Would Artificial Intelligence Reduce the Shortage of the Radiologists

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Table of Contents

Introduction
Why is This Research Needed?
Findings from the Background Literature Review
Conclusion
References

Introduction

Artificial intelligence (AI) is a discipline of computer science that uses various technological techniques to create computers that do activities that would typically need human intellect. The term artificial intelligence (AI) refers to computer systems that simulate cognitive capabilities such as learning and problem-solving. The general interest in artificial intelligence (AI) technologies is increasing at a rapid pace. Machine-learning devices have multiplied in medicine, particularly for image processing, heralding new substantial difficulties for the usability of AI in healthcare. This naturally presents a slew of legal and ethical concerns.

As founders of the digital world in healthcare, Radiologists may now welcome AI as a new partner in their profession, along with the possibility for radiology to play a more significant role in healthcare, as demonstrated in a previous article. Nevertheless, there are obstacles to AI use in medicine, particularly in radiology, that are the responsibility of regulatory bodies and legislatures rather than physicians.

The fast advancement of Artificial Intelligence technology and its incorporation into regular medical imaging will have a substantial impact on radiology treatment. The positioning strategy will ensure that doctors successfully move into their new positions as enhanced clinicians. Scarce or non-existent radiography capabilities restrict resource-constrained health organizations’ use of artificial intelligence (AI) for computed tomography. They encounter constraints in terms of local equipment, people knowledge, infrastructure, data innovation, and government rules. The credibility of AI for treatment decisions in health promotion and reduced contexts is impeded by insufficient data variety, opaque AI algorithms, and the restricted engagement of commodity health organizations in AI generation and validation.

Over the last few decades, doctors’ activity has expanded significantly. This is due mainly to an increased frequency of cross-sectional imaging studies, improved image processing difficulty owing to the collection of more enormous databases, and falling imaging payments. The latter requires radiological clinics to boost efficiency to sustain levels of income while restricting their financial options for hiring additional employees. As a result, the total workload per radiologist has grown significantly in recent years. Not unexpectedly, burnout is acknowledged as a growing issue among radiologists. Occupational stress may potentially jeopardize the delivery of safe and effective care that radiologists can give.

AI has enormous potential to improve precision and effectiveness in radiology and has inherent flaws and biases. The widespread application of AI-based intelligent and autonomous systems in radiology raises the danger of systemic mistakes with severe consequences and presents complicated ethical and social challenges. There is currently minimal experience employing AI for patient care in a variety of clinical contexts. An extensive study is required to determine how to best use AI in clinical settings.

Meanwhile, some anticipate that artificial intelligence (AI) will speed up scan times, generate an accurate diagnosis, and reduce radiologists burden. Although there is no evidence to support the claim that AI would reduce effort, it can already significantly influence political and strategic choices. Based on this hypothesis, authorities may indeed decide not to raise, or perhaps limit, the number of citizens who may participate in radiological training courses, limit financial capacity for hiring new radiologists, and further reduce payments for imaging systems.

Why is This Research Needed?

In the 1900s efforts to establish radiography as a specialized field, the theoretical part of capturing analog X-ray images, transferring, and producing pictures on fragile glass plates for later interpretation, needed medically qualified doctors and technicians. As a result the number of radiologists and radiographers available today can not cope with the number of exams required for patients, and we need a new strategy to accommodate AI and the shortage of Radiology staff.

Then revolution in digitalization allowed for the collection of massive amounts of fully digital independent images. A second revolution which is the Internet of Things (IoT) provides high-speed internet enabling equipment to be connected across the globe, and big data become even bigger. And now a third revolution is the technological innovation of AI in radiology.

Findings from the Background Literature Review

Supportive evidence findings from the literature review have shown the main reasons for the shortage of radiology staff to be inadequate remuneration, no privacy, not enough education, increasing aging population, complex funding challenges, reduced reimbursements, increased volume of work, advanced technology, 22% of UK Consultant Radiologist will retire in the next five years, alarm to add to the current shortage.

Supportive evidence findings from the literature review have shown a significant advancement of AI in Radiology. Today, we can use AI support systems for efficient appointments, worklists, standardized image protocols, optimized image acquisition, and dose reduction. Also, AI applications can automatically achieve accurate volume measurements of lesions and brain structures for MS, Alzheimer’s dementia, and trauma accidents. All these AI application advancements will lead to achieving the most excellent efficiency of staff and equipment in radiology.

Supportive evidence findings from the literature review have shown the main obstacles to introducing AI in radiology are the lack of competence of radiology staff, non-recognition of this technology, and incorrect diagnosing results. Also, Cybersecurity might lead to ethical issues because we are using the cloud to send images to AI servers.

Conclusion

Supportive evidence findings from the literature review have shown implementing AI in radiology will have a significant impact, such as incorporating radiology staff in the design and development of AI solutions, reducing errors and bias, increasing accuracy in diagnosing images, improving quality, 90% accuracy rate of diagnosing Neurological diseases by AI application.

References

Kwee, T. C., & Kwee, R. M. (2021). ‘Workload of diagnostic radiologists in the foreseeable future based on recent scientific advances: growth expectations and role of artificial intelligence’. Insights into imaging, 12(1), pp. 1-12. Web.

Mollura, D. J., Culp, M. P., Pollack, E., Battino, G., Scheel, J. R., Mango, V. L., & Dako, F. (2020). ‘Artificial intelligence in low-and middle-income countries: innovating global health radiology’. Radiology, 297(3), pp. 513-520.

Liew, C. (2018). ‘The future of radiology augmented with artificial intelligence: a strategy for success’. European journal of radiology, 102, pp. 152-156.

Pesapane, F., Volonté, C., Codari, M., & Sardanelli, F. (2018). ‘Artificial intelligence as a medical device in radiology: ethical and regulatory issues in Europe and the United States’. Insights into imaging, 9(5), pp. 745-753. Web.

Rimmer, A., (2017). Radiologist shortage leaves patient care at risk, warns royal college. BMJ; BMJ, [e-journal] 359, pp.j4683. Web.

Waymel, Q., Badr, S., Demondion, X., Cotten, A., & Jacques, T. (2019). ‘Impact of the rise of artificial intelligence in radiology: what do radiologists think?’. Diagnostic and Interventional Imaging, 100(6), pp. 327-336.

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Would Artificial Intelligence Reduce the Shortage of the Radiologists

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