BMJ Health & Care Informatics最新文献_第9页

Invitation to join the Healthcare AI Language Group: HeALgroup.AI Initiative. 邀请加入医疗保健人工智能语言组：HeALgroup.AI 计划。

IF 4.1

BMJ Health & Care Informatics Pub Date : 2024-03-12 DOI: 10.1136/bmjhci-2023-100884

Sebastian Manuel Staubli, Basel Jobeir, Michael Spiro, Dimitri Aristotle Raptis

引用次数: 0

Bibliometric analysis of the 3-year trends (2018-2021) in literature on artificial intelligence in ophthalmology and vision sciences. 眼科学和视觉科学领域人工智能文献的 3 年趋势（2018-2021 年）文献计量分析。

IF 4.1

BMJ Health & Care Informatics Pub Date : 2024-02-28 DOI: 10.1136/bmjhci-2023-100780

Hayley Monson, Jeffrey Demaine, Adrianna Perryman, Tina Felfeli

{"title":"Bibliometric analysis of the 3-year trends (2018-2021) in literature on artificial intelligence in ophthalmology and vision sciences.","authors":"Hayley Monson, Jeffrey Demaine, Adrianna Perryman, Tina Felfeli","doi":"10.1136/bmjhci-2023-100780","DOIUrl":"10.1136/bmjhci-2023-100780","url":null,"abstract":"Objectives: The objective of this analysis is to present a current view of the field of ophthalmology and vision research and artificial intelligence (AI) from topical and geographical perspectives. This will clarify the direction of the field in the future and aid clinicians in adapting to new technological developments.Methods: A comprehensive search of four different databases was conducted. Statistical and bibliometric analysis were done to characterise the literature. Softwares used included the R Studio bibliometrix package, and VOSviewer.Results: A total of 3939 articles were included in the final bibliometric analysis. Diabetic retinopathy (391, 6% of the top 100 keywords) was the most frequently occurring indexed keyword by a large margin. The highest impact literature was produced by the least populated countries and in those countries who collaborate internationally. This was confirmed via a hypothesis test where no correlation was found between gross number of published articles and average number of citations (p value=0.866, r=0.038), while graphing ratio of international collaboration against average citations produced a positive correlation (r=0.283). Majority of publications were found to be concentrated in journals specialising in vision and computer science, with this category of journals having the highest number of publications per journal (18.00 publications/journal), though they represented a small proportion of the total journals (<1%).Conclusion: This study provides a unique characterisation of the literature at the intersection of AI and ophthalmology and presents correlations between article impact and geography, in addition to summarising popular research topics.","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"31 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10910687/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139989284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Explainable machine learning for breast cancer diagnosis from mammography and ultrasound images: a systematic review 从乳房 X 射线照相术和超声波图像诊断乳腺癌的可解释机器学习：系统综述

IF 4.1

BMJ Health & Care Informatics Pub Date : 2024-02-01 DOI: 10.1136/bmjhci-2023-100954

Daraje kaba Gurmessa, Worku Jimma

{"title":"Explainable machine learning for breast cancer diagnosis from mammography and ultrasound images: a systematic review","authors":"Daraje kaba Gurmessa, Worku Jimma","doi":"10.1136/bmjhci-2023-100954","DOIUrl":"https://doi.org/10.1136/bmjhci-2023-100954","url":null,"abstract":"Background Breast cancer is the most common disease in women. Recently, explainable artificial intelligence (XAI) approaches have been dedicated to investigate breast cancer. An overwhelming study has been done on XAI for breast cancer. Therefore, this study aims to review an XAI for breast cancer diagnosis from mammography and ultrasound (US) images. We investigated how XAI methods for breast cancer diagnosis have been evaluated, the existing ethical challenges, research gaps, the XAI used and the relation between the accuracy and explainability of algorithms. Methods In this work, Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist and diagram were used. Peer-reviewed articles and conference proceedings from PubMed, IEEE Explore, ScienceDirect, Scopus and Google Scholar databases were searched. There is no stated date limit to filter the papers. The papers were searched on 19 September 2023, using various combinations of the search terms ‘breast cancer’, ‘explainable’, ‘interpretable’, ‘machine learning’, ‘artificial intelligence’ and ‘XAI’. Rayyan online platform detected duplicates, inclusion and exclusion of papers. Results This study identified 14 primary studies employing XAI for breast cancer diagnosis from mammography and US images. Out of the selected 14 studies, only 1 research evaluated humans’ confidence in using the XAI system—additionally, 92.86% of identified papers identified dataset and dataset-related issues as research gaps and future direction. The result showed that further research and evaluation are needed to determine the most effective XAI method for breast cancer. Conclusion XAI is not conceded to increase users’ and doctors’ trust in the system. For the real-world application, effective and systematic evaluation of its trustworthiness in this scenario is lacking. PROSPERO registration number CRD42023458665. Data are available upon reasonable request.","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"25 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139669953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Seamless EMR data access: Integrated governance, digital health and the OMOP-CDM 无缝的 EMR 数据访问：综合治理、数字医疗和 OMOP-CDM

