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arXiv 2024年

作者： Fleurence, Rachael L. Dawoud, Dalia Bian, Jiang Higashi, Mitchell K. Wang, Xiaoyan Xu, Hua Chhatwal, Jagpreet Ayer, Turgay Office of the Director National Institutes of Health National Institute of Biomedical Imaging and Bioengineering BethesdaMD United States National Institute for Health and Care Excellence London United Kingdom Cairo University Faculty of Pharmacy Cairo Egypt Health Outcomes and Biomedical Informatics College of Medicine University of Florida FL United States Biomedical Informatics Clinical and Translational Science Institute University of Florida FL United States Office of Data Science and Research Implementation University of Florida Health GainesvilleFL United States ISPOR The Professional Society for Health Economics and Outcomes Research LawrencevilleNJ United States Tulane University School of Public Health and Tropical Medicine New OrleansLA United States Intelligent Medical Objects RosemontIL United States Institute Department of Biomedical Informatics and Data Science School of Medicine Yale University New HavenCT United States Institute for Technology Assessment Massachusetts General Hospital Harvard Medical School BostonMA United States Center for Health Decision Science Harvard University BostonMA United States Value Analytics Labs BostonMA United States Center for Health & Humanitarian Systems Georgia Institute of Technology AtlantaGA United States

Introduction: Generative Artificial Intelligence, particularly large language models (LLMs), offers transformative potential for Health Economics and Outcomes research (HEOR). However, evaluating the quality, transparency, and rigor of LLM-assisted research lacks standardized guidance. This article introduces the ELEVATE-AI LLMs framework and checklist, designed to support researchers and reviewers in assessing LLM use in HEOR. Methods: The ELEVATE-AI LLMs framework was developed through a targeted review of existing guidelines and evaluation frameworks. The framework comprises ten evaluation domains, including model characteristics, accuracy, comprehensiveness, and fairness. The accompanying checklist operationalizes the framework. To validate the framework, we applied it to two published studies, demonstrating its usability across different HEOR tasks. Results: The ELEVATE-AI LLMs framework provides a comprehensive structure for evaluating LLM-assisted research, while the checklist facilitates practical application. Validation of the framework and checklist on studies of systematic literature reviews and health economic modeling highlighted their ability to identify strengths and gaps in reporting. Limitations: While the ELEVATE-AI LLMs framework provides robust guidance, its broader generalizability and applicability to diverse HEOR tasks require further empirical testing. Additionally, several metrics adapted from computer science, need further validation in HEOR contexts. Conclusion: The ELEVATE-AI LLMs framework and checklist fill a critical gap in HEOR by offering structured guidance for evaluating LLM-assisted research. By promoting transparency, accuracy, and reproducibility, they aim to standardize and improve the integration of LLMs into HEOR, ensuring their outputs meet the field's rigorous standards. © 2024, CC BY.

关键词： Ability testing

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Generative AI in Health Economics and Outcomes research: A Taxonomy of Key Definitions and Emerging Applications – an ISPOR Working Group Report

arXiv

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arXiv 2024年

作者： Fleurence, Rachael L. Wang, Xiaoyan Bian, Jiang Higashi, Mitchell K. Ayer, Turgay Xu, Hua Dawoud, Dalia Chhatwal, Jagpreet Office of the Director National Institutes of Health National Institute of Biomedical Imaging and Bioengineering BethesdaMD United States Tulane University School of Public Health and Tropical Medicine New OrleansLA United States Intelligent Medical Objects RosemontIL United States College of Medicine University of Florida FL United States Biomedical Informatics Clinical and Translational Science Institute University of Florida FL United States Office of Data Science and Research Implementation University of Florida Health GainesvilleFL United States ISPOR The Professional Society for Health Economics and Outcomes Research LawrencevilleNJ United States Center for Health & Humanitarian Systems Georgia Institute of Technology AtlantaGA United States Value Analytics Labs BostonMA United States Institute Department of Biomedical Informatics and Data Science School of Medicine Yale University New HavenCT United States National Institute for Health and Care Excellence London United Kingdom Cairo University Faculty of Pharmacy Cairo Egypt Institute for Technology Assessment Massachusetts General Hospital Harvard Medical School BostonMA United States Center for Health Decision Science Harvard University BostonMA United States

