Australian health review : a publication of the Australian Hospital Association
Dec 1, 2022
Objective This scoping review maps the approach undertaken by nurses to influence the implementation of artificial intelligence in health care. It also provides evidence of how frequently nurses drive the implementation of artificial intelligence, an...
Journal of the American Medical Informatics Association : JAMIA
Nov 14, 2022
The lack of diversity, equity, and inclusion continues to hamper the artificial intelligence (AI) field and is especially problematic for healthcare applications. In this article, we expand on the need for diversity, equity, and inclusion, specifical...
In the late 2010s, artificial intelligence (AI) technologies became complementary to the research areas of food science and nutrition. This review aims to summarize these technological advances by systematically describing the following: the use of A...
Studies in health technology and informatics
Nov 3, 2022
The explosion of interest in exploiting machine learning techniques in healthcare has brought the issue of inferring causation from observational data to centre stage. In our work in supporting the health decisions of the individual person/patient-as...
Studies in health technology and informatics
Nov 3, 2022
The advancement of healthcare towards P5 medicine requires communication and cooperation between all actors and institutions involved. Interoperability must go beyond integrating data from different sources and include the understanding of the meanin...
Compendium of continuing education in dentistry (Jamesburg, N.J. : 1995)
Nov 1, 2022
Artificial intelligence (AI) and Augmented Intelligence (AuI) have existed for decades but have only recently been applied to dentistry and incorporated into higher education for dental professionals. Early examples of the incorporation of AI into de...
BACKGROUND: Artificial intelligence (AI) platforms are increasingly being utilized in various healthcare applications. There are few platforms that provide quantifiable assessments of dermatologic or aesthetic conditions by employing industry establi...
OBJECTIVES: Few machine learning (ML) models are successfully deployed in clinical practice. One of the common pitfalls across the field is inappropriate problem formulation: designing ML to fit the data rather than to address a real-world clinical p...
Radiation oncology is a field that heavily relies on new technology. Data science and artificial intelligence will have an important role in the entire radiotherapy workflow. A new paradigm of routine healthcare data reuse to automate treatments and ...
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