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Moulaei K, Yadegari A, Baharestani M, Farzanbakhsh S, Sabet B, Reza Afrash M. Generative artificial intelligence in healthcare: A scoping review on benefits, challenges and applications. Int J Med Inform 2024; 188:105474. [PMID: 38733640 DOI: 10.1016/j.ijmedinf.2024.105474] [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: 04/06/2024] [Revised: 05/03/2024] [Accepted: 05/04/2024] [Indexed: 05/13/2024]
Abstract
BACKGROUND Generative artificial intelligence (GAI) is revolutionizing healthcare with solutions for complex challenges, enhancing diagnosis, treatment, and care through new data and insights. However, its integration raises questions about applications, benefits, and challenges. Our study explores these aspects, offering an overview of GAI's applications and future prospects in healthcare. METHODS This scoping review searched Web of Science, PubMed, and Scopus . The selection of studies involved screening titles, reviewing abstracts, and examining full texts, adhering to the PRISMA-ScR guidelines throughout the process. RESULTS From 1406 articles across three databases, 109 met inclusion criteria after screening and deduplication. Nine GAI models were utilized in healthcare, with ChatGPT (n = 102, 74 %), Google Bard (Gemini) (n = 16, 11 %), and Microsoft Bing AI (n = 10, 7 %) being the most frequently employed. A total of 24 different applications of GAI in healthcare were identified, with the most common being "offering insights and information on health conditions through answering questions" (n = 41) and "diagnosis and prediction of diseases" (n = 17). In total, 606 benefits and challenges were identified, which were condensed to 48 benefits and 61 challenges after consolidation. The predominant benefits included "Providing rapid access to information and valuable insights" and "Improving prediction and diagnosis accuracy", while the primary challenges comprised "generating inaccurate or fictional content", "unknown source of information and fake references for texts", and "lower accuracy in answering questions". CONCLUSION This scoping review identified the applications, benefits, and challenges of GAI in healthcare. This synthesis offers a crucial overview of GAI's potential to revolutionize healthcare, emphasizing the imperative to address its limitations.
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Affiliation(s)
- Khadijeh Moulaei
- Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
| | - Atiye Yadegari
- Department of Pediatric Dentistry, School of Dentistry, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Mahdi Baharestani
- Network of Interdisciplinarity in Neonates and Infants (NINI), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Shayan Farzanbakhsh
- Network of Interdisciplinarity in Neonates and Infants (NINI), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Babak Sabet
- Department of Surgery, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Afrash
- Department of Artificial Intelligence, Smart University of Medical Sciences, Tehran, Iran.
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Li J, Zong H, Wu E, Wu R, Peng Z, Zhao J, Yang L, Xie H, Shen B. Exploring the potential of artificial intelligence to enhance the writing of english academic papers by non-native english-speaking medical students - the educational application of ChatGPT. BMC MEDICAL EDUCATION 2024; 24:736. [PMID: 38982429 PMCID: PMC11232216 DOI: 10.1186/s12909-024-05738-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Accepted: 07/02/2024] [Indexed: 07/11/2024]
Abstract
BACKGROUND Academic paper writing holds significant importance in the education of medical students, and poses a clear challenge for those whose first language is not English. This study aims to investigate the effectiveness of employing large language models, particularly ChatGPT, in improving the English academic writing skills of these students. METHODS A cohort of 25 third-year medical students from China was recruited. The study consisted of two stages. Firstly, the students were asked to write a mini paper. Secondly, the students were asked to revise the mini paper using ChatGPT within two weeks. The evaluation of the mini papers focused on three key dimensions, including structure, logic, and language. The evaluation method incorporated both manual scoring and AI scoring utilizing the ChatGPT-3.5 and ChatGPT-4 models. Additionally, we employed a questionnaire to gather feedback on students' experience in using ChatGPT. RESULTS After implementing ChatGPT for writing assistance, there was a notable increase in manual scoring by 4.23 points. Similarly, AI scoring based on the ChatGPT-3.5 model showed an increase of 4.82 points, while the ChatGPT-4 model showed an increase of 3.84 points. These results highlight the potential of large language models in supporting academic writing. Statistical analysis revealed no significant difference between manual scoring and ChatGPT-4 scoring, indicating the potential of ChatGPT-4 to assist teachers in the grading process. Feedback from the questionnaire indicated a generally positive response from students, with 92% acknowledging an improvement in the quality of their writing, 84% noting advancements in their language skills, and 76% recognizing the contribution of ChatGPT in supporting academic research. CONCLUSION The study highlighted the efficacy of large language models like ChatGPT in augmenting the English academic writing proficiency of non-native speakers in medical education. Furthermore, it illustrated the potential of these models to make a contribution to the educational evaluation process, particularly in environments where English is not the primary language.
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Affiliation(s)
- Jiakun Li
- Department of Urology and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Hui Zong
- Department of Urology and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Erman Wu
- Department of Urology and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China
- Department of Neurosurgery, the First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, China
| | - Rongrong Wu
- Department of Urology and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Zhufeng Peng
- Department of Urology and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Jing Zhao
- Department of Urology and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Lu Yang
- Department of Urology and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Hong Xie
- West China Hospital, West China School of Medicine, Sichuan University, No. 37, Guoxue Alley, Chengdu, 610041, China.
| | - Bairong Shen
- Department of Urology and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China.
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China.
