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Cheng S, Xiao Y, Liu L, Sun X. Comparative outcomes of AI-assisted ChatGPT and face-to-face consultations in infertility patients: a cross-sectional study. Postgrad Med J 2024:qgae083. [PMID: 38970829 DOI: 10.1093/postmj/qgae083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2024] [Revised: 05/16/2024] [Accepted: 06/21/2024] [Indexed: 07/08/2024]
Abstract
BACKGROUND With the advent of artificial intelligence (AI) in healthcare, digital platforms like ChatGPT offer innovative alternatives to traditional medical consultations. This study seeks to understand the comparative outcomes of AI-assisted ChatGPT consultations and conventional face-to-face interactions among infertility patients. METHODS A cross-sectional study was conducted involving 120 infertility patients, split evenly between those consulting via ChatGPT and traditional face-to-face methods. The primary outcomes assessed were patient satisfaction, understanding, and consultation duration. Secondary outcomes included demographic information, clinical history, and subsequent actions post-consultation. RESULTS While both consultation methods had a median age of 34 years, patients using ChatGPT reported significantly higher satisfaction levels (median 4 out of 5) compared to face-to-face consultations (median 3 out of 5; p < 0.001). The ChatGPT group also experienced shorter consultation durations, with a median difference of 12.5 minutes (p < 0.001). However, understanding, demographic distributions, and subsequent actions post-consultation were comparable between the two groups. CONCLUSIONS AI-assisted ChatGPT consultations offer a promising alternative to traditional face-to-face consultations in assisted reproductive medicine. While patient satisfaction was higher and consultation durations were shorter with ChatGPT, further studies are required to understand the long-term implications and clinical outcomes associated with AI-driven medical consultations. Key Messages What is already known on this topic: Artificial intelligence (AI) applications, such as ChatGPT, have shown potential in various healthcare settings, including primary care and mental health support. Infertility is a significant global health issue that requires extensive consultations, often facing challenges such as long waiting times and varied patient satisfaction. Previous studies suggest that AI can offer personalized care and immediate feedback, but its efficacy compared with traditional consultations in reproductive medicine was not well-studied. What this study adds: This study demonstrates that AI-assisted ChatGPT consultations result in significantly higher patient satisfaction and shorter consultation durations compared with traditional face-to-face consultations among infertility patients. Both consultation methods were comparable in terms of patient understanding, demographic distributions, and subsequent actions postconsultation. How this study might affect research, practice, or policy: The findings suggest that AI-driven consultations could serve as an effective and efficient alternative to traditional methods, potentially reducing consultation times and improving patient satisfaction in reproductive medicine. Further research could explore the long-term impacts and broader applications of AI in clinical settings, influencing future healthcare practices and policies toward integrating AI technologies.
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Affiliation(s)
- Shaolong Cheng
- Department of Reproductive Medicine Center, The Affiliated Hospital, Southwest Medical University, 25 Taiping Street, Luzhou, 646000, China
| | - Yuping Xiao
- Department of Reproductive Medicine Center, The Affiliated Hospital, Southwest Medical University, 25 Taiping Street, Luzhou, 646000, China
| | - Ling Liu
- Department of Reproductive Medicine Center, The Affiliated Hospital, Southwest Medical University, 25 Taiping Street, Luzhou, 646000, China
| | - Xingyu Sun
- Department of Gynecology, The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, Sichuan 646000, China
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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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Karobari MI, Suryawanshi H, Patil SR. Revolutionizing oral and maxillofacial surgery: ChatGPT's impact on decision support, patient communication, and continuing education. Int J Surg 2024; 110:3143-3145. [PMID: 38446838 PMCID: PMC11175733 DOI: 10.1097/js9.0000000000001286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 02/22/2024] [Indexed: 03/08/2024]
Affiliation(s)
- Mohmed Isaqali Karobari
- Department of Restorative Dentistry and Endodontics, Faculty of Dentistry, University of Puthisastra, Phnom Penh, Cambodia
- Dental Research Unit, Center for Global Health Research, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu
| | - Hema Suryawanshi
- Department of Oral Pathology and Microbiology, Chhattisgarh Dental College and Research Institute
| | - Santosh R. Patil
- Department of Oral Medicine and Radiology, Chhattisgarh Dental College and Research Institute, India