Objective: This article offers a taxonomy of generative artificial intelligence (AI) for health economics and outcomes research (HEOR), explores its emerging applications, and outlines methods to enhance the accuracy and reliability of AI-generated outputs. Methods: The review defines foundational generative AI concepts and highlights current HEOR applications, including systematic literature reviews, health economic modeling, real-world evidence generation, and dossier development. Approaches such as prompt engineering (zero-shot, few-shot, chain-of-thought, persona pattern prompting), retrieval-augmented generation, model fine-tuning, and the use of domain-specific models are introduced to improve AI accuracy and reliability. Results: Generative AI shows significant potential in HEOR, enhancing efficiency, productivity, and offering novel solutions to complex challenges. Foundation models are promising in automating complex tasks, though challenges remain in scientific reliability, bias, interpretability, and workflow integration. The article discusses strategies to improve the accuracy of these AI tools. Conclusion: Generative AI could transform HEOR by increasing efficiency and accuracy across various applications. However, its full potential can only be realized by building HEOR expertise and addressing the limitations of current AI technologies. As AI evolves, ongoing research and innovation will shape its future role in the field. © 2024, CC BY.

关键词： Generative adversarial networks

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Generative AI for Health Technology Assessment: Opportunities, Challenges, and Policy Considerations

arXiv

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arXiv 2024年

作者： Fleurence, Rachael L. Bian, Jiang Wang, Xiaoyan Xu, Hua Dawoud, Dalia Higashi, Mitch Chhatwal, Jagpreet National Institute of Biomedical Imaging and Bioengineering National Institutes of Health BethesdaMD United States Health Outcomes and Biomedical Informatics College of Medicine University of Florida GainesvilleFL United States Biomedical Informatics Clinical and Translational Science Institute University of Florida GainesvilleFL United States Office of Data Science and Research Implementation University of Florida Health GainesvilleFL United States Tulane University School of Public Health and Tropical Medicine New OrleansLA United States Intelligent Medical Objects RosemontIL United States Department of Biomedical Informatics and Data Science School of Medicine Yale University New HavenCT United States National Institute for Health and Care Excellence London United Kingdom Cairo University Faculty of Pharmacy Cairo Egypt ISPOR - The Professional Society for Health Economics and Outcomes Research LawrencevilleNJ United States Institute for Technology Assessment Massachusetts General Hospital Harvard Medical School BostonMA United States Center for Health Decision Science Harvard University BostonMA United States

Objective: To provide an introduction to the uses of generative Artificial Intelligence (AI) and foundation models, including large language models (LLMs), in the field of health technology assessment (HTA). Methods: We reviewed applications of generative AI in three areas: systematic literature reviews, real world evidence (RWE) and health economic modeling. Results: (1) Literature reviews: generative AI has the potential to assist in automating aspects of systematic literature reviews by proposing search terms, screening abstracts, extracting data and generating code for meta-analyses;(2) Real World Evidence (RWE): generative AI can facilitate automating processes and analyze large collections of real-world data (RWD) including unstructured clinical notes and imaging;(3) Health economic modeling: generative AI can aid in the development of health economic models, from conceptualization to validation. Limitations in the use of foundation models and LLMs include challenges surrounding their scientific rigor and reliability, the potential for bias, implications for equity, as well as nontrivial concerns regarding adherence to regulatory and ethical standards, particularly in terms of data privacy and security. Additionally, we survey the current policy landscape and provide suggestions for HTA agencies on responsibly integrating generative AI into their workflows, emphasizing the importance of human oversight and the fast-evolving nature of these tools. Conclusions: While generative AI technology holds promise with respect to HTA applications, it is still undergoing rapid developments and improvements. Continued careful evaluation of their applications to HTA is required. Both developers and users of research incorporating these tools, should familiarize themselves with their current capabilities and limitations. © 2024, CC BY.

