scholarly journals Unlocking the Power of Artificial Intelligence and Big Data in Medicine

10.2196/16607 ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. e16607 ◽  
Author(s):  
Christian Lovis
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Decision Support ◽  
Data Science ◽  
Global Environment ◽  
Data Driven ◽  
Cross Linking ◽  
Science And Society ◽  
Slowing Down

Data-driven science and its corollaries in machine learning and the wider field of artificial intelligence have the potential to drive important changes in medicine. However, medicine is not a science like any other: It is deeply and tightly bound with a large and wide network of legal, ethical, regulatory, economical, and societal dependencies. As a consequence, the scientific and technological progresses in handling information and its further processing and cross-linking for decision support and predictive systems must be accompanied by parallel changes in the global environment, with numerous stakeholders, including citizen and society. What can be seen at the first glance as a barrier and a mechanism slowing down the progression of data science must, however, be considered an important asset. Only global adoption can transform the potential of big data and artificial intelligence into an effective breakthroughs in handling health and medicine. This requires science and society, scientists and citizens, to progress together.

scholarly journals Unlocking the Power of Artificial Intelligence and Big Data in Medicine (Preprint)

2019 ◽  
Author(s):  
Christian Lovis
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Decision Support ◽  
Data Science ◽  
Global Environment ◽  
Data Driven ◽  
Cross Linking ◽  
Science And Society ◽  
Slowing Down

UNSTRUCTURED Data-driven science and its corollaries in machine learning and the wider field of artificial intelligence have the potential to drive important changes in medicine. However, medicine is not a science like any other: It is deeply and tightly bound, with a large and wide network of legal, ethical, regulatory, economical, and societal dependencies. As a consequence, the scientific and technological progresses in handling information and its further processing and cross-linking for decision support and predictive systems must be accompanied by parallel changes in the global environment, with numerous stakeholders, including citizen and society. What can be seen at the first glance as a barrier and mechanism slowing down the progression of data science must, however, be considered an important asset. Only global adoption can transform the potential of big data and artificial intelligence into an effective breakthroughs in handling health and medicine. This requires science and society, scientists and citizens, to progress together.

scholarly journals Autonomous Collision Avoidance at Sea: A Survey

2021 ◽  
Vol 8 ◽  
Author(s):  
Hans-Christoph Burmeister ◽  
Manfred Constapel
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Support ◽  
Collision Avoidance ◽  
Data Science ◽  
Autonomous Systems ◽  
Data Driven ◽  
Comprehensive Overview ◽  
Planning Methods ◽  
Survey Results

In this survey, results from an investigation on collision avoidance and path planning methods developed in recent research are provided. In particular, existing methods based on Artificial Intelligence, data-driven methods based on Machine Learning, and other Data Science approaches are investigated to provide a comprehensive overview of maritime collision avoidance techniques applicable to Maritime Autonomous Surface Ships. Relevant aspects of those methods and approaches are summarized and put into suitable perspectives. As autonomous systems are expected to operate alongside or in place of conventionally manned vessels, they must comply with the COLREGs for robust decision-support/-making. Thus, the survey specifically covers how COLREGs are addressed by the investigated methods and approaches. A conclusion regarding their utilization in industrial implementations is drawn.

Data Science: Big Data, Machine Learning, and Artificial Intelligence

2018 ◽  
Vol 15 (3) ◽  
pp. 497-498 ◽  
Author(s):  
Ruth C. Carlos ◽  
Charles E. Kahn ◽  
Safwan Halabi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Data Science

scholarly journals The Artificial Intelligence (AI) during COVID-19 Pandemic

2021 ◽  
Vol 1 (1) ◽  
pp. 3-5
Author(s):  
Binta Suleman Abdullahi ◽  
Faisal Muhammad
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Data Science ◽  
Health Issue ◽  
Global Public Health ◽  
Health Crisis ◽  
Public Health Crisis

The coronavirus disease 2019 (COVID-19) pandemic is the twenty-first century's first global public health crisis. Currently, a variety of AI-powered initiatives based on data science, "machine learning," or "big data," are being employed in a variety of disciplines to forecast, explain, and manage the various scenarios produced by the health issue.

scholarly journals Beyond Privacy: Protecting Data Interests in the Age of Artificial Intelligence

2021 ◽  
Vol 3 (1) ◽  
pp. 96-108
Author(s):  
Matt Bartlett
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Economic Value ◽  
Social Dimension ◽  
Data Driven ◽  
Data Control ◽  
The Social ◽  
User Data ◽  
General Data