IF 4.1

BMJ Health & Care Informatics Pub Date : 2024-02-01 DOI: 10.1136/bmjhci-2023-100953

Christine Mary Hallinan, Roger Ward, Graeme K Hart, Clair Sullivan, Nicole Pratt, Ashley P Ng, Daniel Capurro, Anton Van Der Vegt, Siaw-Teng Liaw, Oliver Daly, Blanca Gallego Luxan, David Bunker, Douglas Boyle

{"title":"Seamless EMR data access: Integrated governance, digital health and the OMOP-CDM","authors":"Christine Mary Hallinan, Roger Ward, Graeme K Hart, Clair Sullivan, Nicole Pratt, Ashley P Ng, Daniel Capurro, Anton Van Der Vegt, Siaw-Teng Liaw, Oliver Daly, Blanca Gallego Luxan, David Bunker, Douglas Boyle","doi":"10.1136/bmjhci-2023-100953","DOIUrl":"https://doi.org/10.1136/bmjhci-2023-100953","url":null,"abstract":"Objectives In this overview, we describe theObservational Medical Outcomes Partnership Common Data Model (OMOP-CDM), the established governance processes employed in EMR data repositories, and demonstrate how OMOP transformed data provides a lever for more efficient and secure access to electronic medical record (EMR) data by health service providers and researchers. Methods Through pseudonymisation and common data quality assessments, the OMOP-CDM provides a robust framework for converting complex EMR data into a standardised format. This allows for the creation of shared end-to-end analysis packages without the need for direct data exchange, thereby enhancing data security and privacy. By securely sharing de-identified and aggregated data and conducting analyses across multiple OMOP-converted databases, patient-level data is securely firewalled within its respective local site. Results By simplifying data management processes and governance, and through the promotion of interoperability, the OMOP-CDM supports a wide range of clinical, epidemiological, and translational research projects, as well as health service operational reporting. Discussion Adoption of the OMOP-CDM internationally and locally enables conversion of vast amounts of complex, and heterogeneous EMR data into a standardised structured data model, simplifies governance processes, and facilitates rapid repeatable cross-institution analysis through shared end-to-end analysis packages, without the sharing of data. Conclusion The adoption of the OMOP-CDM has the potential to transform health data analytics by providing a common platform for analysing EMR data across diverse healthcare settings. Data sharing not applicable as no datasets generated.","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"65 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139922526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance of large language models on advocating the management of meningitis: a comparative qualitative stud 大语言模型在脑膜炎管理宣传方面的表现：一项定性比较研究

IF 4.1

BMJ Health & Care Informatics Pub Date : 2024-02-01 DOI: 10.1136/bmjhci-2023-100978

