] [Indexed: 11/12/2023] Open
Abstract
OBJECTIVES The advent of conversational artificial intelligence (AI) systems employing large language models such as ChatGPT has sparked public, professional and academic debates on the capabilities of such technologies. This mixed-methods study sets out to review and systematically explore the capabilities of ChatGPT to adequately provide health advice to patients when prompted regarding four topics from the field of cardiovascular diseases. METHODS As of 30 May 2023, 528 items on PubMed contained the term ChatGPT in their title and/or abstract, with 258 being classified as journal articles and included in our thematic state-of-the-art review. For the experimental part, we systematically developed and assessed 123 prompts across the four topics based on three classes of users and two languages. Medical and communications experts scored ChatGPT's responses according to the 4Cs of language model evaluation proposed in this article: correct, concise, comprehensive and comprehensible. RESULTS The articles reviewed were fairly evenly distributed across discussing how ChatGPT could be used for medical publishing, in clinical practice and for education of medical personnel and/or patients. Quantitatively and qualitatively assessing the capability of ChatGPT on the 123 prompts demonstrated that, while the responses generally received above-average scores, they occupy a spectrum from the concise and correct via the absurd to what only can be described as hazardously incorrect and incomplete. Prompts formulated at higher levels of health literacy generally yielded higher-quality answers. Counterintuitively, responses in a lower-resource language were often of higher quality. CONCLUSIONS The results emphasise the relationship between prompt and response quality and hint at potentially concerning futures in personalised medicine. The widespread use of large language models for health advice might amplify existing health inequalities and will increase the pressure on healthcare systems by providing easy access to many seemingly likely differential diagnoses and recommendations for seeing a doctor for even harmless ailments.
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Affiliation(s)
- Anton Danholt Lautrup
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Tobias Hyrup
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Anna Schneider-Kamp
- Department of Business and Management, University of Southern Denmark Faculty of Business and Social Sciences, Odense, Denmark
| | - Marie Dahl
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Jes Sanddal Lindholt
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Peter Schneider-Kamp
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
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Abu Hammour K, Alhamad H, Al-Ashwal FY, Halboup A, Abu Farha R, Abu Hammour A. ChatGPT in pharmacy practice: a cross-sectional exploration of Jordanian pharmacists' perception, practice, and concerns. J Pharm Policy Pract 2023; 16:115. [PMID: 37789443 PMCID: PMC10548710 DOI: 10.1186/s40545-023-00624-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 09/22/2023] [Indexed: 10/05/2023] Open
Abstract
OBJECTIVES The purpose of this study is to find out how much pharmacists know and have used ChatGPT in their practice. We investigated the advantages and disadvantages of utilizing ChatGPT in a pharmacy context, the amount of training necessary to use it proficiently, and the influence on patient care using a survey. METHODS This cross-sectional study was carried out between May and June 2023 to assess the potential and problems that pharmacists observed while integrating chatbots powered by AI (ChatGPT) in pharmacy practice. The correlation between perceived benefits and concerns was evaluated using Spearman's rho correlation due to the data's non-normal distribution.Any pharmacists licensed by the Jordanian Pharmacists Association were included in the study. A convenient sampling technique was used to choose the participants, and the study questionnaire was distributed utilizing an online medium (Facebook and WhatsApp). Anyone who expressed interest in taking part was given a link to the study's instructions so they may read them before giving their electronic consent and accessing the survey. RESULTS The potential advantages of ChatGPT in the pharmacy practice were widely acknowledged by the participants. The majority of participants (69.9%) concurred that educational material about pharmacy items or therapeutic areas can be provided using ChatGPT, with 66.9% of respondents believing that ChatGPT is a machine learning algorithm. Concerns about the accuracy of AI-generated responses were also prevalent. More than half of the participants (55.7%) raised the possibility that AI systems such as ChatGPT could pick up on and replicate prejudices and discriminatory patterns from the data they were trained on. Analysis shows a statistically significant positive link, albeit a minor one, between the perceived advantages of ChatGPT and its drawbacks (r = 0.255, p < 0.001). However, concerns were strongly correlated with knowledge of ChatGPT. In contrast to those who were either unsure or had not heard of ChatGPT (64.2%), individuals who had heard of it were more likely to have strong concerns (79.8%) (p = 0.002). Finally, the results show a statistically significant association between the frequency of ChatGPT use and positive perceptions of the tool (p < 0.001). CONCLUSIONS Although ChatGPT has shown promise in health and pharmaceutical practice, its application should be rigorously regulated by evidence-based law. According to the study's findings, pharmacists support the use of ChatGPT in pharmacy practice but have concerns about its use due to ethical reasons, legal problems, privacy concerns, worries about the accuracy of the data generated, data learning, and bias risk.