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Flitcroft MA, Sheriff SA, Wolfrath N, Maddula R, McConnell L, Xing Y, Haines KL, Wong SL, Kothari AN. Performance of Artificial Intelligence Content Detectors Using Human and Artificial Intelligence-Generated Scientific Writing. Ann Surg Oncol 2024:10.1245/s10434-024-15549-6. [PMID: 38909113 DOI: 10.1245/s10434-024-15549-6] [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: 01/09/2024] [Accepted: 05/16/2024] [Indexed: 06/24/2024]
Abstract
BACKGROUND Few studies have examined the performance of artificial intelligence (AI) content detection in scientific writing. This study evaluates the performance of publicly available AI content detectors when applied to both human-written and AI-generated scientific articles. METHODS Articles published in Annals of Surgical Oncology (ASO) during the year 2022, as well as AI-generated articles using OpenAI's ChatGPT, were analyzed by three AI content detectors to assess the probability of AI-generated content. Full manuscripts and their individual sections were evaluated. Group comparisons and trend analyses were conducted by using ANOVA and linear regression. Classification performance was determined using area under the curve (AUC). RESULTS A total of 449 original articles met inclusion criteria and were evaluated to determine the likelihood of being generated by AI. Each detector also evaluated 47 AI-generated articles by using titles from ASO articles. Human-written articles had an average probability of being AI-generated of 9.4% with significant differences between the detectors. Only two (0.4%) human-written manuscripts were detected as having a 0% probability of being AI-generated by all three detectors. Completely AI-generated articles were evaluated to have a higher average probability of being AI-generated (43.5%) with a range from 12.0 to 99.9%. CONCLUSIONS This study demonstrates differences in the performance of various AI content detectors with the potential to label human-written articles as AI-generated. Any effort toward implementing AI detectors must include a strategy for continuous evaluation and validation as AI models and detectors rapidly evolve.
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Affiliation(s)
- Madelyn A Flitcroft
- Department of Surgery, Division of Surgical Oncology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Salma A Sheriff
- Department of Surgery, Division of Surgical Oncology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Nathan Wolfrath
- Department of Surgery, Division of Surgical Oncology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Ragasnehith Maddula
- Department of Surgery, Division of Surgical Oncology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Laura McConnell
- Department of Surgery, Division of Surgical Oncology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Yun Xing
- Department of Surgery, Division of Surgical Oncology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Krista L Haines
- Department of Surgery, Division of Trauma, Critical Care, and Acute Care Surgery, Duke University, Durham, NC, USA
| | - Sandra L Wong
- Department of Surgery, Dartmouth Hitchcock Medical Center, Lebanon, NH, USA
| | - Anai N Kothari
- Department of Surgery, Division of Surgical Oncology, Medical College of Wisconsin, Milwaukee, WI, USA.
- Clinical and Translational Science Institute of SE Wisconsin, Medical College of Wisconsin, Milwaukee, WI, USA.
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Masters K, Benjamin J, Agrawal A, MacNeill H, Pillow MT, Mehta N. Twelve tips on creating and using custom GPTs to enhance health professions education. MEDICAL TEACHER 2024; 46:752-756. [PMID: 38285894 DOI: 10.1080/0142159x.2024.2305365] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 01/10/2024] [Indexed: 01/31/2024]
Abstract
The custom GPT is the latest powerful feature added to ChatGPT. Non-programmers can create and share their own GPTs ("chat bots"), allowing Health Professions Educators to apply the capabilities of ChatGPT to create administrative assistants, online tutors, virtual patients, and more, to support their clinical and non-clinical teaching environments. To achieve this correctly, however, requires some skills, and this 12-Tips paper provides those: we explain how to construct data sources, build relevant GPTs, and apply some basic security.
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Affiliation(s)
- Ken Masters
- Department of FDE Medical Education and Informatics, Sultan Qaboos University, Muscat, Oman
| | - Jennifer Benjamin
- Texas Childrens Hospital, Baylor College of Medicine, Houston, TX, USA
| | - Anoop Agrawal
- Emergency Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Heather MacNeill
- Department of Medicine, University of Toronto Temerty of Medicine, Toronto, CA, USA
| | - M Tyson Pillow
- Department of Education, Innovation & Technology, Baylor College of Medicine, Houston, TX, USA
| | - Neil Mehta
- Department of Internal Medicine and Geriatrics, Cleveland Clinic, Cleveland, OH, USA
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Naqvi WM, Shaikh SZ, Mishra GV. Large language models in physical therapy: time to adapt and adept. Front Public Health 2024; 12:1364660. [PMID: 38887241 PMCID: PMC11182445 DOI: 10.3389/fpubh.2024.1364660] [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: 01/02/2024] [Accepted: 05/10/2024] [Indexed: 06/20/2024] Open
Abstract
Healthcare is experiencing a transformative phase, with artificial intelligence (AI) and machine learning (ML). Physical therapists (PTs) stand on the brink of a paradigm shift in education, practice, and research. Rather than visualizing AI as a threat, it presents an opportunity to revolutionize. This paper examines how large language models (LLMs), such as ChatGPT and BioMedLM, driven by deep ML can offer human-like performance but face challenges in accuracy due to vast data in PT and rehabilitation practice. PTs can benefit by developing and training an LLM specifically for streamlining administrative tasks, connecting globally, and customizing treatments using LLMs. However, human touch and creativity remain invaluable. This paper urges PTs to engage in learning and shaping AI models by highlighting the need for ethical use and human supervision to address potential biases. Embracing AI as a contributor, and not just a user, is crucial by integrating AI, fostering collaboration for a future in which AI enriches the PT field provided data accuracy, and the challenges associated with feeding the AI model are sensitively addressed.
