scholarly journals Data for sale: trust, confidence and sharing health data with commercial companies

2021 ◽  
pp. medethics-2021-107464
Author(s):  
Mackenzie Graham
Keyword(s):  
Artificial Intelligence ◽  
Data Sharing ◽  
Health Data ◽  
Computing Power ◽  
Health Institutions

Powered by ‘big health data’ and enormous gains in computing power, artificial intelligence and related technologies are already changing the healthcare landscape. Harnessing the potential of these technologies will necessitate partnerships between health institutions and commercial companies, particularly as it relates to sharing health data. The need for commercial companies to be trustworthy users of data has been argued to be critical to the success of this endeavour. I argue that this approach is mistaken. Our interactions with commercial companies need not, and should not, be based on trust. Rather, they should be based on confidence. I begin by elucidating the differences between trust, reliability, and confidence, and argue that trust is not the appropriate attitude to adopt when it comes to sharing data with commercial companies. I argue that what we really should want is confidence in a system of data sharing. I then provide an outline of what a confidence-worthy system of data sharing with commercial companies might look like, and conclude with some remarks about the role of trust within this system.

scholarly journals Pembuatan Aplikasi Sistem Informasi Kesehatan (SIFKES) untuk Meningkatkan Integrasi Data Puskesmas di Kota Jambi Berbasis Web Service

2018 ◽  
Vol 6 (2) ◽  
pp. 150-156
Author(s):  
Lailyn Puad ◽  
Windy Adriana
Keyword(s):  
Artificial Intelligence ◽  
Quality Of Service ◽  
Web Service ◽  
Health Data ◽  
Health Centers ◽  
Classic Problem ◽  
Health Institutions ◽  
The Web

Health is one parameter that is often used as a measure of the welfare of a region. Jambi City has 20 health centers spread across 8 sub-districts and has a duty to serve the entire community of Jambi City. The classic problem that always arises in the integration of data raises a mature unsuitability to the accuracy of the patient's diagnosis. Web services are the perfect answer to use as a data centralization medium, with their superiority in terms of capacity, speed and security of the web service very apt to be implemented in the integration of crucial data such as health data. Supported by artificial intelligence applied to be used as an assistant to the role of a physician in the diagnosis process, this application is suitable for use on every line of health institutions as a step to improve the quality of service to the community.

Role of Artificial Intelligence (AI) in Surgery: Introduction, General Principles, and Potential Applications

2020 ◽  
Author(s):  
Alberto Mangano ◽  
Valentina Valle ◽  
Nicolas Dreifuss ◽  
Gabriela Aguiluz ◽  
Mario Masrur
Keyword(s):  
Artificial Intelligence ◽  
Cognitive Tasks ◽  
Future Perspectives ◽  
General Introduction ◽  
Computing Power ◽  
Interdisciplinary Field ◽  
Potential Applications ◽  
Number Of Publications ◽  
Surgical Setting

AI (Artificial intelligence) is an interdisciplinary field aimed at the development of algorithms to endow machines with the capability of executing cognitive tasks. The number of publications regarding AI and surgery has increased dramatically over the last two decades. This phenomenon can partly be explained by the exponential growth in computing power available to the largest AI training runs. AI can be classified into different sub-domains with extensive potential clinical applications in the surgical setting. AI will increasingly become a major component of clinical practice in surgery. The aim of the present Narrative Review is to give a general introduction and summarized overview of AI, as well as to present additional remarks on potential surgical applications and future perspectives in surgery.

scholarly journals On the Coronavirus (COVID-19) Outbreak and the Smart City Network: Universal Data Sharing Standards Coupled with Artificial Intelligence (AI) to Benefit Urban Health Monitoring and Management

Healthcare ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 46 ◽  
Author(s):  
Zaheer Allam ◽  
David S. Jones
Keyword(s):  
Artificial Intelligence ◽  
Data Sharing ◽  
Urban Health ◽  
Health Monitoring ◽  
Smart City ◽  
Political Influence ◽  
Health Data ◽  
The Other ◽  
City Network ◽  
Global Understanding

As the Coronavirus (COVID-19) expands its impact from China, expanding its catchment into surrounding regions and other countries, increased national and international measures are being taken to contain the outbreak. The placing of entire cities in ‘lockdown’ directly affects urban economies on a multi-lateral level, including from social and economic standpoints. This is being emphasised as the outbreak gains ground in other countries, leading towards a global health emergency, and as global collaboration is sought in numerous quarters. However, while effective protocols in regard to the sharing of health data is emphasised, urban data, on the other hand, specifically relating to urban health and safe city concepts, is still viewed from a nationalist perspective as solely benefiting a nation’s economy and its economic and political influence. This perspective paper, written one month after detection and during the outbreak, surveys the virus outbreak from an urban standpoint and advances how smart city networks should work towards enhancing standardization protocols for increased data sharing in the event of outbreaks or disasters, leading to better global understanding and management of the same.