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Affiliation(s)
- Khawla Abu Hammour
- Department of Clinical Pharmacy and Biopharmaceutics, Faculty of Pharmacy, University of Jordan, Amman, Jordan
| | - Hamza Alhamad
- Department of Clinical Pharmacy, Faculty of Pharmacy, Zarqa University, Zarqa, Jordan
| | - Fahmi Y Al-Ashwal
- Department of Clinical Pharmacy, College of Pharmacy, Al-Ayen University, Thi-Qar, Iraq.
- Department of Clinical Pharmacy and Pharmacy Practice, Faculty of Pharmacy, University of Science and Technology, Sana'a, Yemen.
| | - Abdulsalam Halboup
- Department of Clinical Pharmacy and Pharmacy Practice, Faculty of Pharmacy, University of Science and Technology, Sana'a, Yemen
- Discipline of Clinical Pharmacy, School of Pharmaceutical Sciences, University Sains Malaysia, Gelugor, Pulau Pinang, Malaysia
| | - Rana Abu Farha
- Clinical Pharmacy and Therapeutics Department, Faculty of Pharmacy, Applied Science Private University, P.O. Box 11937, Amman, Jordan
| | - Adnan Abu Hammour
- Medrise Medical Center, Dubai Healthcare City, Dubai, United Arab Emirates
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Nilsson U. Dear ChatGPT, Do We Need Perianesthesia Nurses in the PACU? J Perianesth Nurs 2023; 38:830-831. [PMID: 37777313 DOI: 10.1016/j.jopan.2023.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 07/16/2023] [Indexed: 10/02/2023]
Affiliation(s)
- Ulrica Nilsson
- Division of Nursing, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Perioperative Medicine and Intensive Care, Karolinska University Hospital, Stockholm, Sweden.
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Lareyre F, Nasr B, Chaudhuri A, Di Lorenzo G, Carlier M, Raffort J. Comprehensive Review of Natural Language Processing (NLP) in Vascular Surgery. EJVES Vasc Forum 2023; 60:57-63. [PMID: 37822918 PMCID: PMC10562666 DOI: 10.1016/j.ejvsvf.2023.09.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 07/13/2023] [Accepted: 09/08/2023] [Indexed: 10/13/2023] Open
Abstract
Objective The use of Natural Language Processing (NLP) has attracted increased interest in healthcare with various potential applications including identification and extraction of health information, development of chatbots and virtual assistants. The aim of this comprehensive literature review was to provide an overview of NLP applications in vascular surgery, identify current limitations, and discuss future perspectives in the field. Data sources The MEDLINE database was searched on April 2023. Review methods The database was searched using a combination of keywords to identify studies reporting the use of NLP and chatbots in three main vascular diseases. Keywords used included Natural Language Processing, chatbot, chatGPT, aortic disease, carotid, peripheral artery disease, vascular, and vascular surgery. Results Given the heterogeneity of study design, techniques, and aims, a comprehensive literature review was performed to provide an overview of NLP applications in vascular surgery. By enabling identification and extraction of information on patients with vascular diseases, such technology could help to analyse data from healthcare information systems to provide feedback on current practice and help in optimising patient care. In addition, chatbots and NLP driven techniques have the potential to be used as virtual assistants for both health professionals and patients. Conclusion While Artificial Intelligence and NLP technology could be used to enhance care for patients with vascular diseases, many challenges remain including the need to define guidelines and clear consensus on how to evaluate and validate these innovations before their implementation into clinical practice.
