Artificial Intelligence in Diagnostic Radiology

Abstract Objectives Why is there a major gap between the promises of AI and its applications in the domain of diagnostic radiology? To answer this question, we systematically review and critically analyze the AI applications in the radiology domain. Methods We systematically analyzed these applications based on their focal modality and anatomic region as well as their stage of development, technical infrastructure, and approval. Results We identified 269 AI applications in the diagnostic radiology domain, offered by 99 companies. We show that AI applications are primarily narrow in terms of tasks, modality, and anatomic region. A majority of the available AI functionalities focus on supporting the “perception” and “reasoning” in the radiology workflow. Conclusions Thereby, we contribute by (1) offering a systematic framework for analyzing and mapping the technological developments in the diagnostic radiology domain, (2) providing empirical evidence regarding the landscape of AI applications, and (3) offering insights into the current state of AI applications. Accordingly, we discuss the potential impacts of AI applications on the radiology work and we highlight future possibilities for developing these applications. Key Points • Many AI applications are introduced to the radiology domain and their number and diversity grow very fast. • Most of the AI applications are narrow in terms of modality, body part, and pathology. • A lot of applications focus on supporting “perception” and “reasoning” tasks.

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Evaluation of radiologist’s knowledge about the Artificial Intelligence in diagnostic radiology: a survey-based study

Acta Radiologica Open ◽

10.1177/2058460120945320 ◽

2020 ◽

Vol 9 (7) ◽

pp. 205846012094532

Author(s):

Abdulrahman Tajaldeen ◽

Salem Alghamdi

Keyword(s):

Artificial Intelligence ◽

Saudi Arabia ◽

Current Practice ◽

Rapid Development ◽

Daily Basis ◽

Diagnostic Radiology ◽

Online Questionnaire ◽

Educational Activities ◽

Residency Programme ◽

Lack Of Knowledge

Background Advanced developments in diagnostic radiology have provided a rapid increase in the number of radiological investigations worldwide. Recently, Artificial Intelligence (AI) has been applied in diagnostic radiology. The purpose of developing such applications is to clinically validate and make them feasible for the current practice of diagnostic radiology, in which there is less time for diagnosis. Purpose To assess radiologists’ knowledge about AI’s role and establish a baseline to help in providing educational activities on AI in diagnostic radiology in Saudi Arabia. Material and Methods An online questionnaire was designed using QuestionPro software. The study was conducted in large hospitals located in different regions in Saudi Arabia. A total of 93 participants completed the questionnaire, of which 32 (34%) were trainee radiologists from year 1 to year 4 (R1–R4) of the residency programme, 33 (36%) were radiologists and fellows, and 28 (30%) were consultants. Results The responses to the question related to the use of AI on a daily basis illustrated that 76 (82%) of the participants were not using any AI software at all during daily interpretation of diagnostic images. Only 17 (18%) reported that they used AI software for diagnostic radiology. Conclusion There is a significant lack of knowledge about AI in our residency programme and radiology departments at hospitals. Due to the rapid development of AI and its application in diagnostic radiology, there is an urgent need to enhance awareness about its role in different diagnostic fields.

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Artificial Intelligence and Machine Learning in Radiology

Error and Uncertainty in Diagnostic Radiology ◽

10.1093/med/9780190665395.003.0006 ◽

2019 ◽

pp. 199-226

Author(s):

Michael A. Bruno

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Deep Learning ◽

Diagnostic Radiology ◽

Job Description ◽

Error Prevention ◽

Human Endeavor ◽

Key Concepts ◽

The Impact

This final chapter, which assumes no prior reader knowledge of the topic, reviews the promise of artificial intelligence (AI), especially machine learning and deep learning in radiology. We initially discuss key concepts in the field of AI and gain a broad overview of the field and its potential, as well as the impact it is having on multiple areas of human endeavor. Subsequently, we focus on current and projected aspects of AI as applied to diagnostic radiology, specifically on how AI might provide an avenue for error prevention and remediation in radiology. The possible impact of AI in changing the radiologist’s role and basic job description is also considered.

