How To Extract Quantitative Data For Systematic Review And Meta-Analysis ? – Pubrica
A systematic review of artificial intelligence in imaging – Pubrica
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Transcript of A systematic review of artificial intelligence in imaging – Pubrica
Copyright © 2020 pubrica. All rights reserved 1
A Systematic Review of Artificial Intelligence in Imaging
Dr. Nancy Agens, Head,
Technical Operations, Pubrica
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.
Keywords: Systematic Review Writing,
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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-
analysis. The lancet digital health, 1(6), e271-e297.
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
and molecular imaging, 1-17.