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Affiliation(s)
- Waqar M. Naqvi
- Department of Interdisciplinary Sciences, Datta Meghe Institute of Higher Education and Research, Wardha, India
- Department of Physiotherapy, College of Health Sciences, Gulf Medical University, Ajman, United Arab Emirates
- NKP Salve Institute of Medical Sciences and Research Center, Nagpur, India
| | - Summaiya Zareen Shaikh
- Department of Neuro-Physiotherapy, The SIA College of Health Sciences, College of Physiotherapy, Thane, India
| | - Gaurav V. Mishra
- Department of Radiodiagnosis, Datta Meghe Institute of Higher Education and Research, Wardha, India
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Temsah MH, Alhuzaimi AN, Almansour M, Aljamaan F, Alhasan K, Batarfi MA, Altamimi I, Alharbi A, Alsuhaibani AA, Alwakeel L, Alzahrani AA, Alsulaim KB, Jamal A, Khayat A, Alghamdi MH, Halwani R, Khan MK, Al-Eyadhy A, Nazer R. Art or Artifact: Evaluating the Accuracy, Appeal, and Educational Value of AI-Generated Imagery in DALL·E 3 for Illustrating Congenital Heart Diseases. J Med Syst 2024; 48:54. [PMID: 38780839 DOI: 10.1007/s10916-024-02072-0] [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: 01/24/2024] [Accepted: 04/30/2024] [Indexed: 05/25/2024]
Abstract
Artificial Intelligence (AI), particularly AI-Generated Imagery, has the potential to impact medical and patient education. This research explores the use of AI-generated imagery, from text-to-images, in medical education, focusing on congenital heart diseases (CHD). Utilizing ChatGPT's DALL·E 3, the research aims to assess the accuracy and educational value of AI-created images for 20 common CHDs. In this study, we utilized DALL·E 3 to generate a comprehensive set of 110 images, comprising ten images depicting the normal human heart and five images for each of the 20 common CHDs. The generated images were evaluated by a diverse group of 33 healthcare professionals. This cohort included cardiology experts, pediatricians, non-pediatric faculty members, trainees (medical students, interns, pediatric residents), and pediatric nurses. Utilizing a structured framework, these professionals assessed each image for anatomical accuracy, the usefulness of in-picture text, its appeal to medical professionals, and the image's potential applicability in medical presentations. Each item was assessed on a Likert scale of three. The assessments produced a total of 3630 images' assessments. Most AI-generated cardiac images were rated poorly as follows: 80.8% of images were rated as anatomically incorrect or fabricated, 85.2% rated to have incorrect text labels, 78.1% rated as not usable for medical education. The nurses and medical interns were found to have a more positive perception about the AI-generated cardiac images compared to the faculty members, pediatricians, and cardiology experts. Complex congenital anomalies were found to be significantly more predicted to anatomical fabrication compared to simple cardiac anomalies. There were significant challenges identified in image generation. Based on our findings, we recommend a vigilant approach towards the use of AI-generated imagery in medical education at present, underscoring the imperative for thorough validation and the importance of collaboration across disciplines. While we advise against its immediate integration until further validations are conducted, the study advocates for future AI-models to be fine-tuned with accurate medical data, enhancing their reliability and educational utility.
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Affiliation(s)
- Mohamad-Hani Temsah
- College of Medicine, King Saud University, Riyadh, Saudi Arabia.
- Pediatric Department, King Saud University Medical City, King Saud University, Riyadh, Saudi Arabia.
- Evidence-Based Health Care & Knowledge Translation Research Chair, Family & Community Medicine Department, College of Medicine, King Saud University, 11362, Riyadh, Saudi Arabia.
| | - Abdullah N Alhuzaimi
- College of Medicine, King Saud University, Riyadh, Saudi Arabia
- Division of Pediatric Cardiology, Cardiac Science Department, College of Medicine, King Saud University Medical City, 11362, Riyadh, Saudi Arabia
| | - Mohammed Almansour
- College of Medicine, King Saud University, Riyadh, Saudi Arabia
- Department of Medical Education, College of Medicine, King Saud University, Riyadh, Saudi Arabia
| | - Fadi Aljamaan
- College of Medicine, King Saud University, Riyadh, Saudi Arabia
- Critical Care Department, King Saud University Medical City, Riyadh, Saudi Arabia
| | - Khalid Alhasan
- College of Medicine, King Saud University, Riyadh, Saudi Arabia
- Pediatric Department, King Saud University Medical City, King Saud University, Riyadh, Saudi Arabia
- Kidney & Pancreas Health Center, Organ Transplant Center of Excellence, King Faisal Specialist Hospital & Research Center, Riyadh, Saudi Arabia
| | - Munirah A Batarfi
- Basic Medical Sciences, College of Medicine King Saud bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
| | | | - Amani Alharbi
- Pediatric Department, King Saud University Medical City, King Saud University, Riyadh, Saudi Arabia
| | | | - Leena Alwakeel
- Pediatric Department, King Saud University Medical City, King Saud University, Riyadh, Saudi Arabia
| | | | | | - Amr Jamal
- College of Medicine, King Saud University, Riyadh, Saudi Arabia
- Evidence-Based Health Care & Knowledge Translation Research Chair, Family & Community Medicine Department, College of Medicine, King Saud University, 11362, Riyadh, Saudi Arabia
- Department of Family and Community Medicine, King Saud University Medical City, 11362, Riyadh, Saudi Arabia
| | - Afnan Khayat
- Health Information Management Department, Prince Sultan Military College of Health Sciences, Al Dhahran, Saudi Arabia
| | - Mohammed Hussien Alghamdi
- College of Medicine, King Saud University, Riyadh, Saudi Arabia
- Division of Pediatric Cardiology, Cardiac Science Department, College of Medicine, King Saud University Medical City, 11362, Riyadh, Saudi Arabia
- Department of Medical Education, College of Medicine, King Saud University, Riyadh, Saudi Arabia
| | - Rabih Halwani
- Department of Clinical Sciences, College of Medicine, University of Sharjah, 27272, Sharjah, United Arab Emirates
- Research Institute for Medical and Health Sciences, University of Sharjah, 27272, Sharjah, United Arab Emirates
| | - Muhammad Khurram Khan
- Center of Excellence in Information Assurance, King Saud University, 11653, Riyadh, Saudi Arabia
| | - Ayman Al-Eyadhy
- College of Medicine, King Saud University, Riyadh, Saudi Arabia
- Pediatric Department, King Saud University Medical City, King Saud University, Riyadh, Saudi Arabia
| | - Rakan Nazer
- College of Medicine, King Saud University, Riyadh, Saudi Arabia
- Department of Cardiac Science, King Fahad Cardiac Center, College of Medicine, King Saud University, Riyadh, Saudi Arabia