关键词： Electronic health record

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Automated Identification of Eviction Status from Electronic Health Record Notes

arXiv

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arXiv 2022年

作者： Yao, Zonghai Tsai, Jack Liu, Weisong Levy, David A. Druhl, Emily Reisman, Joel I. Yu, Hong Manning College of Information and Computer Sciences University of Massachusetts Amherst MA United States National Center on Homelessness among Veterans U.S. Department of Veterans Affairs Homeless Programs Office WashingtonDC United States School of Public Health University of Texas Health Science Center at Houston HoustonTX United States Department of Psychiatry Yale University School of Medicine New HavenCT United States Department of Medicine University of Massachusetts Medical School WorcesterMA United States Center for Biomedical and Health Research in Data Sciences Miner School of Computer and Information Sciences University of Massachusetts LowellMA United States Center for Healthcare Organization and Implementation Research VA Bedford Health Care MA United States

Objective: Evictions are important social and behavioral determinants of health. Evictions are associated with a cascade of negative events that can lead to unemployment, housing insecurity/homelessness, long-term poverty, and mental health problems. In this study, we developed a natural language processing system to automatically detect eviction status from electronic health record (EHR) notes. Materials and Methods: We first defined eviction status (eviction presence and eviction period) and then annotated eviction status in 5000 EHR notes from the Veterans Health Administration (VHA). We developed a novel model, KIRESH, that has shown to substantially outperform other state-of-the-art models such as fine-tuning pre-trained language models like BioBERT and Bio ClinicalBERT. Moreover, we designed a novel prompt to further improve the model performance by using the intrinsic connection between the two sub-tasks of eviction presence and period prediction. Finally, we used the Temperature Scaling-based Calibration on our KIRESH-Prompt method to avoid over-confidence issues arising from the imbalance dataset. Results: KIRESH-Prompt substantially outperformed strong baseline models including fine-tuning the Bio ClinicalBERT model to achieve 0.74672 MCC, 0.71153 Macro-F1, and 0.83396 Micro-F1 in predicting eviction period and 0.66827 MCC, 0.62734 Macro-F1, and 0.7863 Micro-F1 in predicting eviction presence. We also conducted additional experiments on a benchmark social determinants of health (SBDH) dataset to demonstrate the generalizability of our methods. Conclusion and Future Work: KIRESH-Prompt has substantially improved eviction status classification. We plan to deploy KIRESH-Prompt to the VHA EHRs as an eviction surveillance system to help address the US Veterans' housing insecurity. Copyright © 2022, The Authors. All rights reserved.

关键词： Calibration

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The science of Precision Prevention: research Opportunities and Clinical Applications to Reduce Cardiovascular Health Disparities

JACC: Advances

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JACC: Advances 2024年第1期3卷 100759-100759页