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Wu J, Ma Y, Wang J, Xiao M. The Application of ChatGPT in Medicine: A Scoping Review and Bibliometric Analysis. J Multidiscip Healthc 2024; 17:1681-1692. [PMID: 38650670 PMCID: PMC11034560 DOI: 10.2147/jmdh.s463128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Accepted: 03/25/2024] [Indexed: 04/25/2024] Open
Abstract
Purpose ChatGPT has a wide range of applications in the medical field. Therefore, this review aims to define the key issues and provide a comprehensive view of the literature based on the application of ChatGPT in medicine. Methods This scope follows Arksey and O'Malley's five-stage framework. A comprehensive literature search of publications (30 November 2022 to 16 August 2023) was conducted. Six databases were searched and relevant references were systematically catalogued. Attention was focused on the general characteristics of the articles, their fields of application, and the advantages and disadvantages of using ChatGPT. Descriptive statistics and narrative synthesis methods were used for data analysis. Results Of the 3426 studies, 247 met the criteria for inclusion in this review. The majority of articles (31.17%) were from the United States. Editorials (43.32%) ranked first, followed by experimental studys (11.74%). The potential applications of ChatGPT in medicine are varied, with the largest number of studies (45.75%) exploring clinical practice, including assisting with clinical decision support and providing disease information and medical advice. This was followed by medical education (27.13%) and scientific research (16.19%). Particularly noteworthy in the discipline statistics were radiology, surgery and dentistry at the top of the list. However, ChatGPT in medicine also faces issues of data privacy, inaccuracy and plagiarism. Conclusion The application of ChatGPT in medicine focuses on different disciplines and general application scenarios. ChatGPT has a paradoxical nature: it offers significant advantages, but at the same time raises great concerns about its application in healthcare settings. Therefore, it is imperative to develop theoretical frameworks that not only address its widespread use in healthcare but also facilitate a comprehensive assessment. In addition, these frameworks should contribute to the development of strict and effective guidelines and regulatory measures.
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Affiliation(s)
- Jie Wu
- Department of Nursing, the First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Yingzhuo Ma
- Department of Nursing, the First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Jun Wang
- Department of Nursing, the First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Mingzhao Xiao
- Department of Urology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
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Tasijawa FA, Herwawan JH. Assessing the Effectiveness of ChatGPT in Delivering Mental Health Support: A Qualitative Study [Letter]. J Multidiscip Healthc 2024; 17:765-766. [PMID: 38404716 PMCID: PMC10893874 DOI: 10.2147/jmdh.s464332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 02/19/2024] [Indexed: 02/27/2024] Open
Affiliation(s)
- Fandro Armando Tasijawa
- Faculty of Health, Universitas Kristen Indonesia Maluku, Ambon City, Maluku Province, Indonesia
| | - Joan Herly Herwawan
- Faculty of Health, Universitas Kristen Indonesia Maluku, Ambon City, Maluku Province, Indonesia
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Alotaibi SS, Rehman A, Hasnain M. Revolutionizing ocular cancer management: a narrative review on exploring the potential role of ChatGPT. Front Public Health 2023; 11:1338215. [PMID: 38192545 PMCID: PMC10773849 DOI: 10.3389/fpubh.2023.1338215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 12/04/2023] [Indexed: 01/10/2024] Open
Abstract
This paper pioneers the exploration of ocular cancer, and its management with the help of Artificial Intelligence (AI) technology. Existing literature presents a significant increase in new eye cancer cases in 2023, experiencing a higher incidence rate. Extensive research was conducted using online databases such as PubMed, ACM Digital Library, ScienceDirect, and Springer. To conduct this review, Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines are used. Of the collected 62 studies, only 20 documents met the inclusion criteria. The review study identifies seven ocular cancer types. Important challenges associated with ocular cancer are highlighted, including limited awareness about eye cancer, restricted healthcare access, financial barriers, and insufficient infrastructure support. Financial barriers is one of the widely examined ocular cancer challenges in the literature. The potential role and limitations of ChatGPT are discussed, emphasizing its usefulness in providing general information to physicians, noting its inability to deliver up-to-date information. The paper concludes by presenting the potential future applications of ChatGPT to advance research on ocular cancer globally.
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Affiliation(s)
- Saud S. Alotaibi
- Information Systems Department, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Amna Rehman
- Department of Computer Science, Lahore Leads University, Lahore, Pakistan
| | - Muhammad Hasnain
- Department of Computer Science, Lahore Leads University, Lahore, Pakistan
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