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Zhang J, Yang P, Zeng L, Li S, Zhou J. Ventilator-Associated Pneumonia Prediction Models Based on AI: Scoping Review. JMIR Med Inform 2024; 12:e57026. [PMID: 38771220 PMCID: PMC11107770 DOI: 10.2196/57026] [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: 02/02/2024] [Revised: 04/08/2024] [Accepted: 04/11/2024] [Indexed: 05/22/2024] Open
Abstract
Background Ventilator-associated pneumonia (VAP) is a serious complication of mechanical ventilation therapy that affects patients' treatments and prognoses. Owing to its excellent data mining capabilities, artificial intelligence (AI) has been increasingly used to predict VAP. Objective This paper reviews VAP prediction models that are based on AI, providing a reference for the early identification of high-risk groups in future clinical practice. Methods A scoping review was conducted in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The Wanfang database, the Chinese Biomedical Literature Database, Cochrane Library, Web of Science, PubMed, MEDLINE, and Embase were searched to identify relevant articles. Study selection and data extraction were independently conducted by 2 reviewers. The data extracted from the included studies were synthesized narratively. Results Of the 137 publications retrieved, 11 were included in this scoping review. The included studies reported the use of AI for predicting VAP. All 11 studies predicted VAP occurrence, and studies on VAP prognosis were excluded. Further, these studies used text data, and none of them involved imaging data. Public databases were the primary sources of data for model building (studies: 6/11, 55%), and 5 studies had sample sizes of <1000. Machine learning was the primary algorithm for studying the VAP prediction models. However, deep learning and large language models were not used to construct VAP prediction models. The random forest model was the most commonly used model (studies: 5/11, 45%). All studies only performed internal validations, and none of them addressed how to implement and apply the final model in real-life clinical settings. Conclusions This review presents an overview of studies that used AI to predict and diagnose VAP. AI models have better predictive performance than traditional methods and are expected to provide indispensable tools for VAP risk prediction in the future. However, the current research is in the model construction and validation stage, and the implementation of and guidance for clinical VAP prediction require further research.
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Affiliation(s)
- Jinbo Zhang
- Nursing Department, Affiliated Hospital of Zunyi Medical University, Zunyi, China
- Nursing College, Zunyi Medical University, Zunyi, China
| | - Pingping Yang
- Nursing Department, Affiliated Hospital of Zunyi Medical University, Zunyi, China
- Nursing College, Zunyi Medical University, Zunyi, China
| | - Lu Zeng
- Nursing Department, Affiliated Hospital of Zunyi Medical University, Zunyi, China
- Nursing College, Zunyi Medical University, Zunyi, China
| | - Shan Li
- Nursing Department, Affiliated Hospital of Zunyi Medical University, Zunyi, China
- Nursing College, Zunyi Medical University, Zunyi, China
| | - Jiamei Zhou
- Nursing Department, Affiliated Hospital of Zunyi Medical University, Zunyi, China
- Nursing College, Zunyi Medical University, Zunyi, China
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Preiksaitis C, Ashenburg N, Bunney G, Chu A, Kabeer R, Riley F, Ribeira R, Rose C. The Role of Large Language Models in Transforming Emergency Medicine: Scoping Review. JMIR Med Inform 2024; 12:e53787. [PMID: 38728687 PMCID: PMC11127144 DOI: 10.2196/53787] [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: 10/19/2023] [Revised: 12/20/2023] [Accepted: 04/05/2024] [Indexed: 05/12/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI), more specifically large language models (LLMs), holds significant potential in revolutionizing emergency care delivery by optimizing clinical workflows and enhancing the quality of decision-making. Although enthusiasm for integrating LLMs into emergency medicine (EM) is growing, the existing literature is characterized by a disparate collection of individual studies, conceptual analyses, and preliminary implementations. Given these complexities and gaps in understanding, a cohesive framework is needed to comprehend the existing body of knowledge on the application of LLMs in EM. OBJECTIVE Given the absence of a comprehensive framework for exploring the roles of LLMs in EM, this scoping review aims to systematically map the existing literature on LLMs' potential applications within EM and identify directions for future research. Addressing this gap will allow for informed advancements in the field. METHODS Using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) criteria, we searched Ovid MEDLINE, Embase, Web of Science, and Google Scholar for papers published between January 2018 and August 2023 that discussed LLMs' use in EM. We excluded other forms of AI. A total of 1994 unique titles and abstracts were screened, and each full-text paper was independently reviewed by 2 authors. Data were abstracted independently, and 5 authors performed a collaborative quantitative and qualitative synthesis of the data. RESULTS A total of 43 papers were included. Studies were predominantly from 2022 to 2023 and conducted in the United States and China. We uncovered four major themes: (1) clinical decision-making and support was highlighted as a pivotal area, with LLMs playing a substantial role in enhancing patient care, notably through their application in real-time triage, allowing early recognition of patient urgency; (2) efficiency, workflow, and information management demonstrated the capacity of LLMs to significantly boost operational efficiency, particularly through the automation of patient record synthesis, which could reduce administrative burden and enhance patient-centric care; (3) risks, ethics, and transparency were identified as areas of concern, especially regarding the reliability of LLMs' outputs, and specific studies highlighted the challenges of ensuring unbiased decision-making amidst potentially flawed training data sets, stressing the importance of thorough validation and ethical oversight; and (4) education and communication possibilities included LLMs' capacity to enrich medical training, such as through using simulated patient interactions that enhance communication skills. CONCLUSIONS LLMs have the potential to fundamentally transform EM, enhancing clinical decision-making, optimizing workflows, and improving patient outcomes. This review sets the stage for future advancements by identifying key research areas: prospective validation of LLM applications, establishing standards for responsible use, understanding provider and patient perceptions, and improving physicians' AI literacy. Effective integration of LLMs into EM will require collaborative efforts and thorough evaluation to ensure these technologies can be safely and effectively applied.