作者： Pearson, Thomas A. Vitalis, Debbie Pratt, Charlotte Campo, Rebecca Armoundas, Antonis A. Au, David Beech, Bettina Brazhnik, Olga Chute, Christopher G. Davidson, Karina W. Diez-Roux, Ana V. Fine, Lawrence J. Gabriel, Davera Groenveld, Peter Hall, Jaclyn Hamilton, Alison B. Hu, Hui Ji, Heng Kind, Amy Kraus, William E. Krumholz, Harlan Mensah, George A. Merchant, Raina M. Mozaffarian, Dariush Murray, David M. Neumark-Sztainer, Dianne Petersen, Maya Goff, David College of Medicine and College of Public Health and Health Professions University of Florida Health Science Center Gainesville FL United States Division of Cardiovascular Sciences National Heart Lung and Blood Institute National Institutes of Health Bethesda MD United States Cardiovascular Research Center Massachusetts General Hospital and Broad Institute Massachusetts Institute of Technology Cambridge MA United States Center of Innovation for Veteran-Centered and Value-Driven Care University of Washington Seattle WA United States UH Population Health University of Houston Houston TX United States Johns Hopkins Medicine Institute for Clinical and Translational Research Baltimore MD United States Institute of Health System Science Feinstein Institutes for Medical Research Northwell Health New Hyde Park NY United States Feinstein Institutes for Medical Research Northwell Health Manhasset NY United States Urban Health Collaborative Drexel Dornsife School of Public Health Philadelphia PA United States Biomedical Informatics and Data Science Section Johns Hopkins University School of Medicine Baltimore MD United States Center for Health Care Transformation and Innovation University of Pennsylvania Perelman School of Medicine Philadelphia PA United States Department of Health Outcomes and Biomedical Informatics Institute for Child Health Policy College of Medicine University of Florida Gainesville FL United States Center for the Study of Healthcare Innovation Implementation & Policy VA Greater Los Angeles Healthcare System Los Angeles CA United States Channing Division of Network Medicine Department of Medicine Brigham and Women's Hospital and Harvard Medical School Boston MA United States Department of Computer Science University of Illinois Urbana-Champaign Champaign IL United States Center for Health Disparities Research (CHDR) University of Wisconsin School of Medicine and Public Health Madison WI United States Duke Molecular Physiology Insti

Precision prevention embraces personalized prevention but includes broader factors such as social determinants of health to improve cardiovascular health. The quality, quantity, precision, and diversity of data relatable to individuals and communities continue to expand. New analytical methods can be applied to these data to create tools to attribute risk, which may allow a better understanding of cardiovascular health disparities. Interventions using these analytic tools should be evaluated to establish feasibility and efficacy for addressing cardiovascular disease disparities in diverse individuals and communities. Training in these approaches is important to create the next generation of scientists and practitioners in precision prevention. This state-of-the-art review is based on a workshop convened to identify current gaps in knowledge and methods used in precision prevention intervention research, discuss opportunities to expand trials of implementation science to close the health equity gaps, and expand the education and training of a diverse precision prevention workforce. © 2023 The Authors

关键词： data science health equity health promotion implementation science personalized medicine precision analytics

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A Taxonomy of Generative AI in HEOR: Concepts, Emerging Applications, and Advanced Tools - An ISPOR Working Group Report

引用

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes research 2025年

作者： Rachael L Fleurence Xiaoyan Wang Jiang Bian Mitchell K Higashi Turgay Ayer Hua Xu Dalia Dawoud Jagpreet Chhatwal National Institute of Biomedical Imaging and Bioengineering Bethesda MD United States. Electronic address: Rachael.fleurence@nih.gov. Tulane University School of Public Health and Tropical Medicine New Orleans LA Intelligent Medical Objects Rosemont IL United States. Health Outcomes and Biomedical Informatics College of Medicine University of Florida FL United States Biomedical Informatics Clinical and Translational Science Institute University of Florida FL United States Office of Data Science and Research Implementation University of Florida Health Gainesville FL United States Regenstrief Institute Indianapolis IN United States Biostatistics and Health Data Science School of Medicine Indiana University Indianapolis IN United States. ISPOR The Professional Society for Health Economics and Outcomes Research Lawrenceville NJ United States. Center for Health & Humanitarian Systems Georgia Institute of Technology Atlanta GA Value Analytics Labs Boston MA United States. Institute Department of Biomedical Informatics and Data Science School of Medicine Yale University New Haven CT United States. National Institute for Health and Care Excellence London United Kingdom Cairo University Faculty of Pharmacy Cairo Egypt. Institute for Technology Assessment Massachusetts General Hospital Harvard Medical School Boston MA United States Center for Health Decision Science Harvard University Boston MA United States.