Urs Fisch, Paulina Kliem, Pascale Grzonka, Raoul Sutter

{"title":"Performance of large language models on advocating the management of meningitis: a comparative qualitative stud","authors":"Urs Fisch, Paulina Kliem, Pascale Grzonka, Raoul Sutter","doi":"10.1136/bmjhci-2023-100978","DOIUrl":"https://doi.org/10.1136/bmjhci-2023-100978","url":null,"abstract":"Objectives We aimed to examine the adherence of large language models (LLMs) to bacterial meningitis guidelines using a hypothetical medical case, highlighting their utility and limitations in healthcare. Methods A simulated clinical scenario of a patient with bacterial meningitis secondary to mastoiditis was presented in three independent sessions to seven publicly accessible LLMs (Bard, Bing, Claude-2, GTP-3.5, GTP-4, Llama, PaLM). Responses were evaluated for adherence to good clinical practice and two international meningitis guidelines. Results A central nervous system infection was identified in 90% of LLM sessions. All recommended imaging, while 81% suggested lumbar puncture. Blood cultures and specific mastoiditis work-up were proposed in only 62% and 38% sessions, respectively. Only 38% of sessions provided the correct empirical antibiotic treatment, while antiviral treatment and dexamethasone were advised in 33% and 24%, respectively. Misleading statements were generated in 52%. No significant correlation was found between LLMs’ text length and performance (r=0.29, p=0.20). Among all LLMs, GTP-4 demonstrated the best performance. Discussion Latest LLMs provide valuable advice on differential diagnosis and diagnostic procedures but significantly vary in treatment-specific information for bacterial meningitis when introduced to a realistic clinical scenario. Misleading statements were common, with performance differences attributed to each LLM’s unique algorithm rather than output length. Conclusions Users must be aware of such limitations and performance variability when considering LLMs as a support tool for medical decision-making. Further research is needed to refine these models' comprehension of complex medical scenarios and their ability to provide reliable information. Data are available upon reasonable request.","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"6 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139667162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rapidly scalable and low-cost public health surveillance reporting system for COVID-19. 针对 COVID-19 的可快速扩展的低成本公共卫生监测报告系统。

IF 4.1

BMJ Health & Care Informatics Pub Date : 2024-01-18 DOI: 10.1136/bmjhci-2023-100759

Vivek Jason Jayaraj, Chiu-Wan Ng, Victor Chee-Wai Hoe, Diane Woei-Quan Chong, Sanjay Rampal

{"title":"Rapidly scalable and low-cost public health surveillance reporting system for COVID-19.","authors":"Vivek Jason Jayaraj, Chiu-Wan Ng, Victor Chee-Wai Hoe, Diane Woei-Quan Chong, Sanjay Rampal","doi":"10.1136/bmjhci-2023-100759","DOIUrl":"10.1136/bmjhci-2023-100759","url":null,"abstract":"Objective: Data-driven innovations are essential in strengthening disease control. We developed a low-cost, open-source system for robust epidemiological intelligence in response to the COVID-19 crisis, prioritising scalability, reproducibility and dynamic reporting.Methods: A five-tiered workflow of data acquisition; processing; databasing, sharing, version control; visualisation; and monitoring was used. COVID-19 data were initially collated from press releases and then transitioned to official sources.Results: Key COVID-19 indicators were tabulated and visualised, deployed using open-source hosting in October 2022. The system demonstrated high performance, handling extensive data volumes, with a 92.5% user conversion rate, evidencing its value and adaptability.Conclusion: This cost-effective, scalable solution aids health specialists and authorities in tracking disease burden, particularly in low-resource settings. Such innovations are critical in health crises like COVID-19 and adaptable to diverse health scenarios.","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"31 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11077347/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139490782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Regulating AI for health. 规范人工智能，促进健康。

IF 4.1

BMJ Health & Care Informatics Pub Date : 2023-12-21 DOI: 10.1136/bmjhci-2023-100931

Ian Oppermann

引用次数: 0

Call for the responsible artificial intelligence in the healthcare. 呼吁在医疗保健领域使用负责任的人工智能。

IF 4.1

BMJ Health & Care Informatics Pub Date : 2023-12-21 DOI: 10.1136/bmjhci-2023-100920

Umashankar Upadhyay, Anton Gradisek, Usman Iqbal, Eshita Dhar, Yu-Chuan Li, Shabbir Syed-Abdul

{"title":"Call for the responsible artificial intelligence in the healthcare.","authors":"Umashankar Upadhyay, Anton Gradisek, Usman Iqbal, Eshita Dhar, Yu-Chuan Li, Shabbir Syed-Abdul","doi":"10.1136/bmjhci-2023-100920","DOIUrl":"10.1136/bmjhci-2023-100920","url":null,"abstract":"The integration of artificial intelligence (AI) into healthcare is progressively becoming pivotal, especially with its potential to enhance patient care and operational workflows. This paper navigates through the complexities and potentials of AI in healthcare, emphasising the necessity of explainability, trustworthiness, usability, transparency and fairness in developing and implementing AI models. It underscores the 'black box' challenge, highlighting the gap between algorithmic outputs and human interpretability, and articulates the pivotal role of explainable AI in enhancing the transparency and accountability of AI applications in healthcare. The discourse extends to ethical considerations, exploring the potential biases and ethical dilemmas that may arise in AI application, with a keen focus on ensuring equitable and ethical AI use across diverse global regions. Furthermore, the paper explores the concept of responsible AI in healthcare, advocating for a balanced approach that leverages AI's capabilities for enhanced healthcare delivery and ensures ethical, transparent and accountable use of technology, particularly in clinical decision-making and patient care.","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"30 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138884376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Call to digital health leaders: test and leverage this guideline to support health information technology implementation in practice. 呼吁数字卫生领导者:测试和利用本指南，以支持卫生信息技术在实践中的实施。