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Affiliation(s)
- Carl Preiksaitis
- Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Nicholas Ashenburg
- Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Gabrielle Bunney
- Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Andrew Chu
- Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Rana Kabeer
- Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Fran Riley
- Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Ryan Ribeira
- Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Christian Rose
- Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, CA, United States
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Skryd A, Lawrence K. ChatGPT as a Tool for Medical Education and Clinical Decision-Making on the Wards: Case Study. JMIR Form Res 2024; 8:e51346. [PMID: 38717811 PMCID: PMC11112466 DOI: 10.2196/51346] [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: 07/28/2023] [Revised: 11/30/2023] [Accepted: 03/21/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND Large language models (LLMs) are computational artificial intelligence systems with advanced natural language processing capabilities that have recently been popularized among health care students and educators due to their ability to provide real-time access to a vast amount of medical knowledge. The adoption of LLM technology into medical education and training has varied, and little empirical evidence exists to support its use in clinical teaching environments. OBJECTIVE The aim of the study is to identify and qualitatively evaluate potential use cases and limitations of LLM technology for real-time ward-based educational contexts. METHODS A brief, single-site exploratory evaluation of the publicly available ChatGPT-3.5 (OpenAI) was conducted by implementing the tool into the daily attending rounds of a general internal medicine inpatient service at a large urban academic medical center. ChatGPT was integrated into rounds via both structured and organic use, using the web-based "chatbot" style interface to interact with the LLM through conversational free-text and discrete queries. A qualitative approach using phenomenological inquiry was used to identify key insights related to the use of ChatGPT through analysis of ChatGPT conversation logs and associated shorthand notes from the clinical sessions. RESULTS Identified use cases for ChatGPT integration included addressing medical knowledge gaps through discrete medical knowledge inquiries, building differential diagnoses and engaging dual-process thinking, challenging medical axioms, using cognitive aids to support acute care decision-making, and improving complex care management by facilitating conversations with subspecialties. Potential additional uses included engaging in difficult conversations with patients, exploring ethical challenges and general medical ethics teaching, personal continuing medical education resources, developing ward-based teaching tools, supporting and automating clinical documentation, and supporting productivity and task management. LLM biases, misinformation, ethics, and health equity were identified as areas of concern and potential limitations to clinical and training use. A code of conduct on ethical and appropriate use was also developed to guide team usage on the wards. CONCLUSIONS Overall, ChatGPT offers a novel tool to enhance ward-based learning through rapid information querying, second-order content exploration, and engaged team discussion regarding generated responses. More research is needed to fully understand contexts for educational use, particularly regarding the risks and limitations of the tool in clinical settings and its impacts on trainee development.
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Affiliation(s)
- Anthony Skryd
- Department of Medicine, NYU Langone Health, New York City, NY, United States
| | - Katharine Lawrence
- Department of Population Health, NYU Grossman School of Medicine, New York City, NY, United States
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Schaye V, Triola MM. The generative artificial intelligence revolution: How hospitalists can lead the transformation of medical education. J Hosp Med 2024. [PMID: 38591332 DOI: 10.1002/jhm.13360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 03/19/2024] [Accepted: 03/23/2024] [Indexed: 04/10/2024]
Affiliation(s)
- Verity Schaye
- Department of Medicine, New York University Grossman School of Medicine, New York, New York
| | - Marc M Triola
- Institute for Innovations in Medical Education, New York University Grossman School of Medicine, New York, New York
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11
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Sievert M, Aubreville M, Mueller SK, Eckstein M, Breininger K, Iro H, Goncalves M. Diagnosis of malignancy in oropharyngeal confocal laser endomicroscopy using GPT 4.0 with vision. Eur Arch Otorhinolaryngol 2024; 281:2115-2122. [PMID: 38329525 DOI: 10.1007/s00405-024-08476-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 01/11/2024] [Indexed: 02/09/2024]
Abstract
PURPOSE Confocal Laser Endomicroscopy (CLE) is an imaging tool, that has demonstrated potential for intraoperative, real-time, non-invasive, microscopical assessment of surgical margins of oropharyngeal squamous cell carcinoma (OPSCC). However, interpreting CLE images remains challenging. This study investigates the application of OpenAI's Generative Pretrained Transformer (GPT) 4.0 with Vision capabilities for automated classification of CLE images in OPSCC. METHODS CLE Images of histological confirmed SCC or healthy mucosa from a database of 12 809 CLE images from 5 patients with OPSCC were retrieved and anonymized. Using a training data set of 16 images, a validation set of 139 images, comprising SCC (83 images, 59.7%) and healthy normal mucosa (56 images, 40.3%) was classified using the application programming interface (API) of GPT4.0. The same set of images was also classified by CLE experts (two surgeons and one pathologist), who were blinded to the histology. Diagnostic metrics, the reliability of GPT and inter-rater reliability were assessed. RESULTS Overall accuracy of the GPT model was 71.2%, the intra-rater agreement was κ = 0.837, indicating an almost perfect agreement across the three runs of GPT-generated results. Human experts achieved an accuracy of 88.5% with a substantial level of agreement (κ = 0.773). CONCLUSIONS Though limited to a specific clinical framework, patient and image set, this study sheds light on some previously unexplored diagnostic capabilities of large language models using few-shot prompting. It suggests the model`s ability to extrapolate information and classify CLE images with minimal example data. Whether future versions of the model can achieve clinically relevant diagnostic accuracy, especially in uncurated data sets, remains to be investigated.