scholarly journals Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology

2018 ◽  
Vol 69 (2) ◽  
pp. 120-135 ◽  
Author(s):  
An Tang ◽  
Roger Tam ◽  
Alexandre Cadrin-Chênevert ◽  
Will Guest ◽  
Jaron Chong ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Patient Care ◽  
Working Group ◽  
White Paper ◽  
Patient Centered ◽  
Computing Power ◽  
Implementation Phase ◽  
Early Adoption ◽  
Canadian Association

Artificial intelligence (AI) is rapidly moving from an experimental phase to an implementation phase in many fields, including medicine. The combination of improved availability of large datasets, increasing computing power, and advances in learning algorithms has created major performance breakthroughs in the development of AI applications. In the last 5 years, AI techniques known as deep learning have delivered rapidly improving performance in image recognition, caption generation, and speech recognition. Radiology, in particular, is a prime candidate for early adoption of these techniques. It is anticipated that the implementation of AI in radiology over the next decade will significantly improve the quality, value, and depth of radiology's contribution to patient care and population health, and will revolutionize radiologists' workflows. The Canadian Association of Radiologists (CAR) is the national voice of radiology committed to promoting the highest standards in patient-centered imaging, lifelong learning, and research. The CAR has created an AI working group with the mandate to discuss and deliberate on practice, policy, and patient care issues related to the introduction and implementation of AI in imaging. This white paper provides recommendations for the CAR derived from deliberations between members of the AI working group. This white paper on AI in radiology will inform CAR members and policymakers on key terminology, educational needs of members, research and development, partnerships, potential clinical applications, implementation, structure and governance, role of radiologists, and potential impact of AI on radiology in Canada.

Patient Perceptions on Data Sharing and Applying Artificial Intelligence to Health Care Data: Cross-sectional Survey (Preprint)

2020 ◽  
Author(s):  
Ravi Aggarwal ◽  
Soma Farag ◽  
Guy Martin ◽  
Hutan Ashrafian ◽  
Ara Darzi
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Data Sharing ◽  
Health Data ◽  
Patient Perceptions ◽  
Successful Implementation ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Health Care Data ◽  
Commercial Organizations

BACKGROUND Considerable research is being conducted as to how artificial intelligence (AI) can be effectively applied to health care. However, for the successful implementation of AI, large amounts of health data are required for training and testing algorithms. As such, there is a need to understand the perspectives and viewpoints of patients regarding the use of their health data in AI research. OBJECTIVE We surveyed a large sample of patients for identifying current awareness regarding health data research, and for obtaining their opinions and views on data sharing for AI research purposes, and on the use of AI technology on health care data. METHODS A cross-sectional survey with patients was conducted at a large multisite teaching hospital in the United Kingdom. Data were collected on patient and public views about sharing health data for research and the use of AI on health data. RESULTS A total of 408 participants completed the survey. The respondents had generally low levels of prior knowledge about AI. Most were comfortable with sharing health data with the National Health Service (NHS) (318/408, 77.9%) or universities (268/408, 65.7%), but far fewer with commercial organizations such as technology companies (108/408, 26.4%). The majority endorsed AI research on health care data (357/408, 87.4%) and health care imaging (353/408, 86.4%) in a university setting, provided that concerns about privacy, reidentification of anonymized health care data, and consent processes were addressed. CONCLUSIONS There were significant variations in the patient perceptions, levels of support, and understanding of health data research and AI. Greater public engagement levels and debates are necessary to ensure the acceptability of AI research and its successful integration into clinical practice in future.

scholarly journals Leveraging the trust of nurses to advance a digital agenda in Europe: a critical review of health policy literature

2021 ◽  
Vol 1 ◽  
pp. 26
Author(s):  
Paul De Raeve ◽  
Patricia M. Davidson ◽  
Franklin A. Shaffer ◽  
Eric Pol ◽  
Amit Kumar Pandey ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Health Policy ◽  
Nurse Practitioners ◽  
White Paper ◽  
Health Data ◽  
Nursing Workforce ◽  
Preventative Care ◽  
Validity And Reliability ◽  
Nursing Time