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Affiliation(s)
- Fabien Lareyre
- Department of Vascular Surgery, Hospital of Antibes Juan-les-Pins, France
- Université Côte d'Azur, Inserm, U1065, C3M, Nice, France
| | - Bahaa Nasr
- Department of Vascular and Endovascular Surgery, Brest University Hospital, Brest, France
- INSERM, UMR 1101, LaTIM, Brest, France
| | - Arindam Chaudhuri
- Bedfordshire - Milton Keynes Vascular Centre, Bedfordshire Hospitals, NHS Foundation Trust, Bedford, UK
| | - Gilles Di Lorenzo
- Department of Vascular Surgery, Hospital of Antibes Juan-les-Pins, France
| | - Mathieu Carlier
- Department of Urology, University Hospital of Nice, Nice, France
| | - Juliette Raffort
- Université Côte d'Azur, Inserm, U1065, C3M, Nice, France
- Institute 3IA Côte d’Azur, Université Côte d’Azur, France
- Clinical Chemistry Laboratory, University Hospital of Nice, France
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Zhou Z, Wang X, Li X, Liao L. Is ChatGPT an Evidence-based Doctor? Eur Urol 2023; 84:355-356. [PMID: 37061445 DOI: 10.1016/j.eururo.2023.03.037] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Accepted: 03/31/2023] [Indexed: 04/17/2023]
Affiliation(s)
- Zhonghan Zhou
- Cheeloo College of Medicine, Shandong University, Jinan; Department of Urology, China Rehabilitation Research Center, Beijing, China; University of Health and Rehabilitation Sciences, Qingdao, China; China Rehabilitation Science Institute, Beijing, China; Beijing Key Laboratory of Neural Injury and Rehabilitation, Beijing, China
| | - Xuesheng Wang
- University of Health and Rehabilitation Sciences, Qingdao, China; China Rehabilitation Science Institute, Beijing, China; Beijing Key Laboratory of Neural Injury and Rehabilitation, Beijing, China; School of Rehabilitation, Capital Medical University, Beijing, China
| | - Xunhua Li
- University of Health and Rehabilitation Sciences, Qingdao, China; China Rehabilitation Science Institute, Beijing, China; Beijing Key Laboratory of Neural Injury and Rehabilitation, Beijing, China; School of Rehabilitation, Capital Medical University, Beijing, China
| | - Limin Liao
- Cheeloo College of Medicine, Shandong University, Jinan; Department of Urology, China Rehabilitation Research Center, Beijing, China; University of Health and Rehabilitation Sciences, Qingdao, China; China Rehabilitation Science Institute, Beijing, China; Beijing Key Laboratory of Neural Injury and Rehabilitation, Beijing, China; School of Rehabilitation, Capital Medical University, Beijing, China.