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Affiliation(s)
- Matti Sievert
- Department of Otorhinolaryngology, Head and Neck Surgery, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen University Hospital, Erlangen, Germany
| | | | - Sarina Katrin Mueller
- Department of Otorhinolaryngology, Head and Neck Surgery, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen University Hospital, Erlangen, Germany
| | - Markus Eckstein
- Institute of Pathology, Friedrich-Alexander-Universität Erlangen-Nürnberg, University Hospital, Erlangen, Germany
| | - Katharina Breininger
- Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Heinrich Iro
- Department of Otorhinolaryngology, Head and Neck Surgery, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen University Hospital, Erlangen, Germany
| | - Miguel Goncalves
- Department of Otorhinolaryngology, Plastic and Aesthetic Operations, University Hospital Würzburg, Joseph-Schneider-Straße 11, 97080, Würzburg, Germany.
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12
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Xu X, Chen Y, Miao J. Opportunities, challenges, and future directions of large language models, including ChatGPT in medical education: a systematic scoping review. JOURNAL OF EDUCATIONAL EVALUATION FOR HEALTH PROFESSIONS 2024; 21:6. [PMID: 38486402 PMCID: PMC11035906 DOI: 10.3352/jeehp.2024.21.6] [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: 01/12/2024] [Accepted: 03/05/2024] [Indexed: 03/19/2024]
Abstract
BACKGROUND ChatGPT is a large language model (LLM) based on artificial intelligence (AI) capable of responding in multiple languages and generating nuanced and highly complex responses. While ChatGPT holds promising applications in medical education, its limitations and potential risks cannot be ignored. METHODS A scoping review was conducted for English articles discussing ChatGPT in the context of medical education published after 2022. A literature search was performed using PubMed/MEDLINE, Embase, and Web of Science databases, and information was extracted from the relevant studies that were ultimately included. RESULTS ChatGPT exhibits various potential applications in medical education, such as providing personalized learning plans and materials, creating clinical practice simulation scenarios, and assisting in writing articles. However, challenges associated with academic integrity, data accuracy, and potential harm to learning were also highlighted in the literature. The paper emphasizes certain recommendations for using ChatGPT, including the establishment of guidelines. Based on the review, 3 key research areas were proposed: cultivating the ability of medical students to use ChatGPT correctly, integrating ChatGPT into teaching activities and processes, and proposing standards for the use of AI by medical students. CONCLUSION ChatGPT has the potential to transform medical education, but careful consideration is required for its full integration. To harness the full potential of ChatGPT in medical education, attention should not only be given to the capabilities of AI but also to its impact on students and teachers.
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Affiliation(s)
- Xiaojun Xu
- Division of Hematology/Oncology, Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Centre for Child Health, Zhejiang, China
| | - Yixiao Chen
- Division of Hematology/Oncology, Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Centre for Child Health, Zhejiang, China
| | - Jing Miao
- Division of Hematology/Oncology, Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Centre for Child Health, Zhejiang, China
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13
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Hess BJ, Cupido N, Ross S, Kvern B. Becoming adaptive experts in an era of rapid advances in generative artificial intelligence. MEDICAL TEACHER 2024; 46:300-303. [PMID: 38092006 DOI: 10.1080/0142159x.2023.2289844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 11/28/2023] [Indexed: 02/24/2024]
Affiliation(s)
- Brian J Hess
- College of Family Physicians of Canada, Department of Certification and Assessment, Mississauga, Ontario, Canada
| | - Nathan Cupido
- The Wilson Centre, University Health Network and Temerty Faculty of Medicine, and the Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Ontario, Canada
| | - Shelley Ross
- Department of Family Medicine, Faculty of Medicine and Dentistry, College of Health Sciences, University of Alberta, Edmonton, Canada
| | - Brent Kvern
- College of Family Physicians of Canada, Department of Certification and Assessment, Mississauga, Ontario, Canada
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14
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Kumar A, Burr P, Young TM. Using AI Text-to-Image Generation to Create Novel Illustrations for Medical Education: Current Limitations as Illustrated by Hypothyroidism and Horner Syndrome. JMIR MEDICAL EDUCATION 2024; 10:e52155. [PMID: 38386400 PMCID: PMC10921331 DOI: 10.2196/52155] [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: 08/24/2023] [Revised: 01/12/2024] [Accepted: 01/29/2024] [Indexed: 02/23/2024]
Abstract
Our research letter investigates the potential, as well as the current limitations, of widely available text-to-image tools in generating images for medical education. We focused on illustrations of important physical signs in the face (for which confidentiality issues in conventional patient photograph use may be a particular concern) that medics should know about, and we used facial images of hypothyroidism and Horner syndrome as examples.