This article is a critical and integrative review of health policy literature examining artificial intelligence (AI) and its implications for healthcare systems and the frontline nursing workforce. A key focus is on co-creation as essential for the deployment and adoption of AI. Our review hinges on the European Commission’s White Paper on Artificial Intelligence from 2020, which provides a useful roadmap. The value of health data spaces and electronic health records (EHRs) is considered; and the role of advanced nurse practitioners in harnessing the potential of AI tools in their practice is articulated. Finally, this paper examines “trust” as a precondition for the successful deployment and adoption of AI in Europe. AI applications in healthcare can enhance safety and quality, and mitigate against common risks and challenges, once the necessary level of trust is achieved among all stakeholders. Such an approach can enable effective preventative care across healthcare settings, particularly community and primary care. However, the acceptance of AI tools in healthcare is dependent on the robustness, validity and reliability of data collected and donated from EHRs (Smart4Health & InteropEHRate). Nurse stakeholders have a key role to play in this regard, since trust can only be fostered through engaging frontline end-users in the co-design of EHRs and new AI tools. Nurses hold an intimate understanding of the direct benefits of such technology, such as releasing valuable nursing time for essential patient care, and empowering patients and their family members as recipients of nursing care. This article brings together insights from a unique group of stakeholders to explore the interaction between AI, the co-creation of data spaces and EHRs, and the role of the frontline nursing workforce. We identify the pre-conditions needed for successful deployment of AI and offer insights regarding the importance of co-creating the future European Health Data Space.

scholarly journals Leveraging the trust of nurses to advance a digital agenda in Europe: a critical review of health policy literature

2021 ◽  
Vol 1 ◽  
pp. 26
Author(s):  
Paul De Raeve ◽  
Patricia M. Davidson ◽  
Franklin A. Shaffer ◽  
Eric Pol ◽  
Amit Kumar Pandey ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Health Policy ◽  
Nurse Practitioners ◽  
White Paper ◽  
Health Data ◽  
Nursing Workforce ◽  
Preventative Care ◽  
Validity And Reliability ◽  
Nursing Time

This article is a critical and integrative review of health policy literature examining artificial intelligence (AI) and its implications for healthcare systems and the frontline nursing workforce. A key focus is on co-creation as essential for the deployment and adoption of AI. Our review hinges on the European Commission’s White Paper on Artificial Intelligence from 2020, which provides a useful roadmap. The value of health data spaces and electronic health records (EHRs) is considered; and the role of advanced nurse practitioners in harnessing the potential of AI tools in their practice is articulated. Finally, this paper examines “trust” as a precondition for the successful deployment and adoption of AI in Europe. AI applications in healthcare can enhance safety and quality, and mitigate against common risks and challenges, once the necessary level of trust is achieved among all stakeholders. Such an approach can enable effective preventative care across healthcare settings, particularly community and primary care. However, the acceptance of AI tools in healthcare is dependent on the robustness, validity and reliability of data collected and donated from EHRs. Nurse stakeholders have a key role to play in this regard, since trust can only be fostered through engaging frontline end-users in the co-design of EHRs and new AI tools. Nurses hold an intimate understanding of the direct benefits of such technology, such as releasing valuable nursing time for essential patient care, and empowering patients and their family members as recipients of nursing care. This article brings together insights from a unique group of stakeholders to explore the interaction between AI, the co-creation of data spaces and EHRs, and the role of the frontline nursing workforce. We identify the pre-conditions needed for successful deployment of AI and offer insights regarding the importance of co-creating the future European Health Data Space.

Management of Economics and State "Bottom"

2020 ◽  
Vol 17 (6) ◽  
pp. 76-91
Author(s):  
E. D. Solozhentsev
Keyword(s):  
Quality Of Life ◽  
Artificial Intelligence ◽  
Probabilistic Models ◽  
Human Life ◽  
Special Software ◽  
Human Quality ◽  
Relationship Of ◽  
The Relationship

The scientific problem of economics “Managing the quality of human life” is formulated on the basis of artificial intelligence, algebra of logic and logical-probabilistic calculus. Managing the quality of human life is represented by managing the processes of his treatment, training and decision making. Events in these processes and the corresponding logical variables relate to the behavior of a person, other persons and infrastructure. The processes of the quality of human life are modeled, analyzed and managed with the participation of the person himself. Scenarios and structural, logical and probabilistic models of managing the quality of human life are given. Special software for quality management is described. The relationship of human quality of life and the digital economy is examined. We consider the role of public opinion in the management of the “bottom” based on the synthesis of many studies on the management of the economics and the state. The bottom management is also feedback from the top management.

Artificial Intelligence in Logistics

2019 ◽  
Vol 12 (1) ◽  
pp. 47-60
Author(s):  
László Kota
Keyword(s):  
Artificial Intelligence ◽  
Computing Power ◽  
Self Driving Cars

The artificial intelligence undergoes an enormous development since its appearance in the fifties. The computing power has grown exponentially since then, enabling the use of artificial intelligence applications in different areas. Since then, artificial intelligence applications are not only present in the industry, but they have slowly conquered households as well. Their use in logistics is becoming more and more widespread, just think of self-driving cars and trucks. In this paper, the author attempts to summarize and present the artificial intelligence logistical applications, its development and impact on logistics.

Sign in / Sign up

Export Citation Format

Share Document