The future of radiology augmented with Artificial Intelligence: A strategy for success

Liew, Charlene

doi:10.1016/j.ejrad.2018.03.019

Cited by 197 publications

(139 citation statements)

References 20 publications

Supporting

Mentioning

120

Contrasting

Unclassified

Order By: Relevance

“…What is required to make it successful (i.e., impact patient care and outcomes) is to carefully develop strategies that are proactive in terms of how we control and manage patient data that are used to develop and then implement AI tools (i.e., data are a commodity but how do we make the selling of data palatable to industry, healthcare enterprises, and patients), how and in what circumstances we integrate AI into the clinical workflow, how we educate and train users, how we deal with the still emerging medicolegal issues surrounding AI, and even how we assess the impact of AI on the ultimate users-clinicians. 43,44…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence: Lessons Learned from Radiology

Krupiński

2019

Healthcare Transformation

View full text Add to dashboard Cite

Artificial intelligence (AI) will continue to increase its significant impact on the everyday acquisition, interpretation, and application of data in our healthcare system. It is difficult to predict whether and to what extent AI will change the health status of patients, but it certainly will change the way decisions are made, how healthcare is delivered, and the ways providers, patients, and healthcare enterprises interact with and use data from an increasing array of sources. Radiology has often been at the forefront of technological change in healthcare and the AI revolution is no different. The lessons radiology has learned to date, its successes and challenges, can help provide guidance for other specialties, especially since radiology interacts with and cross-cuts nearly every other specialty and touches nearly every patient at least once in his or her lifetime.

show abstract

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence: Lessons Learned from Radiology

Krupiński

2019

Healthcare Transformation

View full text Add to dashboard Cite

show abstract

“…Advances in diagnostic imaging modalities have increased in terms of complexity and volume of generated digital data. These factors led to the creation of a new approach to imaging diagnosis called radiomics . It consists of algorithms that decompose input images into basic features that may be used to classify or interpret the image, such as edges, gradients, shape, signal intensity, wavelength, and textures.…”

Section: Radiomics and DL Applications In Radiologymentioning

confidence: 99%

“…Consequently, automatic methods based on deep neural networks have been tested for several purposes, which are as follows: classification, image registration, segmentation, lesion detection, image retrieval, image guided therapy, image generation, and enhancement . Most recently, radiomics and AI research have been advancing in the dental field, revealing the potential of these technologies to substantially improve clinical care …”

Section: Radiomics and DL Applications In Radiologymentioning

confidence: 99%

Radiomics and Machine Learning in Oral Healthcare

Leite

Vasconcelos

Willems

et al. 2020

Proteomics Clinical Apps

View full text Add to dashboard Cite

The increasing storage of information, data, and forms of knowledge has led to the development of new technologies that can help to accomplish complex tasks in different areas, such as in dentistry. In this context, the role of computational methods, such as radiomics and Artificial Intelligence (AI) applications, has been progressing remarkably for dentomaxillofacial radiology (DMFR). These tools bring new perspectives for diagnosis, classification, and prediction of oral diseases, treatment planning, and for the evaluation and prediction of outcomes, minimizing the possibilities of human errors. A comprehensive review of the state-of-the-art of using radiomics and machine learning (ML) for imaging in oral healthcare is presented in this paper. Although the number of published studies is still relatively low, the preliminary results are very promising and in a near future, an augmented dentomaxillofacial radiology (ADMFR) will combine the use of radiomics-based and AI-based analyses with the radiologist's evaluation. In addition to the opportunities and possibilities, some challenges and limitations have also been discussed for further investigations.

show abstract

“…As for stable and continuous data, deep learning can perform its powerful functions to the greatest extent. Whereas, if the data is sporadic or the quality is low, the hybridization of human and computer can offer better accuracy 27,28 .…”

Section: Ai In Radiologymentioning

confidence: 99%

Insight of Artificial Intelligence Application in Healthcare

Kuang¹

2019

ijSciences

View full text Add to dashboard Cite

Ever since "Deep Blue" defeated the human champion chess player in 1997, artificial intelligence has caused extensive attention and discussion. With the development of computing and data storage technologies, artificial intelligence has been made great progress and applied in a wide range of fields. Here, we focus on several creative and novel applications of artificial intelligence in the healthcare field and also discuss their future trends.

show abstract

The future of radiology augmented with Artificial Intelligence: A strategy for success

Cited by 197 publications

References 20 publications

Artificial Intelligence: Lessons Learned from Radiology

Artificial Intelligence: Lessons Learned from Radiology

Radiomics and Machine Learning in Oral Healthcare

Insight of Artificial Intelligence Application in Healthcare

Contact Info

Product

Resources

About