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Affiliation(s)
- Ajay Kumar
- Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Pierce Burr
- Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Tim Michael Young
- Queen Square Institute of Neurology, University College London, London, United Kingdom
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15
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Semeraro F, Gamberini L, Carmona F, Monsieurs KG. Clinical questions on advanced life support answered by artificial intelligence. A comparison between ChatGPT, Google Bard and Microsoft Copilot. Resuscitation 2024; 195:110114. [PMID: 38211808 DOI: 10.1016/j.resuscitation.2024.110114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Accepted: 01/03/2024] [Indexed: 01/13/2024]
Affiliation(s)
- Federico Semeraro
- Department of Anesthesia, Intensive Care and Prehospital Emergency, Maggiore Hospital Carlo Alberto Pizzardi, Bologna, Italy.
| | - Lorenzo Gamberini
- Department of Anesthesia, Intensive Care and Prehospital Emergency, Maggiore Hospital Carlo Alberto Pizzardi, Bologna, Italy
| | | | - Koenraad G Monsieurs
- Department of Emergency Medicine, Antwerp University Hospital and University of Antwerp, Antwerp, Belgium
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16
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García-Zamora S, López-Santi R, Sosa-Liprandi Á, Hardy CA, Miranda-Arboleda AF, Echeverría LE, Arce JM, Uribe W, Zaidel EJ, Aguilera Mora LF, Di-Toro D, Baranchuk A. Impact of an online course on enhancing the diagnosis of Chagas disease in Latin America. ARCHIVOS PERUANOS DE CARDIOLOGIA Y CIRUGIA CARDIOVASCULAR 2024; 5:7-12. [PMID: 38596605 PMCID: PMC10999316 DOI: 10.47487/apcyccv.v5i1.341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 03/09/2024] [Indexed: 04/11/2024]
Abstract
Objective Chagas disease poses a public health problem in Latin America, and the electrocardiogram is a crucial tool in the diagnosis and monitoring of this pathology. In this context, the aim of this study was to quantify the change in the ability to detect electrocardiographic patterns among healthcare professionals after completing a virtual course. Materials and Methods An asynchronous virtual course with seven pre-recorded classes was conducted. Participants answered the same questionnaire at the beginning and end of the training. Based on these responses, pre and post-test results for each participant were compared. Results The study included 1656 participants from 21 countries; 87.9% were physicians, 5.2% nurses, 4.1% technicians, and 2.8% medical students. Initially, 3.1% answered at least 50% of the pre-test questions correctly, a proportion that increased to 50.4% after the course (p=0.001). Regardless of their baseline characteristics, 82.1% of course attendees improved their answers after completing the course. Conclusions The implementation of an asynchronous online course on electrocardiography in Chagas disease enhanced the skills of both medical and non-medical personnel to recognize this condition.
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Affiliation(s)
- Sebastián García-Zamora
- Servicio de Cardiología, Sanatorio Delta, Rosario, Argentina.Servicio de CardiologíaSanatorio DeltaRosarioArgentina
| | - Ricardo López-Santi
- Servicio de Cardiología, Hospital Italiano de La Plata, Buenos Aires, Argentina.Servicio de CardiologíaHospital Italiano de La PlataBuenos AiresArgentina
| | - Álvaro Sosa-Liprandi
- Servicio de Cardiología, Sanatorio Güemes, Buenos Aires, Argentina.Servicio de CardiologíaSanatorio GüemesBuenos AiresArgentina
| | - Carina A. Hardy
- Servicio de Electrofisiología, Instituto do Coração (Incor), Facultad de Medicina de San Pablo, Brazil.Servicio de ElectrofisiologíaInstituto do Coração (Incor)Facultad de MedicinaSan PabloBrazil
| | - Andrés F. Miranda-Arboleda
- Servicio de Arritmias, Brigham and Women’s Hospital, Harvard Medical School, Boston, United States.Servicio de ArritmiasBrigham and Women’s HospitalHarvard Medical SchoolBostonUnited States
| | - Luis E. Echeverría
- Clínica de insuficiencia cardíaca y trasplante, Fundación Cardiovascular de Colombia, Floridablanca, Colombia.Clínica de insuficiencia cardíaca y trasplanteFundación Cardiovascular de ColombiaFloridablancaColombia
| | - José Mauricio Arce
- Servicio de Arritmias, Instituto Nacional de Tórax, La Paz, Bolivia.Servicio de ArritmiasInstituto Nacional de TóraxLa PazBolivia
| | - William Uribe
- Sociedad Inter Americana de Cardiología, Medellín, Colombia.Sociedad Inter Americana de CardiologíaMedellínColombia
| | - Ezequiel José Zaidel
- Departamento de Farmacología, Facultad de Medicina, Universidad de Buenos Aires, Buenos Aires, Argentina.Universidad de Buenos AiresDepartamento de FarmacologíaFacultad de MedicinaUniversidad de Buenos AiresBuenos AiresArgentina
| | - Luisa Fernanda Aguilera Mora
- Clínica de Insuficiencia Cardiaca, Instituto Cardiovascular de Mínima Invasión, Jalisco, Mexico.Clínica de Insuficiencia CardiacaInstituto Cardiovascular de Mínima InvasiónJaliscoMexico
| | - Darío Di-Toro
- Hospital General de Agudos Dr. Cosme Argerich, Buenos Aires, Argentina.Hospital General de Agudos Dr. Cosme ArgerichBuenos AiresArgentina
| | - Adrián Baranchuk
- División de Cardiología, Universidad de Queen, Kingston, Ontario, Canada.División de CardiologíaUniversidad de QueenKingston, OntarioCanada
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Lee H, Park S. Information amount, accuracy, and relevance of generative artificial intelligence platforms’ answers regarding learning objectives of medical arthropodology evaluated in English and Korean queries in December 2023: a descriptive study. JOURNAL OF EDUCATIONAL EVALUATION FOR HEALTH PROFESSIONS 2023; 20:39. [PMID: 38151711 DOI: 10.3352/jeehp.2023.20.39] [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: 12/21/2023] [Accepted: 12/28/2023] [Indexed: 12/29/2023]
Abstract
