Artificial Intelligence and Radiology: Have Rumors of the Radiologist's Demise Been Greatly Exaggerated?

Nawrocki, Tomer; Maldjian, Pierre D.; Slasky, Shira E.; Contractor, Sohail

doi:10.1016/j.acra.2017.12.027

Cited by 21 publications

(24 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Artificial intelligence (AI) technology has existed for more than half a century and has become more and more sophisticated [11]. The first reports on AI use in radiology date back to 1963, but the flames of those years quickly dozed off [12].…”

Section: Artificial Intelligencementioning

confidence: 99%

Artificial Intelligence: Is It Armageddon for Breast Radiologists?

Chiwome

Okojie

Rahman

et al. 2020

Cureus

View full text Add to dashboard Cite

Artificial Intelligence (AI) has taken radiology by storm, in particular, mammogram interpretation, and we have seen a recent surge in the number of publications on potential uses of AI in breast radiology. Breast cancer exerts a lot of burden on the National Health Service (NHS) and is the second most common cancer in the UK as of 2018. New cases of breast cancer have been on the rise in the past decade, while the survival rate has been improving. The NHS breast cancer screening program led to an improvement in survival rate. The expansion of the screening program led to more mammograms, thereby putting more work on the hands of radiologists, and the issue of double reading further worsens the workload. The introduction of computeraided detection (CAD) systems to help radiologists was found not to have the expected outcome of improving the performance of readers. Unreliability of CAD systems has led to the explosion of studies and development of applications with the potential use in breast imaging. The purported success recorded with the use of machine learning in breast radiology has led to people postulating ideas that AI will replace breast radiologists. Of course, AI has many applications and potential uses in radiology, but will it replace radiologists? We reviewed many articles on the use of AI in breast radiology to give future radiologists and radiologists full information on this topic. This article focuses on explaining the basic principles and terminology of AI in radiology, potential uses, and limitations of AI in radiology. We have also analysed articles and answered the question of whether AI will replace radiologists.

show abstract

Section: Artificial Intelligencementioning

confidence: 99%

Artificial Intelligence: Is It Armageddon for Breast Radiologists?

Chiwome

Okojie

Rahman

et al. 2020

Cureus

View full text Add to dashboard Cite

show abstract

“…The presented framework of machine learning and its role within intelligent agents is still on a conceptual level. However, given the misunderstandings and ambiguity of the two terms [6][7][8][9], we see potential for further research with the aim both to clarify the terminology and to map uncharted territory for machine-learning enabled artificial intelligence.…”

Section: Research Priorities For Machinelearning-enabled Artificial Imentioning

confidence: 99%

“…Researchers would typically describe such tasks of identifying specific instances within social media platforms as classification tasks within the field of (supervised) machine learning [2]- [4]. However, with rising popularity of artificial intelligence (AI) [5], the term AI is often used interchangeably with machine learning-not only by Facebook's CEO in the example above or in other interviews [6], but also across various theoretical and application-oriented contributions in recent literature [7]- [9]. Carner (2017) even states that he still uses AI as a synonym for machine learning although knowing this is not correct [10].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in Artificial Intelligence: Towards a Common Understanding

Kühl¹,

Goutier²,

Hirt³

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

The application of "machine learning" and "artificial intelligence" has become popular within the last decade. Both terms are frequently used in science and media, sometimes interchangeably, sometimes with different meanings. In this work, we aim to clarify the relationship between these terms and, in particular, to specify the contribution of machine learning to artificial intelligence. We review relevant literature and present a conceptual framework which clarifies the role of machine learning to build (artificial) intelligent agents. Hence, we seek to provide more terminological clarity and a starting point for (interdisciplinary) discussions and future research.

show abstract

“…The real question is, however, what exactly will it do and how much of what a healthcare provider does will be done by AI-will it really take away jobs or even eliminate certain specialties like radiology? [27][28][29] Views are mixed. An interesting study by Goldberg et al 30 examined one of the most popular social media platforms, Twitter, to gauge the public's perceptions about AI.…”

Section: Ai and Computer-aided Detection/diagnosismentioning

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

Artificial Intelligence and Radiology: Have Rumors of the Radiologist's Demise Been Greatly Exaggerated?

Cited by 21 publications

References 30 publications

Artificial Intelligence: Is It Armageddon for Breast Radiologists?

Artificial Intelligence: Is It Armageddon for Breast Radiologists?

Machine Learning in Artificial Intelligence: Towards a Common Understanding

Artificial Intelligence: Lessons Learned from Radiology

Contact Info

Product

Resources

About