OBJECTIVE:This article presents a taxonomy of generative artificial intelligence (AI) for health economics and outcomes research (HEOR), explores emerging applications, outlines methods to improve the accuracy and reliability of AI-generated outputs and describes current limitations. METHODS:Foundational generative AI concepts are defined, and current HEOR applications are highlighted, including for systematic literature reviews, health economic modeling, real-world evidence generation, and dossier development. Techniques such as prompt engineering (e.g., zero-shot, few-shot, chain-of-thought, persona pattern prompting), retrieval-augmented generation, model fine-tuning, and domain-specific models, and use of agents are introduced to enhance AI performance. Limitations associated with the use of generative AI foundation models are described. RESULTS:Generative AI demonstrates significant potential in HEOR, offering enhanced efficiency, productivity, and innovative solutions to complex challenges. While foundation models show promise in automating complex tasks, challenges persist in scientific accuracy and reproducibility, bias and fairness and operational deployment. Strategies to address these issues and improve AI accuracy are discussed. CONCLUSION:Generative AI has the potential to transform HEOR by improving efficiency and accuracy across diverse applications. However, realizing this potential requires building HEOR expertise and addressing the limitations of current AI technologies. Ongoing research and innovation will be key to shaping AI's future role in our field.

关键词：

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The SUPER reporting guideline suggested for reporting of surgical technique

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Hepatobiliary Surgery and Nutrition 2023年第4期12卷 534-544,I0024-I0026页

作者： Kaiping Zhang Yanfang Ma Jinlin Wu Qianling Shi Leandro Cardoso Barchi Marco Scarci Rene Horsleben Petersen Calvin S.H.Ng Steven Hochwald Ryuichi Waseda Fabio Davoli Robert Fruscio Giovanni Battista Levi Sandri Michel Gonzalez Benjamin Wei Guillaume Piessen Jianfei Shen Xianzhuo Zhang Panpan Jiao Yulong He Nuria M.Novoa Benedetta Bedetti Sebastien Gilbert Alan D.L.Sihoe Alper Toker Alfonso Fiorelli Marcelo F.Jimenez Toni Lerut Aung Y.Oo Grace S.Li Xueqin Tang Yawen Lu Hussein Elkhayat Tomaz Stupnik Tanel Laisaar Firas Abu Akar Diego Gonzalez-Rivas Zhanhao Su Bin Qiu Stephen D.Wang Yaolong Chen Shugeng Gao Editorial Office AME Publishing CompanyHong KongChina School of Chinese Medicine Hong Kong Baptist UniversityHong KongChina Department of Cardiovascular Surgery Guangdong Provincial People’s HospitalGuangdong Academy of Medical SciencesGuangzhouChina Evidence-based Medicine Center School of Basic Medical SciencesLanzhou UniversityLanzhouChina Lanzhou University Institute of Health Data Science LanzhouChina Digestive Surgery Division Department of GastroenterologyUniversity of Sao Paulo School of MedicineSão PauloBrazil Department of Cardiothoracic Surgery Imperial College Healthcare NHS TrustLondonUK Department of Cardiothoracic Surgery University Hospital of CopenhagenRigshospitaletCopenhagenDenmark The Chinese University of Hong Kong Prince of Wales HospitalShatinNew TerritoriesHong KongChina Department of Surgical Oncology Roswell Park Cancer InstituteBuffaloNYUSA Department of General Thoracic Breast and Pediatric SurgeryFukuoka UniversityFukuokaJapan Department of Thoracic Surgery AUSL RomagnaS.Maria delle Croci Teaching HospitalRavennaItaly Clinic of Obstetrics and Gynecology University of Milan-BicoccaSan Gerardo HospitalMonzaItaly Division of General Surgery and Liver Transplantation S.Camillo HospitalRomeItaly Service of Thoracic Surgery Lausanne University Hospital(CHUV)LausanneSwitzerland Division of Cardiothoracic Surgery Department of SurgeryUniversity of Alabama BirminghamBirminghamALUSA University of Lille CNRSInsermCHU LilleUMR9020-U1277-CANTHER-Cancer Heterogeneity Plasticity and Resistance to TherapiesLilleFrance Department of Cardiothoracic Surgery Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical UniversityLinhaiChina The First School of Clinical Medicine Lanzhou UniversityLanzhouChina Thoracic Surgery Service University Hospital of SalamancaBiomedical Institute of SalamancaSalamancaSpain Department of Thoracic Surgery Helios Clinic Bonn/Rhein SiegBonnGermany Division of Thoracic Surgery Department of SurgeryThe Ottawa

