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.
{"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}
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.
{"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}
Xavatar is a media, educational, and therapeutic platform specializing in immersive virtual reality (VR) and augmented reality (AR) content, as well as seamless interconnectivity across various devices such as mobiles, tablets, and computers. It is aimed at improving the lives of patients with chronic mobility and communication disorders, including dementia, Alzheimer's, autism, chronic immobility, isolation, and long-term hospitalization. The project represents a fusion of digital technologies including the Metaverse, artificial intelligence (AI), and Web3, all designed to enhance healthcare interactions and patient support. This opinion piece explores the transformative potential of Xavatar, highlighting its role in shaping future healthcare landscapes through innovative, empathetic, and engaging digital solutions.
{"title":"Xavatar: A Web3 Metaverse Application as a support for Patients with Mobility Disorders","authors":"Jason P. Rothberg, Colin Keogh, Y. Rusinovich","doi":"10.62487/gq9wh015","DOIUrl":"https://doi.org/10.62487/gq9wh015","url":null,"abstract":"Xavatar is a media, educational, and therapeutic platform specializing in immersive virtual reality (VR) and augmented reality (AR) content, as well as seamless interconnectivity across various devices such as mobiles, tablets, and computers. It is aimed at improving the lives of patients with chronic mobility and communication disorders, including dementia, Alzheimer's, autism, chronic immobility, isolation, and long-term hospitalization. The project represents a fusion of digital technologies including the Metaverse, artificial intelligence (AI), and Web3, all designed to enhance healthcare interactions and patient support. This opinion piece explores the transformative potential of Xavatar, highlighting its role in shaping future healthcare landscapes through innovative, empathetic, and engaging digital solutions.","PeriodicalId":518288,"journal":{"name":"Web3 Journal: ML in Health Science","volume":"52 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140664433","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}
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.
目的：本研究旨在评估医疗保健从业人员和科学研究人员对当前官方监管建议的接受程度，这些建议涉及通过 "宗教 "等混杂因素将人类分类纳入基于人工智能和 ML 的临床研究和医疗保健环境。材料与方法：使用 Telegram 平台进行匿名在线调查，向参与者提出一个问题："从医学伦理和科学的角度来看，您认为将宗教信仰纳入人工智能和机器学习模型是否合理？受访者只有两个回答选项：是 "或 "否"。这项调查专门针对国际群体，主要侧重于讲英语和俄语的临床医生和科学研究人员。调查结果共有 134 人参与了调查。结果显示，三分之二的受访者（87 人）同意将宗教状况作为预测因素纳入 ML 和 AI 模型是不恰当的。结论三分之二的医疗从业人员和科学研究人员都认为，根据宗教信仰对医疗机构中的个人进行分类是不恰当的。有必要开展教育计划，告知医疗保健和科研专业人员，人工智能和 ML 应用应建立在无偏见且符合道德规范的预测指标基础上。人工智能无法区分人类的个体特征。因此，基于宗教等混杂因素构建医疗人工智能和 ML 模型不太可能有助于确定病因或治疗任何病理或疾病。此外，这种预测因素的高冲突可能性可能会进一步加深社会差异。
{"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}
This report examines the disproportionate media focus on AI and ML investments compared to humanitarian and environmental issues. It questions the ethical implications of this trend and the trustworthiness of mass media in balancing technological advancements with human-centric concerns.
本报告探讨了与人道主义和环境问题相比，媒体对人工智能和 ML 投资的过度关注。报告质疑了这一趋势的道德影响，以及大众传媒在平衡技术进步与以人为本方面的可信度。
{"title":"AI and ML: The Hype Theme in Mass Media","authors":"Y. Rusinovich","doi":"10.62487/z7qn2134","DOIUrl":"https://doi.org/10.62487/z7qn2134","url":null,"abstract":"This report examines the disproportionate media focus on AI and ML investments compared to humanitarian and environmental issues. It questions the ethical implications of this trend and the trustworthiness of mass media in balancing technological advancements with human-centric concerns.","PeriodicalId":518288,"journal":{"name":"Web3 Journal: ML in Health Science","volume":"458 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531234","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}
Only 200 out of 300,000 AI/ML-related original publications on PubMed focus on human-centered settings. Why is human wellbeing often overlooked in medical AI research?
{"title":"AI and ML in Healthcare Settings: Is it Really a Breakthrough?","authors":"Y. Rusinovich","doi":"10.62487/bcpb3133","DOIUrl":"https://doi.org/10.62487/bcpb3133","url":null,"abstract":"Only 200 out of 300,000 AI/ML-related original publications on PubMed focus on human-centered settings. Why is human wellbeing often overlooked in medical AI research? ","PeriodicalId":518288,"journal":{"name":"Web3 Journal: ML in Health Science","volume":"187 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531144","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}
This is the first editorial of the journal, discussing the balance between humans and AI in healthcare and emphasizing the need for a human-centric approach in AI and ML applications.
这是该期刊的第一篇社论，讨论了医疗保健中人类与人工智能之间的平衡问题，并强调了在人工智能和 ML 应用中需要以人为本的方法。
{"title":"Why \"ML in Health Science\"","authors":"Y. Rusinovich","doi":"10.62487/e4ccm968","DOIUrl":"https://doi.org/10.62487/e4ccm968","url":null,"abstract":"This is the first editorial of the journal, discussing the balance between humans and AI in healthcare and emphasizing the need for a human-centric approach in AI and ML applications.","PeriodicalId":518288,"journal":{"name":"Web3 Journal: ML in Health Science","volume":"19 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140532891","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}
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