Copyright © 2020 pubrica. All rights reserved 1

A Systematic Review of Artificial Intelligence in Imaging

Dr. Nancy Agens, Head,

Technical Operations, Pubrica

sales@pubrica.com

In Brief

Artificial intelligence offers a seizable

promise for medical diagnostics. Evaluation

of the diagnostic accuracy of artificial

intelligence algorithms process is comparing

it with the data of healthcare professional

records. A systematic review of imaging

techniques by artificial intelligence is useful

here to research by biomedical researchers

for their investigations. Pubrica is here to

help you with systematic review writing

services to understand the various concepts

of Artificial intelligence in imaging

techniques.

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I. INTRODUCTION

The significance of artificial intelligence has

to change daily life through its AI tools like

speech recognition, robotics, etc. Most of

the healthcare sectors achieved great success

using artificial intelligence. It is essential to

conduct a systematic study about the

artificial analytical tools for the various

biomedical researches. Many reviews state

that “the significance of artificial

intelligence will replace the medical

disciplines or create new job type for

doctors and other clinicians”.

II. MEDICAL IMAGING- A

SYSTEMATIC REVIEW

Diagnostic information for human using

medical imaging is one of the most valuable

sources in the healthcare field. Data

interpretation is facing more challenges.

Despite hurdles, the diagnostic tool of

medical imaging need is increasing as the

available specialists cannot perform such

complicated tasks, especially in

underdeveloped and developing countries.

The diagnosis through AI using automated

machines will understand deep learning that

will be able to solve the problem. In recent

years deep learning models exceed the

human performance creates excitement

among the people. However, there are many

critical challenges raised against this new

technology. A systematic review writing

about artificial intelligence and machine

learning is essential to come up with a better

conclusion about this new emerging

technology.

III. FDA- ARTIFICIAL IMAGING

Conducting a systematic review in the US

Food and Drug Administration states that

the systematic study of the body using an AI

tool is harmless and gives rapid results with

Copyright © 2020 pubrica. All rights reserved 2

30 AI algorithms. The government of the US

implement the usage of artificial intelligence

in medical sectors. All the medicos and

clinicians are allowed to study the

instrumentation of synthetic intelligence.

IV. EVIDENCE FOR THIS REVIEW

They are deep learning through AI that

promises in improving the accuracy and

speed of diagnosis for patients through

medical imaging. Public interest in artificial

intelligence is growing every day and

driving the market forces in diagnostic

technologies. Many studies developed or

validated AI for the diagnostic feature of

any diseases without any restrictions in

language. These studies recognise a change

in model systems by creating deep learning

approaches, results in accurate algorithms

using artificial intelligence when compared

to humans. No other systematic review is

comparing performances of Artificial

intelligence and machine learning with the

other medical professionals. Many disease-

specific systematic reviews are here using

machine learning technologies with reported

algorithms.

V. ADDITIONAL BENEFITS OF THIS

REVIEW

The first systematic review is comparing the

diagnostic accuracy of all artificial

intelligence tools and machine learning

models against professional clinicians using

medical imaging published upto date. Very

few studies provide direct comparisons

between deep learning and clinical

professionals validation histories. The

machine learning validation is more

accurate. As per the meta analysis of deep

learning techniques and health care

professional analysis, clinicians can process

many new algorithms, and external proofs

are also possible. These sets a pathway to

external validations in all predictive models.

Both healthcare and deep learning

algorithms overestimates internal

guarantees.

VI. FUTURE IMPLICATIONS

The methodologies and process of studies

are always incomplete in deep learning

techniques. The level of diagnostic accuracy

Copyright © 2020 pubrica. All rights reserved 2

can be faster in future. FDA will introduce

New international standards of protocols in

future and implement new learning methods.

The source of data interpretation will be

better in future, and writing a systematic

literature review helps to understand the

concepts of deep learning techniques.

VII. CONCLUSION

In this systematic review writing services

under the guidance of Pubrica, the current

state of diagnostic performance using

artificial intelligence in comparison with the

healthcare professionals considering the

daily issues faced by the world in medical

sectors are studied. A meta analysis of

artificial intelligence and deep learning tools

will help us to know more about the future

improvisions in medical fields.

REFERENCES

1. Liu, X., Faes, L., Kale, A. U., Wagner, S. K., Fu, D.

J., Bruynseels, A., ...& Ledsam, J. R. (2019). A

comparison of deep learning performance against

healthcare professionals in detecting diseases from

medical imaging: a systematic review and meta-

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2. Langerhuizen, D. W., Janssen, S. J., Mallee, W. H.,

van den Bekerom, M. P., Ring, D., Kerkhoffs, G.

M., ... & Doornberg, J. N. (2019). What are the

applications and limitations of artificial intelligence

for fracture detection and classification in

orthopaedic trauma imaging? A systematic

review. Clinical Orthopaedics and Related

Research®, 477(11), 2482-2491.

3. Yao, A. D., Cheng, D. L., Pan, I., & Kitamura, F.

(2020). Deep Learning in Neuroradiology: A

Systematic Review of Current Algorithms and

Approaches for the New Wave of Imaging

Technology. Radiology: Artificial Intelligence, 2(2),

e190026.

4. Sollini, M., Antunovic, L., Chiti, A., &Kirienko, M.

(2019). Towards clinical application of image

mining: a systematic review of artificial intelligence

and radionics. European journal of nuclear medicine

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