IF 4.1

BMJ Health & Care Informatics Pub Date : 2023-12-02 DOI: 10.1136/bmjhci-2023-100829

Samantha Erin Harding, Karen Day, Peter Carswell

{"title":"Call to digital health leaders: test and leverage this guideline to support health information technology implementation in practice.","authors":"Samantha Erin Harding, Karen Day, Peter Carswell","doi":"10.1136/bmjhci-2023-100829","DOIUrl":"10.1136/bmjhci-2023-100829","url":null,"abstract":"Background: Health information technology (HIT) is increasingly used to enable health service/system transformation. Most HIT implementations fail to some degree; very few demonstrate sustainable success. No guidelines exist for health service leaders to leverage factors associated with success. The purpose of this paper is to present an evidence-based guideline for leaders to test and leverage in practice.Methods: This guideline was developed from a literature review and refined by a set of eight interviews with people in senior HIT roles, which were thematically analysed. It was refined in the consultancy work of the first author and confirmed after minor refinements.Results: Five key actions were identified: relationships, vision, HIT system attributes, constant evaluation and learning culture.Conclusions: This guideline presents a significant opportunity for health system leaders to systematically check relevant success factors during the implementation process of single projects and regional/national programmes.","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"30 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11340250/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138481932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ChatGPT in Iranian medical licensing examination: evaluating the diagnostic accuracy and decision-making capabilities of an AI-based model 伊朗医学执照考试中的 ChatGPT：评估基于人工智能模型的诊断准确性和决策能力

IF 4.1

BMJ Health & Care Informatics Pub Date : 2023-12-01 DOI: 10.1136/bmjhci-2023-100815

Manoochehr Ebrahimian, Behdad Behnam, Negin Ghayebi, Elham Sobhrakhshankhah

{"title":"ChatGPT in Iranian medical licensing examination: evaluating the diagnostic accuracy and decision-making capabilities of an AI-based model","authors":"Manoochehr Ebrahimian, Behdad Behnam, Negin Ghayebi, Elham Sobhrakhshankhah","doi":"10.1136/bmjhci-2023-100815","DOIUrl":"https://doi.org/10.1136/bmjhci-2023-100815","url":null,"abstract":"Introduction Large language models such as ChatGPT have gained popularity for their ability to generate comprehensive responses to human queries. In the field of medicine, ChatGPT has shown promise in applications ranging from diagnostics to decision-making. However, its performance in medical examinations and its comparison to random guessing have not been extensively studied. Methods This study aimed to evaluate the performance of ChatGPT in the preinternship examination, a comprehensive medical assessment for students in Iran. The examination consisted of 200 multiple-choice questions categorised into basic science evaluation, diagnosis and decision-making. GPT-4 was used, and the questions were translated to English. A statistical analysis was conducted to assess the performance of ChatGPT and also compare it with a random test group. Results The results showed that ChatGPT performed exceptionally well, with 68.5% of the questions answered correctly, significantly surpassing the pass mark of 45%. It exhibited superior performance in decision-making and successfully passed all specialties. Comparing ChatGPT to the random test group, ChatGPT’s performance was significantly higher, demonstrating its ability to provide more accurate responses and reasoning. Conclusion This study highlights the potential of ChatGPT in medical licensing examinations and its advantage over random guessing. However, it is important to note that ChatGPT still falls short of human physicians in terms of diagnostic accuracy and decision-making capabilities. Caution should be exercised when using ChatGPT, and its results should be verified by human experts to ensure patient safety and avoid potential errors in the medical field. Data are available on reasonable request.","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"102 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138576006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0