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Prime Time for Artificial Intelligence in Interventional Radiology

CardioVascular and Interventional Radiology ◽

10.1007/s00270-021-03044-4 ◽

2022 ◽

Author(s):

Jarrel Seah ◽

Tom Boeken ◽

Marc Sapoval ◽

Gerard S. Goh

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Interventional Radiology ◽

Clinical Applications ◽

Diagnostic Radiology ◽

Machine Learning Techniques ◽

Prime Time ◽

Limited Data ◽

Learning Techniques ◽

Diagnostic Capabilities

AbstractMachine learning techniques, also known as artificial intelligence (AI), is about to dramatically change workflow and diagnostic capabilities in diagnostic radiology. The interest in AI in Interventional Radiology is rapidly gathering pace. With this early interest in AI in procedural medicine, IR could lead the way to AI research and clinical applications for all interventional medical fields. This review will address an overview of machine learning, radiomics and AI in the field of interventional radiology, enumerating the possible applications of such techniques, while also describing techniques to overcome the challenge of limited data when applying these techniques in interventional radiology. Lastly, this review will address common errors in research in this field and suggest pathways for those interested in learning and becoming involved about AI.

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The impact of the introduction of artificial intelligence in radiology and its potential legal implications in the UK and Ireland

BJR|Open ◽

10.1259/bjro.20200030 ◽

2020 ◽

Vol 2 (1) ◽

pp. 20200030

Author(s):

Toni Anderson ◽

William C Torreggiani ◽

Peter L Munk ◽

Paul I Mallinson

Keyword(s):

Artificial Intelligence ◽

Computer Science ◽

Human Behaviour ◽

Diagnostic Radiology ◽

Legal Implications ◽

Computer Based ◽

The Uk ◽

The Impact ◽

Missed Diagnoses

Artificial intelligence (AI) has been defined as a branch of computer science dealing with the capability and simulation of a machine to imitate intelligent human behaviour. Diagnostic radiology, being a computer-based service, is unsurprisingly at the forefront of the discussion of the use of AI in medicine. There are however differing schools of thought regarding its use; namely, will AI eventually replace the radiologist? Or indeed will it ever be fully capable of replacing radiology as a speciality, but rather be used as an aid to the profession whereby a human’s input will always be required? Furthermore, what will the legal implications of AI in radiology mean to the profession? Who will be liable for missed diagnoses? Is it possible that the introduction of AI to radiology will in fact make the profession busier?

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Artificial Intelligence and Deep Learning in Diagnostic Radiology—Is This The Next Phase of Scientific and Technological Development?

Radiation Protection Dosimetry ◽

10.1093/rpd/ncab005 ◽

2021 ◽

Author(s):

B Michael Moores

Keyword(s):

Artificial Intelligence ◽

Deep Learning ◽

Technological Development ◽

World Market ◽

Diagnostic Radiology ◽

Imaging Data ◽

Performance Requirements ◽

Patient Protection ◽

Learning Techniques

Abstract This paper is concerned with the role of science and technology in helping to create change in society. Diagnostic radiology is an example of an activity that has undergone significant change due to such developments, which over the past 40 years have led to a huge increase in the volume of medical imaging data generated. However, these developments have by and large left the human elements of the radiological process (referrer, radiographer and radiologist) intact. Diagnostic radiology has now reached a stage whereby the volume of information generated cannot be fully utilised solely by employing human observers to form clinical opinions, a process that has not changed in over 100 years. In order to address this problem, the potential application of Artificial Intelligence (AI) in the form of Deep Learning (DL) techniques to diagnostic radiology indicates that the next technological development phase may already be underway. The paper outlines the historical development of AI techniques, including Machine Learning and DL Neural Networks and discusses how such developments may affect radiological practice over the coming decades. The ongoing growth in the world market for radiological services is potentially a significant driver for change. The application of AI and DL learning techniques will place quantification of diagnostic outcomes at the heart of performance evaluation and quality standards. The effect this might have on the optimisation process will be discussed and in particular the possible need for automation in order to meet more stringent and standardised performance requirements that might result from these developments. Changes in radiological practices would also impact upon patient protection including the associated scientific support requirements and these are discussed.

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