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Mago J, Sharma M. The Potential Usefulness of ChatGPT in Oral and Maxillofacial Radiology. Cureus 2023; 15:e42133. [PMID: 37476297 PMCID: PMC10355343 DOI: 10.7759/cureus.42133] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/19/2023] [Indexed: 07/22/2023] Open
Abstract
Aim This study aimed to evaluate the potential usefulness of Chat Generated Pre-Trained Transformer-3 (ChatGPT-3) in oral and maxillofacial radiology for report writing by identifying radiographic anatomical landmarks and learning about oral and maxillofacial pathologies and their radiographic features. The study also aimed to evaluate the performance of ChatGPT-3 and its usage in oral and maxillofacial radiology training. Materials and methods A questionnaire consisting of 80 questions was queried on the OpenAI app ChatGPT-3. The questions were stratified based on three categories. The categorization was based on random anatomical landmarks, oral and maxillofacial pathologies, and the radiographic features of some of these pathologies. One oral and maxillofacial radiologist evaluated queries that were answered by the ChatGPT-3 model and rated them on a 4-point, modified Likert scale. The post-survey analysis for the performance of ChatGPT-3 was based on the Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis, its application in oral and maxillofacial radiology training, and its recommended use. Results In order of efficiency, Chat GPT-3 gave 100% accuracy in describing radiographic landmarks. However, the content of the oral and maxillofacial pathologies was limited to major or characteristic radiographic features. The mean scores for the queries related to the anatomic landmarks, oral and maxillofacial pathologies, and radiographic features of the oral and maxillofacial pathologies were 3.94, 3.85, and 3.96, respectively. However, the median and mode scores were 4 and were similar to all categories. The data for the oral and maxillofacial pathologies when the questions were not specifically included in the format of the introduction of the pathology, causes, symptoms, and treatment. Out of two abbreviations, one was not answered correctly. Conclusion The study showed that ChatGPT-3 is efficient in describing the pathology, characteristic radiographic features, and describing anatomical landmarks. ChatGPT-3 can be used as an adjunct when an oral radiologist needs additional information on any pathology, however, it cannot be the mainstay for reference. ChatGPT-3 is less detail-oriented, and the data has a risk of infodemics and the possibility of medical errors. However, Chat GPT-3 can be an excellent tool in helping the community in increasing the knowledge and awareness of various pathologies and decreasing the anxiety of the patients while dental healthcare professionals formulate an appropriate treatment plan.
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Affiliation(s)
- Jyoti Mago
- Oral and Maxillofacial Radiology, University of Nevada, Las Vegas (UNLV), Las Vegas, USA
| | - Manoj Sharma
- Public Health, University of Nevada, Las Vegas (UNLV), Las Vegas, USA
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Sallam M. ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns. Healthcare (Basel) 2023; 11:887. [PMID: 36981544 PMCID: PMC10048148 DOI: 10.3390/healthcare11060887] [Citation(s) in RCA: 444] [Impact Index Per Article: 444.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 03/17/2023] [Accepted: 03/17/2023] [Indexed: 03/22/2023] Open
Abstract
ChatGPT is an artificial intelligence (AI)-based conversational large language model (LLM). The potential applications of LLMs in health care education, research, and practice could be promising if the associated valid concerns are proactively examined and addressed. The current systematic review aimed to investigate the utility of ChatGPT in health care education, research, and practice and to highlight its potential limitations. Using the PRIMSA guidelines, a systematic search was conducted to retrieve English records in PubMed/MEDLINE and Google Scholar (published research or preprints) that examined ChatGPT in the context of health care education, research, or practice. A total of 60 records were eligible for inclusion. Benefits of ChatGPT were cited in 51/60 (85.0%) records and included: (1) improved scientific writing and enhancing research equity and versatility; (2) utility in health care research (efficient analysis of datasets, code generation, literature reviews, saving time to focus on experimental design, and drug discovery and development); (3) benefits in health care practice (streamlining the workflow, cost saving, documentation, personalized medicine, and improved health literacy); and (4) benefits in health care education including improved personalized learning and the focus on critical thinking and problem-based learning. Concerns regarding ChatGPT use were stated in 58/60 (96.7%) records including ethical, copyright, transparency, and legal issues, the risk of bias, plagiarism, lack of originality, inaccurate content with risk of hallucination, limited knowledge, incorrect citations, cybersecurity issues, and risk of infodemics. The promising applications of ChatGPT can induce paradigm shifts in health care education, research, and practice. However, the embrace of this AI chatbot should be conducted with extreme caution considering its potential limitations. As it currently stands, ChatGPT does not qualify to be listed as an author in scientific articles unless the ICMJE/COPE guidelines are revised or amended. An initiative involving all stakeholders in health care education, research, and practice is urgently needed. This will help to set a code of ethics to guide the responsible use of ChatGPT among other LLMs in health care and academia.
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Affiliation(s)
- Malik Sallam
- Department of Pathology, Microbiology and Forensic Medicine, School of Medicine, The University of Jordan, Amman 11942, Jordan
- Department of Clinical Laboratories and Forensic Medicine, Jordan University Hospital, Amman 11942, Jordan
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