Reset
Showing 20 of 286 academic articles

Machine Learning and AI for Risk Management

Saqib Aziz, Michael Dowling • Palgrave studies in digital business & enabling technologies (2018)

We explore how machine learning and artificial intelligence (AI) solutions are transforming risk management. A non-technical overview is first given of the main machine learning and AI techniques of benefit to risk managementRisk management...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1007/978-3-030-02330-0_3

Machine Learning in Healthcare Communication

Sarkar Siddique, James C. L. Chow • Encyclopedia (2021)

Machine learning (ML) is a study of computer algorithms for automation through experience. ML is a subset of artificial intelligence (AI) that develops computer systems, which are able to perform tasks generally having need of human intelli...

OPENALEX cc-by DOI: https://doi.org/10.3390/encyclopedia1010021

Machine learning in cardiovascular medicine: are we there yet?

Khader Shameer, Kipp W. Johnson, Benjamin Scott Glicksberg, Joel T. Dudley et al. • Heart (2018)

Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encomp...

OPENALEX DOI: https://doi.org/10.1136/heartjnl-2017-311198

Machine Learning in Healthcare

Hafsa Habehh, Suril Gohel • Current Genomics (2021)

Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) technology have brought on substantial strides in predicting and identifying health emergencies, disease populations, and disease state and immune response, among...

OPENALEX Open Access DOI: https://doi.org/10.2174/1389202922666210705124359 PMC: 8822225

Machine learning and medical education

Vijaya B. Kolachalama, Priya S. Garg • npj Digital Medicine (2018)

Artificial intelligence (AI) driven by machine learning (ML) algorithms is a branch in computer science that is rapidly gaining popularity within the healthcare sector. Recent regulatory approvals of AI-driven companion diagnostics and othe...

OPENALEX cc-by DOI: https://doi.org/10.1038/s41746-018-0061-1 PMC: 6550167

Advancing Biosensors with Machine Learning

Feiyun Cui, Yun Yue, Yi Zhang, Ziming Zhang et al. • ACS Sensors (2020)

Chemometrics play a critical role in biosensors-based detection, analysis, and diagnosis. Nowadays, as a branch of artificial intelligence (AI), machine learning (ML) have achieved impressive advances. However, novel advanced ML methods, es...

OPENALEX DOI: https://doi.org/10.1021/acssensors.0c01424

Causality for Machine Learning

Bernhard Schölkopf • ACM eBooks (2022)

Graphical causal inference as pioneered by Judea Pearl arose from research on artificial intelligence (AI), and for a long time had little connection to the field of machine learning. This article discusses where links have been and should ...

OPENALEX Open Access DOI: https://doi.org/10.1145/3501714.3501755

Strategy in the Age of Artificial Intelligence

Kareem Ayoub, Kenneth Payne • Journal of Strategic Studies (2015)

We argue that Artificial Intelligence (AI) will, in the very near future, have a profound impact on the conduct of strategy and will be disruptive of existing power balances. To do so, we review the psychological foundations of strategy and...

OPENALEX DOI: https://doi.org/10.1080/01402390.2015.1088838

Defining artificial intelligence for librarians

Andrew Martin Cox, Suvodeep Mazumdar A • Journal of Librarianship and Information Science (2022)

The aim of the paper is to define Artificial Intelligence (AI) for librarians by examining general definitions of AI, analysing the umbrella of technologies that make up AI, defining types of use case by area of library operation, and then ...

OPENALEX cc-by DOI: https://doi.org/10.1177/09610006221142029

Resistance to Medical Artificial Intelligence

Chiara Longoni, Andrea Bonezzi, Carey K. Morewedge • Journal of Consumer Research (2019)

Abstract Artificial intelligence (AI) is revolutionizing healthcare, but little is known about consumer receptivity to AI in medicine. Consumers are reluctant to utilize healthcare provided by AI in real and hypothetical choices, separate a...

OPENALEX Open Access DOI: https://doi.org/10.1093/jcr/ucz013

Neurosurgery and artificial intelligence

Mohammad Mofatteh • AIMS neuroscience (2021)

Neurosurgeons receive extensive and lengthy training to equip themselves with various technical skills, and neurosurgery require a great deal of pre-, intra- and postoperative clinical data collection, decision making, care and recovery. Th...

OPENALEX cc-by DOI: https://doi.org/10.3934/neuroscience.2021025 PMC: 8611194

The role of artificial intelligence in achieving the Sustainable Development Goals

Ricardo Vinuesa, Hossein Azizpour, Iolanda Leite, Madeline Balaam et al. • Nature Communications (2020)

The emergence of artificial intelligence (AI) and its progressively wider impact on many sectors requires an assessment of its effect on the achievement of the Sustainable Development Goals. Using a consensus-based expert elicitation proces...

OPENALEX cc-by DOI: https://doi.org/10.1038/s41467-019-14108-y PMC: 6957485

Artificial intelligence in orthopaedic surgery

Anthony B. Lisacek-Kiosoglous, Amber S. Powling, Andreas Fontalis, Ayman Gabr et al. • Bone and Joint Research (2023)

The use of artificial intelligence (AI) is rapidly growing across many domains, of which the medical field is no exception. AI is an umbrella term defining the practical application of algorithms to generate useful output, without the need ...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1302/2046-3758.127.bjr-2023-0111.r1 PMC: 10329876

Artificial intelligence

Gillian Pocock, M Hugh • Oxford University Press eBooks (1993)

This chapter explores the extensive potential of artificial intelligence (AI), which has been recognized and harnessed by computer scientists, including physics and life sciences. It analyses how AI is applied to those areas of chemistry th...

OPENALEX DOI: https://doi.org/10.1093/hesc/9780198557364.003.0001