Web3 Journal: ML in Health Science最新文献

Critical Review on the Contribution of Machine Learning to Health Science 关于机器学习对健康科学贡献的重要评论

Web3 Journal: ML in Health Science Pub Date : 2024-05-20 DOI: 10.62487/qgpcnt08

Neji Hasni

{"title":"Critical Review on the Contribution of Machine Learning to Health Science","authors":"Neji Hasni","doi":"10.62487/qgpcnt08","DOIUrl":"https://doi.org/10.62487/qgpcnt08","url":null,"abstract":"Background: The field of machine learning in health science is evolving exponentially, with a focus on accelerating scientific discoveries, improving holistic well-being, and advancing personalized healthcare. Aim: In this same spirit, this critical review article aims to provide a comprehensive understanding of the role, challenges, opportunities, and ethical considerations of integrating machine learning into health science, with an emphasis on healthcare research and practice. Methods: To base its critiques on previous literature, the elucidative survey considered specific criteria, such as the significance and contribution of each source to the field, methodology or approach, and argument, as well as the use of evidence. Results: The study results indicate that machine learning holds great promise to improve evidence-based health science, but significant work is needed to ensure the technology is developed and deployed in a way that is trustworthy and ethical. Conclusion: In conclusion, the literature review presents a balanced assessment of the strengths, weaknesses, and notable features of the current state of machine learning in health science. The key takeaway point is that while machine learning has demonstrated significant potential to improve health science outcomes and strategic management, there are still important challenges, limitations, and research gaps that need to be addressed to facilitate widespread adoption and trust in these technologies.","PeriodicalId":518288,"journal":{"name":"Web3 Journal: ML in Health Science","volume":"80 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141121117","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

Smartphone Camera for Angiographic Computer Vision in Vascular Medicine 用于血管医学血管造影计算机视觉的智能手机摄像头

Web3 Journal: ML in Health Science Pub Date : 2024-05-03 DOI: 10.62487/82grqt38

Y. Rusinovich, V. Rusinovich, Markus Doss

{"title":"Smartphone Camera for Angiographic Computer Vision in Vascular Medicine","authors":"Y. Rusinovich, V. Rusinovich, Markus Doss","doi":"10.62487/82grqt38","DOIUrl":"https://doi.org/10.62487/82grqt38","url":null,"abstract":"Aim: This study aimed to develop a TensorFlow Lite algorithm for angiography classification and to deploy it on a basic mobile smartphone device, thereby verifying the proof of concept for creating a comprehensive end-to-end mobile computer vision application for vascular medicine. Materials and Methods: After ethical approval by the local ethics committee, we collected institutional and open source peripheral angiograms of lower limbs. The angiograms were labeled by a researcher with more than 10 years of experience in vascular surgery. The labeling included dividing the angiograms according to their anatomical pattern into the Global Limb Anatomic Staging System (GLASS). The model was developed using the open-source TensorFlow framework for general image classification and deployed as an Android application. Results: The model utilized 700 angiograms, distributed as follows within the femoropoliteal GLASS disease (fp) categories: fp0 – 187 images, fp1 – 136 images, fp2 – 128 images, fp3 – 97 images, fp4 – 152 images. The reference dataset included 372 non-angiographic images (not_angio). Consequently, the entire model included 1,072 images. After training and deployment, the model demonstrated the following performance: a mean accuracy of 0.72. The best self-reported accuracy per class was for fp0 0.72, fp4 0.83 and not_angio 1.0 classes. Conclusion: We discovered that a smartphone camera could be utilized for angiographic computer vision through end-to-end applications accessible to every healthcare professional. However, the predictive abilities of the model are limited and require improvement. The development of a robust angiographic computer vision smartphone application should incorporate an upload function, undergo validation through head-to-head human-machine comparisons, potentially include segmentation, and feature a prospective design with explicit consent for using collected data in the development of AI models.","PeriodicalId":518288,"journal":{"name":"Web3 Journal: ML in Health Science","volume":"104 S5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141016203","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

Xavatar: A Web3 Metaverse Application as a support for Patients with Mobility Disorders Xavatar：作为行动障碍患者辅助工具的 Web3 Metaverse 应用程序

Web3 Journal: ML in Health Science Pub Date : 2024-04-24 DOI: 10.62487/gq9wh015

Jason P. Rothberg, Colin Keogh, Y. Rusinovich

引用次数: 0

Human Categorization with “Dirty” Confounders in AI and ML Medical Models: The Role of Religion 人工智能和 ML 医疗模型中的 "肮脏 "混杂因素的人类分类：宗教的作用

Web3 Journal: ML in Health Science Pub Date : 2024-02-13 DOI: 10.62487/2rm68r13

Y. Rusinovich, V. Rusinovich

{"title":"Human Categorization with “Dirty” Confounders in AI and ML Medical Models: The Role of Religion","authors":"Y. Rusinovich, V. Rusinovich","doi":"10.62487/2rm68r13","DOIUrl":"https://doi.org/10.62487/2rm68r13","url":null,"abstract":"Aim: This study was conducted to evaluate the acceptance among healthcare practitioners and scientific researchers of the current official regulatory recommendations regarding the incorporation of human categorization through confounders, such as “Religion”, into AI and ML-based clinical research and healthcare settings. Materials and Methods: An anonymous online survey was conducted using the Telegram platform, where participants were asked a single question: \"Do you consider the inclusion of Religious status in Artificial Intelligence and Machine Learning models justified from the perspective of medical ethics and science?\" Respondents were provided with only two response options: \"Yes\" or \"No.\" This survey was specifically targeted at international groups, focusing primarily on English and Russian-speaking clinicians and scientific researchers. Results: 134 unique individuals participated in the survey. The results revealed that two-third of the respondents (87 individuals) agreed that including Religion status as predictor in the ML and AI models is inappropriate. Conclusion: Two-thirds of healthcare practitioners and scientific researchers agree that categorizing individuals within healthcare settings based on their religion is inappropriate. Educational programs are needed to inform healthcare and scientific professionals that AI and ML applications should be built on unbiased and ethically appropriate predictors. ML is incapable of distinguishing individual human characteristics. Therefore, constructing healthcare AI and ML models based on confounders like religion is unlikely to aid in identifying the cause of or treating any pathology or disease. Moreover, the high conflict potential of this predictor may further deepen societal disparities.","PeriodicalId":518288,"journal":{"name":"Web3 Journal: ML in Health Science","volume":"190 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140527732","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

AI and ML: The Hype Theme in Mass Media 人工智能和 ML：大众媒体的炒作主题

Web3 Journal: ML in Health Science Pub Date : 2024-01-19 DOI: 10.62487/z7qn2134

Y. Rusinovich

引用次数: 0

AI and ML in Healthcare Settings: Is it Really a Breakthrough? 医疗环境中的人工智能和 ML：这真的是一项突破吗？

Web3 Journal: ML in Health Science Pub Date : 2024-01-17 DOI: 10.62487/bcpb3133

Y. Rusinovich

引用次数: 0

Why "ML in Health Science" 为什么选择 "健康科学中的 ML

Web3 Journal: ML in Health Science Pub Date : 2024-01-01 DOI: 10.62487/e4ccm968

Y. Rusinovich

引用次数: 0