{"title":"“Don’t Panic” – Stephen Aylward, PhD, on AI in Medical Imaging","authors":"Sean Sylvia","doi":"10.18043/001c.120566","DOIUrl":"https://doi.org/10.18043/001c.120566","url":null,"abstract":"","PeriodicalId":39574,"journal":{"name":"North Carolina Medical Journal","volume":"84 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141657689","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 article explores the current and potential applications of AI in care management and health care administration through the experience of Acentra Health. By discussing various AI use cases, this paper highlights how AI can augment the capabilities of healthcare professionals and streamline operations. Ethical considerations, legal compliance, and the future implications of AI in the health care sector are also examined.
本文通过 Acentra Health 的经验，探讨了人工智能在护理管理和医疗保健管理方面的当前和潜在应用。通过讨论各种人工智能使用案例，本文重点介绍了人工智能如何增强医疗保健专业人员的能力并简化操作。本文还探讨了人工智能在医疗保健领域的道德考量、法律合规性和未来影响。
{"title":"Applying AI to Care Management and Claims Processing","authors":"Sean Harrison, Melissa Leigh, Daniel Hallenbeck","doi":"10.18043/001c.120569","DOIUrl":"https://doi.org/10.18043/001c.120569","url":null,"abstract":"This article explores the current and potential applications of AI in care management and health care administration through the experience of Acentra Health. By discussing various AI use cases, this paper highlights how AI can augment the capabilities of healthcare professionals and streamline operations. Ethical considerations, legal compliance, and the future implications of AI in the health care sector are also examined.","PeriodicalId":39574,"journal":{"name":"North Carolina Medical Journal","volume":"124 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141656836","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}
Ashok Krishnamurthy, J. Zègre-Hemsey, Rebecca R. Kitzmiller, Brandy Farlow
As a biomedical data scientist, when I think of the future of artificial intelligence in health care, the potential fills me with both excitement and caution. A promising area of innovation, AI can be used to assess the impact of social determinants of health on health outcomes, though more standardization is needed.
{"title":"AI and Social Determinants of Health in Health Care: A Personal Perspective","authors":"Ashok Krishnamurthy, J. Zègre-Hemsey, Rebecca R. Kitzmiller, Brandy Farlow","doi":"10.18043/001c.120568","DOIUrl":"https://doi.org/10.18043/001c.120568","url":null,"abstract":"As a biomedical data scientist, when I think of the future of artificial intelligence in health care, the potential fills me with both excitement and caution. A promising area of innovation, AI can be used to assess the impact of social determinants of health on health outcomes, though more standardization is needed.","PeriodicalId":39574,"journal":{"name":"North Carolina Medical Journal","volume":"66 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141658102","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}
Enthusiasm about the promise of artificial intelligence and machine learning in health care must be accompanied by oversight and remediation of any potential adverse effects on health equity goals that these technologies may create. We describe five equity imperatives for the use of AI/ML in health care that require attention from health care professionals, developers, and policymakers.
{"title":"Toward an “Equitable” Assimilation of Artificial Intelligence and Machine Learning into Our Health Care System","authors":"Ritu Agarwal, G. Gao","doi":"10.18043/001c.120565","DOIUrl":"https://doi.org/10.18043/001c.120565","url":null,"abstract":"Enthusiasm about the promise of artificial intelligence and machine learning in health care must be accompanied by oversight and remediation of any potential adverse effects on health equity goals that these technologies may create. We describe five equity imperatives for the use of AI/ML in health care that require attention from health care professionals, developers, and policymakers.","PeriodicalId":39574,"journal":{"name":"North Carolina Medical Journal","volume":"141 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141655986","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 article underscores the economic benefits of AI, the importance of collaborative innovation, and the need for workforce development to prepare health care professionals for an AI-enhanced future. We include guidance for strategic and ethical AI adoption while advocating for a unified approach to leveraging technology to improve patient outcomes.
{"title":"A Compass for North Carolina Health Care Workers Navigating the Adoption of Artificial Intelligence","authors":"Yvonne Mosley, Miriam Tardif-Douglin, LaPonda Edmondson","doi":"10.18043/001c.120571","DOIUrl":"https://doi.org/10.18043/001c.120571","url":null,"abstract":"This article underscores the economic benefits of AI, the importance of collaborative innovation, and the need for workforce development to prepare health care professionals for an AI-enhanced future. We include guidance for strategic and ethical AI adoption while advocating for a unified approach to leveraging technology to improve patient outcomes.","PeriodicalId":39574,"journal":{"name":"North Carolina Medical Journal","volume":"140 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141655859","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}
Soma Sengupta, Rohan Rao, Zachary Kaufman, Timothy J Stuhlmiller, Kenny K. Wong, Santosh Kesari, Mark A Shapiro, Glenn A. Kramer
The xCures platform aggregates, organizes, structures, and normalizes clinical EMR data across care sites, utilizing advanced technologies for near real-time access. The platform generates data in a format to support clinical care, accelerate research, and promote artificial intelligence/ machine learning algorithm development, highlighted by a clinical decision support algorithm for precision oncology.