Serious challenges are raised by the way in which technology companies like Facebook and Google harvest and process user data. Companies in the modern data economy mine troves of data with sophisticated algorithms to produce valuable behavioural predictions. These data-driven predictions provide companies with a powerful capacity to influence and manipulate users, and these risks are increasing with the explosive growth of ‘Big Data’ and artificial intelligence machine learning. This article analyses the extent to which these challenges are met by existing regimes such as Australia and New Zealand’s respective privacy acts and the European Union’s General Data Protection Regime. While these laws protect certain privacy interests, I argue that users have a broader set of interests in their data meriting protection. I explore three of these novel interests, including the social dimension of data, control and access to predictions mined from data and the economic value of data. This article shows how existing frameworks fail to recognise or protect these novel interests. In light of this failure, lawmakers urgently need to frame new legal regimes to protect against the worst excesses of the data economy.

scholarly journals Data-driven smart sustainable cities of the future: urban computing and intelligence for strategic, short-term, and joined-up planning

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Simon Elias Bibri
Keyword(s):  
Big Data ◽  
Decision Support ◽  
Data Science ◽  
Smart Cities ◽  
Data Driven ◽  
Sustainable Cities ◽  
Short Term ◽  
Science Data ◽  
Urban Computing ◽  
The Future

AbstractSustainable cities are quintessential complex systems—dynamically changing environments and developed through a multitude of individual and collective decisions from the bottom up to the top down. As such, they are full of contestations, conflicts, and contingencies that are not easily captured, steered, and predicted respectively. In short, they are characterized by wicked problems. Therefore, they are increasingly embracing and leveraging what smart cities have to offer as to big data technologies and their novel applications in a bid to effectively tackle the complexities they inherently embody and to monitor, evaluate, and improve their performance with respect to sustainability—under what has been termed “data-driven smart sustainable cities.” This paper analyzes and discusses the enabling role and innovative potential of urban computing and intelligence in the strategic, short-term, and joined-up planning of data-driven smart sustainable cities of the future. Further, it devises an innovative framework for urban intelligence and planning functions as an advanced form of decision support. This study expands on prior work done to develop a novel model for data-driven smart sustainable cities of the future. I argue that the fast-flowing torrent of urban data, coupled with its analytical power, is of crucial importance to the effective planning and efficient design of this integrated model of urbanism. This is enabled by the kind of data-driven and model-driven decision support systems associated with urban computing and intelligence. The novelty of the proposed framework lies in its essential technological and scientific components and the way in which these are coordinated and integrated given their clear synergies to enable urban intelligence and planning functions. These utilize, integrate, and harness complexity science, urban complexity theories, sustainability science, urban sustainability theories, urban science, data science, and data-intensive science in order to fashion powerful new forms of simulation models and optimization methods. These in turn generate optimal designs and solutions that improve sustainability, efficiency, resilience, equity, and life quality. This study contributes to understanding and highlighting the value of big data in regard to the planning and design of sustainable cities of the future.

The 9 Pitfalls of Data Science

2019 ◽  
Author(s):  
Gary Smith ◽  
Jay Cordes
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Critical Thinking ◽  
Real World ◽  
Data Science ◽  
Thinking Skills ◽  
Critical Thinking Skills ◽  
Scientific Rigor ◽  
Good Data

Scientific rigor and critical thinking skills are indispensable in this age of big data because machine learning and artificial intelligence are often led astray by meaningless patterns. The 9 Pitfalls of Data Science is loaded with entertaining real-world examples of both successful and misguided approaches to interpreting data, both grand successes and epic failures. Anyone can learn to distinguish between good data science and nonsense. We are confident that readers will learn how to avoid being duped by data, and make better, more informed decisions. Whether they want to be effective creators, interpreters, or users of data, they need to know the nine pitfalls of data science.

scholarly journals Blockchain Disrupting Fintech and the Banking System

Proceedings ◽  
2021 ◽  
Vol 74 (1) ◽  
pp. 24
Author(s):  
Eduard Alexandru Stoica ◽  
Daria Maria Sitea
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Financial Institutions ◽  
New World ◽  
Great Part ◽  
Banking System ◽  
Daily Activities ◽  
Blockchain Technology ◽  
Almost All

Nowadays society is profoundly changed by technology, velocity and productivity. While individuals are not yet prepared for holographic connection with banks or financial institutions, other innovative technologies have been adopted. Lately, a new world has been launched, personalized and adapted to reality. It has emerged and started to govern almost all daily activities due to the five key elements that are foundations of the technology: machine to machine (M2M), internet of things (IoT), big data, machine learning and artificial intelligence (AI). Competitive innovations are now on the market, helping with the connection between investors and borrowers—notably crowdfunding and peer-to-peer lending. Blockchain technology is now enjoying great popularity. Thus, a great part of the focus of this research paper is on Elrond. The outcomes highlight the relevance of technology in digital finance.

Impact of Big Data and Artificial Intelligence on Industry: Developing a Workforce Roadmap for a Data Driven Economy

2021 ◽  
Author(s):  
Marina Johnson ◽  
Rashmi Jain ◽  
Peggy Brennan-Tonetta ◽  
Ethne Swartz ◽  
Deborah Silver ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Data Driven
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