Human Experts and Artificial Intelligence: The Value of Human Input in Diagnostic Imaging

Diagnostic imaging is regarded as fundamental in the clinical work-up of patients with a suspected or confirmed COVID-19 infection. Recent progress has been made in diagnostic imaging with the integration of artificial intelligence (AI) and machine learning (ML) algorisms leading to an increase in the accuracy of exam interpretation and to the extraction of prognostic information useful in the decision-making process. Considering the ever expanding imaging data generated amid this pandemic, COVID-19 has catalyzed the rapid expansion in the application of AI to combat disease. In this context, many recent studies have explored the role of AI in each of the presumed applications for COVID-19 infection chest imaging, suggesting that implementing AI applications for chest imaging can be a great asset for fast and precise disease screening, identification and characterization. However, various biases should be overcome in the development of further ML-based algorithms to give them sufficient robustness and reproducibility for their integration into clinical practice. As a result, in this literature review, we will focus on the application of AI in chest imaging, in particular, deep learning, radiomics and advanced imaging as quantitative CT.

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Radiographers’ perspectives on the emerging integration of artificial intelligence into diagnostic imaging: The Ghana study

Journal of Medical Radiation Sciences ◽

10.1002/jmrs.460 ◽

2021 ◽

Author(s):

Benard O. Botwe ◽

William K. Antwi ◽

Samuel Arkoh ◽

Theophilus N. Akudjedu

Keyword(s):

Artificial Intelligence ◽

Diagnostic Imaging

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Artificial Intelligence: reshaping the practice of radiological sciences in the 21st century

British Journal of Radiology ◽

10.1259/bjr.20190855 ◽

2020 ◽

Vol 93 (1106) ◽

pp. 20190855 ◽

Cited By ~ 6

Author(s):

Issam El Naqa ◽

Masoom A Haider ◽

Maryellen L Giger ◽

Randall K Ten Haken

Keyword(s):

Artificial Intelligence ◽

Decision Support ◽

Diagnostic Imaging ◽

Predictive Analytics ◽

Current Status ◽

Cost Burden ◽

Computer Aided Detection ◽

Daily Lives ◽

Computer Aided ◽

Software Platforms

Advances in computing hardware and software platforms have led to the recent resurgence in artificial intelligence (AI) touching almost every aspect of our daily lives by its capability for automating complex tasks or providing superior predictive analytics. AI applications are currently spanning many diverse fields from economics to entertainment, to manufacturing, as well as medicine. Since modern AI’s inception decades ago, practitioners in radiological sciences have been pioneering its development and implementation in medicine, particularly in areas related to diagnostic imaging and therapy. In this anniversary article, we embark on a journey to reflect on the learned lessons from past AI’s chequered history. We further summarize the current status of AI in radiological sciences, highlighting, with examples, its impressive achievements and effect on re-shaping the practice of medical imaging and radiotherapy in the areas of computer-aided detection, diagnosis, prognosis, and decision support. Moving beyond the commercial hype of AI into reality, we discuss the current challenges to overcome, for AI to achieve its promised hope of providing better precision healthcare for each patient while reducing cost burden on their families and the society at large.

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Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine

Radiologia Brasileira ◽

10.1590/0100-3984.2019.0049 ◽

2019 ◽

Vol 52 (6) ◽

pp. 387-396 ◽

Cited By ~ 9

Author(s):

Marcel Koenigkam Santos ◽

José Raniery Ferreira Júnior ◽

Danilo Tadao Wada ◽

Ariane Priscilla Magalhães Tenório ◽

Marcello Henrique Nogueira Barbosa ◽

...

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Diagnostic Imaging ◽

Precision Medicine ◽

Quantitative Information ◽

Computer Aided Diagnosis ◽

Rapid Expansion ◽

Computer Aided ◽

The Impact ◽

Aided Diagnosis

Abstract The discipline of radiology and diagnostic imaging has evolved greatly in recent years. We have observed an exponential increase in the number of exams performed, subspecialization of medical fields, and increases in accuracy of the various imaging methods, making it a challenge for the radiologist to “know everything about all exams and regions”. In addition, imaging exams are no longer only qualitative and diagnostic, providing now quantitative information on disease severity, as well as identifying biomarkers of prognosis and treatment response. In view of this, computer-aided diagnosis systems have been developed with the objective of complementing diagnostic imaging and helping the therapeutic decision-making process. With the advent of artificial intelligence, “big data”, and machine learning, we are moving toward the rapid expansion of the use of these tools in daily life of physicians, making each patient unique, as well as leading radiology toward the concept of multidisciplinary approach and precision medicine. In this article, we will present the main aspects of the computational tools currently available for analysis of images and the principles of such analysis, together with the main terms and concepts involved, as well as examining the impact that the development of artificial intelligence has had on radiology and diagnostic imaging.

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