{"title":"A Health Care Clinical Data Platform for Rapid Deployment of Artificial Intelligence and Machine Learning Algorithms for Cancer Care and Oncology Clinical Trials","authors":"Soma Sengupta, Rohan Rao, Zachary Kaufman, Timothy J Stuhlmiller, Kenny K. Wong, Santosh Kesari, Mark A Shapiro, Glenn A. Kramer","doi":"10.18043/001c.120572","DOIUrl":"https://doi.org/10.18043/001c.120572","url":null,"abstract":"The xCures platform aggregates, organizes, structures, and normalizes clinical EMR data across care sites, utilizing advanced technologies for near real-time access. The platform generates data in a format to support clinical care, accelerate research, and promote artificial intelligence/ machine learning algorithm development, highlighted by a clinical decision support algorithm for precision oncology.","PeriodicalId":39574,"journal":{"name":"North Carolina Medical Journal","volume":"103 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141657239","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}
A comprehensive, collective approach to navigating the challenges of bias, privacy, and ethical considerations presented by the use of artificial intelligence in health care will require robust frameworks, continuous learning, and a commitment to equity. The insights and discussions presented in this issue are a testament to the ongoing efforts in North Carolina and beyond to find a balance between innovation with responsibility, ensuring that AI can deliver on its promise to enhance outcomes.
{"title":"Artificial Intelligence in Health Care: Opportunities, Challenges, and the Road Ahead","authors":"Sean Sylvia, Junier Oliva","doi":"10.18043/001c.120561","DOIUrl":"https://doi.org/10.18043/001c.120561","url":null,"abstract":"A comprehensive, collective approach to navigating the challenges of bias, privacy, and ethical considerations presented by the use of artificial intelligence in health care will require robust frameworks, continuous learning, and a commitment to equity. The insights and discussions presented in this issue are a testament to the ongoing efforts in North Carolina and beyond to find a balance between innovation with responsibility, ensuring that AI can deliver on its promise to enhance outcomes.","PeriodicalId":39574,"journal":{"name":"North Carolina Medical Journal","volume":"131 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141656531","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}
Various decisions concerning the management, display, and diagnostic use of electronic health records (EHR) data can be automated using machine learning (ML). We describe how ML is currently applied to EHR data and how it may be applied in the near future. Both benefits and shortcomings of ML are considered.
有关电子健康记录（EHR）数据的管理、显示和诊断使用的各种决策都可以通过机器学习（ML）实现自动化。我们介绍了目前如何将 ML 应用于电子病历数据，以及在不久的将来可能如何应用。我们考虑了 ML 的优点和缺点。
{"title":"Comments on Contemporary Uses of Machine Learning for Electronic Health Records","authors":"Jordan Bryan, Didong Li","doi":"10.18043/001c.120570","DOIUrl":"https://doi.org/10.18043/001c.120570","url":null,"abstract":"Various decisions concerning the management, display, and diagnostic use of electronic health records (EHR) data can be automated using machine learning (ML). We describe how ML is currently applied to EHR data and how it may be applied in the near future. Both benefits and shortcomings of ML are considered.","PeriodicalId":39574,"journal":{"name":"North Carolina Medical Journal","volume":"122 44","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141657045","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}
{"title":"“Plan, Don’t Panic” – Adapting to AI in Health Care","authors":"Peter J. Morris","doi":"10.18043/001c.120534","DOIUrl":"https://doi.org/10.18043/001c.120534","url":null,"abstract":"","PeriodicalId":39574,"journal":{"name":"North Carolina Medical Journal","volume":"81 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141657957","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}
Machine learning models hold great promise with medical applications, but also give rise to a series of ethical challenges. In this survey we focus on training data, model interpretability and bias and the related issues tied to privacy, autonomy, and health equity.
{"title":"Machine Learning in Health Care: Ethical Considerations Tied to Privacy, Interpretability, and Bias","authors":"Thomas Hofweber, Rebecca L. Walker","doi":"10.18043/001c.120562","DOIUrl":"https://doi.org/10.18043/001c.120562","url":null,"abstract":"Machine learning models hold great promise with medical applications, but also give rise to a series of ethical challenges. In this survey we focus on training data, model interpretability and bias and the related issues tied to privacy, autonomy, and health equity.","PeriodicalId":39574,"journal":{"name":"North Carolina Medical Journal","volume":"67 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141658502","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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