PURPOSE This study assessed the performance of 6 generative artificial intelligence (AI) platforms on the learning objectives of medical arthropodology in a parasitology class in Korea. We examined the AI platforms’ performance by querying in Korean and English to determine their information amount, accuracy, and relevance in prompts in both languages. METHODS From December 15 to 17, 2023, 6 generative AI platforms—Bard, Bing, Claude, Clova X, GPT-4, and Wrtn—were tested on 7 medical arthropodology learning objectives in English and Korean. Clova X and Wrtn are platforms from Korean companies. Responses were evaluated using specific criteria for the English and Korean queries. RESULTS Bard had abundant information but was fourth in accuracy and relevance. GPT-4, with high information content, ranked first in accuracy and relevance. Clova X was 4th in amount but 2nd in accuracy and relevance. Bing provided less information, with moderate accuracy and relevance. Wrtn’s answers were short, with average accuracy and relevance. Claude AI had reasonable information, but lower accuracy and relevance. The responses in English were superior in all aspects. Clova X was notably optimized for Korean, leading in relevance. CONCLUSION In a study of 6 generative AI platforms applied to medical arthropodology, GPT-4 excelled overall, while Clova X, a Korea-based AI product, achieved 100% relevance in Korean queries, the highest among its peers. Utilizing these AI platforms in classrooms improved the authors’ self-efficacy and interest in the subject, offering a positive experience of interacting with generative AI platforms to question and receive information.
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Affiliation(s)
- Hyunju Lee
- College of Medicine, Hallym University, Chuncheon, Korea
| | - Soobin Park
- College of Medicine, Hallym University, Chuncheon, Korea
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18
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Knopp MI, Warm EJ, Weber D, Kelleher M, Kinnear B, Schumacher DJ, Santen SA, Mendonça E, Turner L. AI-Enabled Medical Education: Threads of Change, Promising Futures, and Risky Realities Across Four Potential Future Worlds. JMIR MEDICAL EDUCATION 2023; 9:e50373. [PMID: 38145471 PMCID: PMC10786199 DOI: 10.2196/50373] [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: 06/28/2023] [Revised: 12/01/2023] [Accepted: 12/11/2023] [Indexed: 12/26/2023]
Abstract
BACKGROUND The rapid trajectory of artificial intelligence (AI) development and advancement is quickly outpacing society's ability to determine its future role. As AI continues to transform various aspects of our lives, one critical question arises for medical education: what will be the nature of education, teaching, and learning in a future world where the acquisition, retention, and application of knowledge in the traditional sense are fundamentally altered by AI? OBJECTIVE The purpose of this perspective is to plan for the intersection of health care and medical education in the future. METHODS We used GPT-4 and scenario-based strategic planning techniques to craft 4 hypothetical future worlds influenced by AI's integration into health care and medical education. This method, used by organizations such as Shell and the Accreditation Council for Graduate Medical Education, assesses readiness for alternative futures and effectively manages uncertainty, risk, and opportunity. The detailed scenarios provide insights into potential environments the medical profession may face and lay the foundation for hypothesis generation and idea-building regarding responsible AI implementation. RESULTS The following 4 worlds were created using OpenAI's GPT model: AI Harmony, AI conflict, The world of Ecological Balance, and Existential Risk. Risks include disinformation and misinformation, loss of privacy, widening inequity, erosion of human autonomy, and ethical dilemmas. Benefits involve improved efficiency, personalized interventions, enhanced collaboration, early detection, and accelerated research. CONCLUSIONS To ensure responsible AI use, the authors suggest focusing on 3 key areas: developing a robust ethical framework, fostering interdisciplinary collaboration, and investing in education and training. A strong ethical framework emphasizes patient safety, privacy, and autonomy while promoting equity and inclusivity. Interdisciplinary collaboration encourages cooperation among various experts in developing and implementing AI technologies, ensuring that they address the complex needs and challenges in health care and medical education. Investing in education and training prepares professionals and trainees with necessary skills and knowledge to effectively use and critically evaluate AI technologies. The integration of AI in health care and medical education presents a critical juncture between transformative advancements and significant risks. By working together to address both immediate and long-term risks and consequences, we can ensure that AI integration leads to a more equitable, sustainable, and prosperous future for both health care and medical education. As we engage with AI technologies, our collective actions will ultimately determine the state of the future of health care and medical education to harness AI's power while ensuring the safety and well-being of humanity.
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Affiliation(s)
- Michelle I Knopp
- Department of Internal Medicine, College of Medicine, University of Cincinnati, Cincinnati, OH, United States
| | - Eric J Warm
- Department of Internal Medicine, College of Medicine, University of Cincinnati, Cincinnati, OH, United States
| | - Danielle Weber
- Departments of Internal Medicine and Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, OH, United States
| | - Matthew Kelleher
- Departments of Internal Medicine and Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, OH, United States
| | - Benjamin Kinnear
- Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, OH, United States
| | - Daniel J Schumacher
- Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, OH, United States
| | - Sally A Santen
- Department of Medical Education, College of Medicine, University of Cincinnati, Cincinnati, OH, United States
| | - Eneida Mendonça
- Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, OH, United States
| | - Laurah Turner
- Department of Medical Education, College of Medicine, University of Cincinnati, Cincinnati, OH, United States
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