Background:Existing reporting guidelines pay insufficient attention to the detail and comprehensiveness reporting of surgical *** Surgical techniqUe rePorting chEcklist and standaRds(SUPER)aims to address this gap by defining reporting standards for surgical *** SUPER guideline intends to apply to articles that encompass surgical technique in any study design,surgical discipline,and stage of surgical ***:Following the EQUATOR(Enhancing the QUAlity and Transparency Of health research)Network approach,16 surgeons,journal editors,and methodologists reviewed existing reporting guidelines relating to surgical technique,reviewed papers from 15 top journals,and brainstormed to draft initial items for the *** initial items were revised through a three-round Delphi survey from 21 multidisciplinary Delphi panel experts from 13 countries and *** final SUPER items were formed after an online consensus meeting to resolve disagreements and a three-round wording refinement by all 16 SUPER working group members and five SUPER ***:The SUPER reporting guideline includes 22 items that are considered essential for good and informative surgical technique *** items are divided into six sections:background,rationale,and objectives(items 1 to 5);preoperative preparations and requirements(items 6 to 9);surgical technique details(items 10 to 15);postoperative considerations and tasks(items 16 to 19);summary and prospect(items 20 and 21);and other information(item 22).Conclusions:The SUPER reporting guideline has the potential to guide detailed,comprehensive,and transparent surgical technique reporting for *** may also assist journal editors,peer reviewers,systematic reviewers,and guideline developers in the evaluation of surgical technique papers and help practitioners to better understand and reproduce surgical technique.

关键词： Surgical technique surgical innovation reporting guideline reporting checklist Surgical techniqUe rePorting chEcklist and standaRds(SUPER)

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Supporting the implementation of connected care technologies in the Veterans Health Administration: Cross-sectional survey findings from the Veterans Engagement with Technology Collaborative (VET-C) cohort

Journal of Participatory Medicine

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Journal of Participatory Medicine 2020年第3期12卷 e21214页

作者： Etingen, Bella Amante, Daniel J. Martinez, Rachael N. Smith, Bridget M. Shimada, Stephanie L. Richardson, Lorilei Patterson, Angela Houston, Thomas K. Frisbee, Kathleen L. Hogan, Timothy P. eHealth Partnered Evaluation Initiative Edith Nourse Rogers Memorial Veterans Hospital Bedford MA United States Center of Innovation for Complex Chronic Healthcare Hines Veterans Affairs Hospital 5000 5th Ave Hines 60141 IL United States Division of Health Informatics and Implementation Science Department of Population and Quantitative Health Sciences University of Massachusetts Medical School Worcester MA United States Northwestern University Feinberg School of Medicine Chicago IL United States Center for Healthcare Organization and Implementation Research Edith Nourse Rogers Memorial Veterans Hospital Bedford MA United States Department of Health Law Policy and Management Boston University School of Public Health Boston MA United States Office of Connected Care Veterans Health Administration US Department of Veterans Affairs Washington DC United States Department of Population and Data Sciences University of Texas Southwestern Medical Center Dallas TX United States

