Learning objectives• Reviewing the current status, technical aspects and challenges of implementation of Artificial Intelligence (AI) in clinical

radiology.• Justification of radiologists’ fears of AI replacing them in the diagnostic process, and illustrating how radiologists

could benefit of AI.• Optimal application of AI, and illustrating role of each party involved.

BackgroundRadiology is the frontier of medicine into technology, witnessing great, fast evolvingadvances from discovery of X-Ray to radiomics and molecular imaging. This exhibit covers (AI), Machine Learning (ML) and Deep Learning (DL) concepts.[fig 1]

Sherif M. Shalaby1, Mostafa El-Badawy2, Amir Mahmoud3, Karim AboZied4; 1Cairo, Ca/EG, 2Staffordshire/UK, 3Rochester/US, 4Dortmond/GE

Fig. 1: Simplified Structure of relationship of AI to ML and DL.

Findings and procedure details

A white paper on Artificial Intelligence in radiology, getting over the hype

The role of radiologists hasn't always been just reading andinterpreting images.[Fig. 2]

Although AI technology is meant to be broadly applicable, each modality of imaging data (radiographs, ultrasound, CT, MRI) and disease area will require development of specific strategies for optimal performance. For example, 2D CNN can’t leverage 3D information.AI algorithms can detect and identify the clinical history provided to it for some cases, but real life cases are vastly different from showcases of pre-approved state-of-the-art technologies at radiology exhibitions.Medicine has always been about treating patients by physicians, not gathering statistics by a data scientist.[Fig. 3]

Interpretation of findings on radiological imaging (normal or abnormal) requires a high level of expert knowledge, experience, and clinical judgment based on each clinical case scenario.The claims of some neural networks founders about stopping to train radiologists were proven to be inaccurate and biased[5],

and were recently nullified by themselves.The recent success of machine learning, particularly ANNs, can be attributed to advances in (CNN) architectures, wide availability of labeled data, and parallel computing hardware.

Fig. 2: Showing regular daily activities of most radiologists.

Fig. 3: Hype vs reality

Fig. 5: Cases showing optimal performance of AI adoption in practice and researchFig. 4: AI optimal practical success

Fig. 8: Using Generative Adversarial Networks

Fig. 6: Radiomics Process

Fig. 7: Simplified Structure of an Artificial Neural Network (ANN)

Fig. 11: Recent researches in Harvard Medical School found these methods could render highly accurate AI systems almost totally ineffective.

Fig. 9: ML model for stratifying the risk of cancer in pulmonary nodules detected on computed tomography (CT)

Fig. 14: It's worthy to basically debunk the hype, facing the realities

Fig. 12: Radiology Communities and faculty members

ConclusionContinuously discovering the possibilities at the verge of technology is what advances the radiology field. Unbiased approach addressing AI in radiology is required by all parties. [Fig. 14]

A Radiologist commanding AI will replace who doesn’t.

Radiologists are among the most knowledgeable, skillful doctors, they have to show more patient engagement, taking leading roles in multidisciplinary teams.

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AI optimal practical success determinants in[Fig. 4]. Cases showing optimal performance of AI adoption in practice and research [Fig. 5]

Statistics play the major component in ML algorithms. It’s safe to say ML and CNN are nodes and clusters of statistics, rather than being a solid foundation of a clinical science. Though, it’s expected AI implementation in radiology will improve quality and revolutionize radiologists’ workflows.

One of the potentials of AI that it can’t only regulate scheduling, clinical decision-support systems, findings detection, postprocessing but also could facilitate more personalized medicine (precision medicine), in which the individual variability in genes phenotyping by Radiomics. [Fig. 6]

One of the major AI challenges is relying on the availability of quality imaging data, and could be tricked by manipulating certain inputs. Perhaps the most dangerous from a medical perspective, deep learning networks in general tend to be “black boxes” which means they are difficult to explain and validate. The obvious technical vulnerabilities with lack of human-interpretable explanations for model predictions are strong evidences that AI is still far from replacing doctors; nonetheless, it can provide useful tools for skilled medical practitioners.[Fig. 7]

ML includes a broad class of computer programs improving with repetitive experience. The complex process of creating, training, and monitoring ML integrated workflow indicates that success of algorithms will require radiologist involvement and administration for many years to come, which certainly means more engagement rather than replacement.ML/DL comprise many powerful tools with potential to augment the information radiologists can extract from images, with even the ability of inter-modality processing [Fig. 8]

AI applications should respect rules of evidence-based medicine. The professional and ethical responsibility should be taken cautiously by radiologists and computer scientists, differentiating between preprint online articles, technical peer-reviewed articles(computer science, engineering journals),and peer-reviewed articles of real diagnostic performance in radiology journals. Liability, ethics and medical data ownership issues are well-known challenges in context of clinical deployment of AI tools, which will require intervention and advocacy at the level of policymakers.Physicians take ownership and responsibility of diagnoses and treatments delivered. In case of medical errors, the manufacturer and developers of (ML systems) may not be accountable, given that by definition: computers are learning and relearning based on data sets in ways unknown to their developers.It’s been proved that results of ML model can even differ according to the demographics. [Fig. 9]

Because ML applications in radiology are new and few, there are still more questions than answers regarding ethics. It’s difficult to elucidate what is happening in the multiple hidden layers of that black box.Medical imaging AI algorithms could be vulnerable to cyberattacks designed to induce errors within the model, altering results and overthrowing its accuracy. [Fig 10]

Those (adversarial attacks) employ inputs engineered to produce classification errors in deep-learning models. Recent researches in Harvard Medical School found these methods could render highly accurate AI systems almost totally ineffective.[Fig. 11]

While AI/CNN depends on building many digital layers on a radiologic slice and processing each pixel of the image according to predefined algorithms. It still can’t be a replacement to radiologist’s clinical judgment and correlation in the clinical context.

Fig. 10: ethical and legal dilemmas

AI allegedly replacing radiologists is still one of the main fears for adopting AI in the current workflow by radiologists. Though, human body is not a simple static pixels on slices of images, it’s a variable biologic data that differ greatly in each clinical context. Clinical processes for employing most of the claimed AI-based image work are a long way from being ready for daily use, with different aspects of the use cases. Being in the peak of the inflated expectations phase regarding AI and other technological advances in medical imaging, It’s not easy to unbiasedly foresee what will commence within the next few years.Though, investing in a well-trained radiologist who command both medical and technical aspects will always be much worthier and of higher value than following the hype, spending in a newer little bit more advanced AI/ ML system.Role of Radiology communities and regulating agencies:[Fig. 12]

RCR recommendation:[Fig. 13]

References

Fig. 13: Royal College of Radiologists recommendations

Abbreviations:

ACR: American College of Radiologists

AI: Artificial Intelligence

CAR: Canadian Association of Radiology

CNN: Convolutional neural networks

CT: Computed Tomography

CXR: Chest X-ray

DL: Deep learning

ESR: European Society of Radiology

ML: Machine learning

MRI: Magnetic Resonance Imaging

MSKCC: Memorial Sloane-Kettering

NHS; National Health System, UK

RCR: Royal College of Radiologists

UCSF: University of California and San Fransisco

UK: The United Kingdom