Background: Widespread adoption, use, and integration of patient-facing technologies into the workflow of health care systems has been slow, thus limiting the realization of their potential. A growing body of work has focused on how best to promote adoption and use of these technologies and measure their impacts on processes of care and outcomes. This body of work currently suffers from limitations (eg, cross-sectional analyses, limited patient-generated data linked with clinical records) and would benefit from institutional infrastructure to enhance available data and integrate the voice of the patient into implementation and evaluation efforts. Objective: The Veterans Health Administration (VHA) has launched an initiative called the Veterans Engagement with Technology Collaborative cohort to directly address these challenges. This paper reports the process by which the cohort was developed and describes the baseline data being collected from cohort members. The overarching goal of the Veterans Engagement with Technology Collaborative cohort is to directly engage veterans in the evaluation of new VHA patient-facing technologies and in so doing, to create new infrastructure to support related quality improvement and evaluation activities. Methods: Inclusion criteria for veterans to be eligible for membership in the cohort included being an active user of VHA health care services, having a mobile phone, and being an established user of existing VHA patient-facing technologies as represented by use of the secure messaging feature of VHA’s patient portal. Between 2017 and 2018, we recruited veterans who met these criteria and administered a survey to them over the telephone. Results: The majority of participants (N=2727) were male (2268/2727, 83.2%), White (2226/2727, 81.6%), living in their own apartment or house (2519/2696, 93.4%), and had completed some college (1176/2701, 43.5%) or an advanced degree (1178/2701, 43.6%). Cohort members were 59.9 years old, on averag

关键词： EHealth Mobile health Patient engagement Telehealth Veterans

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Knowledge and Awareness of Generative Artificial Intelligence Use in Medicine Among International Stakeholders: A Cross-Sectional Study

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Journal of evidence-based medicine 2025年第2期18卷 e70034页

作者： Xufei Luo Bingyi Wang Yule Li Shuang Liu Haodong Li Yaxuan Ren Wah Yang Kyle Lam Stephen R Ali Gemma Sharp Fabio Ynoe Moraes Ye Wang Di Zhu Zhenhua Yang Daher Mohammad Robert Fruscio Maged N Kamel Boulos Zhicheng Lin Kazuki Ide Xuping Song Lu Zhang Yih Chung Tham Hui Liu Long Ge Yaolong Chen Zhaoxiang Bian Evidence-Based Medicine Center School of Basic Medical Sciences Lanzhou University Lanzhou China. Research Unit of Evidence-Based Evaluation and Guidelines Chinese Academy of Medical Sciences (2021RU017) School of Basic Medical Sciences Lanzhou University Lanzhou China. World Health Organization Collaboration Center for Guideline Implementation and Knowledge Translation Lanzhou China. Institute of Health Data Science Lanzhou University Lanzhou China. Key Laboratory of Evidence Based Medicine of Gansu Province Lanzhou University Lanzhou China. Editorial Office of Medical Journal of Peking Union Medical College Hospital Peking Union Medical College Hospital Chinese Academy of Medical Sciences & Peking Union Medical College Beijing China. Chinese Journal of Hematology National Clinical Research Center for Blood Diseases Haihe Laboratory of Cell Ecosystem Institute of Hematology & Blood Diseases Hospital Chinese Academy of Medical Sciences & Peking Union Medical College Tianjin China. Tianjin Institutes of Health Science Tianjin China. School of Public Health Lanzhou University Lanzhou China. School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Chengdu China. Department of Metabolic and Bariatric Surgery The First Affiliated Hospital of Jinan University Guangzhou China. Department of Surgery and Cancer Imperial College London London UK. Reconstructive Surgery and Regenerative Medicine Research Centre Institute of Life Sciences Swansea University Medical School Swansea UK. School of Psychology University of Queensland Brisbane Australia. Department of Oncology Queen's University Kingston Canada. School of Chinese Medicine Vincent V.C. Woo Chinese Medicine Clinical Research Institute Hong Kong Baptist University Hong Kong China. Orthopedic Department Hôtel Dieu de France Beirut Lebanon. UO Gynecology Fondazione IRCCS San Gerardo dei Tintori Monza Italy. Department of Medicine and Surgery University of Milan-Bicocca Milan Italy.

OBJECTIVE:To assess the knowledge, attitudes, and practices (KAP) of medical stakeholders regarding the use of generative artificial intelligence (GAI) tools. METHODS:A cross-sectional survey was conducted among stakeholders in medicine. Participants included researchers, clinicians, and medical journal editors with varying degrees of familiarity with GAI tools. The survey questionnaire comprised 40 questions covering four main dimensions: basic information, knowledge, attitudes, and practices related to GAI tools. Descriptive analysis, Pearson's correlation, and multivariable regression were used to analyze the data. RESULTS:The overall awareness rate of GAI tools was 93.3%. Participants demonstrated moderate knowledge (mean score 17.71 ± 5.56), positive attitudes (mean score 73.32 ± 15.83), and reasonable practices (mean score 40.70 ± 12.86). Factors influencing knowledge included education level, geographic region, and attitudes (p < 0.05). Attitudes were influenced by work experience and knowledge (p < 0.05), while practices were driven by both knowledge and attitudes (p < 0.001). Participants from outside China scored higher in all dimensions compared to those from China (p < 0.001). Additionally, 74.0% of participants emphasized the importance of reporting GAI usage in research, and 73.9% advocated for naming the specific tool used. CONCLUSION:The findings highlight a growing awareness and generally positive attitude toward GAI tools among medical stakeholders, alongside the recognition of their ethical implications and the necessity for standardized reporting practices. Targeted training and the development of clear reporting guidelines are recommended to enhance the effective use of GAI tools in medical research and practice.

关键词： ChatGPT cross‐sectional survey generative artificial intelligence knowledge and awareness

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Lessons from COVID-19 for behavioural and communication interventions to enhance vaccine uptake

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Communications psychology 2023年第1期1卷 35页

作者： Stephan Lewandowsky Philipp Schmid Katrine Bach Habersaat Siff Malue Nielsen Holly Seale Cornelia Betsch Robert Böhm Mattis Geiger Brett Craig Cass Sunstein Sunita Sah Noni E MacDonald Eve Dubé Daisy Fancourt Heidi J Larson Cath Jackson Alyona Mazhnaya Mohan Dutta Konstantinos N Fountoulakis Iago Kachkachishvili Anna Soveri Marta Caserotti Dorottya Őri Giovanni de Girolamo Carmen Rodriguez-Blazquez Maria Falcón Maria Romay-Barja Maria João Forjaz Sarah Earnshaw Blomquist Emma Appelqvist Anna Temkina Andreas Lieberoth T S Harvey Dawn Holford Angelo Fasce Pierre Van Damme Margie Danchin School of Psychological Science University of Bristol Bristol UK. stephan.lewandowsky@bristol.ac.uk. Department of Psychology University of Potsdam Potsdam Germany. stephan.lewandowsky@bristol.ac.uk. School of Psychological Science University of Western Australia Nedlands WA Australia. stephan.lewandowsky@bristol.ac.uk. Centre for Language Studies Radboud University Nijmegen Nijmegen Netherlands. Health Communication Department of Implementation Research Bernhard-Nocht-Institute for Tropical Medicine Hamburg Germany. Institute for Planetary Health Behaviour University of Erfurt Erfurt Germany. WHO Regional Office for Europe Copenhagen Denmark. School of Population Health University of New South Wales Sydney Australia. Faculty of Psychology University of Vienna Vienna Austria. Department of Psychology University of Copenhagen Copenhagen Denmark. Center for Social Data Science University of Copenhagen Copenhagen Denmark. Harvard University Harvard Law School Cambridge MA USA. Cornell University Ithaca NY USA. Dalhousie University Halifax Nova Scotia Canada. Department of Anthropology Laval University Quebec City Québec Canada. Department of Behavioural Science and Health University College London London UK. Department of Infectious Disease Epidemiology London School of Hygiene & Tropical Medicine London UK. Institute for Health Metrics & Evaluation University of Washington Seattle WA USA. WHO Country Office Mykhaila Hrushevskoho St 9B Kyiv Ukraine. National University of Kyiv-Mohyla Academy Kyiv Ukraine. Center for Culture-centered Approach to Research and Evaluation (CARE) Massey University Palmerston North New Zealand. School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece. Department of Sociology and Social Work Tbilisi State University Tbilisi Georgia. Institute of Clinical Medicine University of Turku Turku Finland. Department of Developmental Psychology and Socialization University of Padova Padova Italy. Instit

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