Copyright © 2020 pubrica. All rights reserved 1

Steps to Use Artificial Intelligence in Imaging

Dr. Nancy Agens, Head,

Technical Operations, Pubrica

sales@pubrica.com

In brief

The medical fraternity is always innovating

and pushing itself into searching for tools

that would help in better diagnosis, better

treatment and a better life for the mankind.

Artificial Intelligence is one such

innovation by man that helps in fulfilling

the above. Studies show that Artificial

Intelligence has been transforming the

whole outlook of medical imaging. Even

though Radiologists can help introduce

Artificial Intelligence into the arena of

healthcare, replacing them (radiologists)

would be next to impossible as they are

required for the communication of the

diagnosis, assurance of quality care,

medical judgment, to consider the

preferences and values of the patients,

disruptions in workflow, interventions

needed for treatment. But the fact that

Artificial Intelligence is highly efficient

and helps Radiologists to cater to patient’s

needs more efficiently and help provide

better diagnosis and treatment cannot be

ignored also.

Keywords: Artificial Intelligence,

computer, integration, imaging, healthcare.

I. INTRODUCTION

Artificial Intelligence helps find

several applications like acquiring and

processing images, storing data, planning

follow-ups, and other tasks. Hence, owing

to the wide scope of applications, Artificial

Intelligence plays a very major role in the

life of a Radiologist.

However, AI has its own challenges in its

implementation in the medical field. Many

diseases are detected by researchers who

publish advanced algorithms but how to get

these findings to the clinic? At the outset,

these algorithms should be fused into

software packages which are user-friendly

and an approval by a regulatory board has to

be obtained. But the most important factor

in the implementation of these packages or

usage of these software is the integration of

workflow in a clinic (Ranschaert, Morozov,

& Algra, 2019). However, Radiologists are

not very enthusiastic in adding another

software solution to their already existing

packages and this proves to be a challenge in

the integration and implementation of

artificial intelligence in imaging studies.

To integrate AI into imaging, there

are different levels to achieve it and each

level comes with its own pros and cons.

Integrating Artificial Intelligence for

imaging right from manual or minimal to

fully integrating it is a challenge by itself.

II. MINIMAL OR MANUAL

INTEGRATION

In order to access the results of

artificial intelligence findings by the

radiologists, he has the option of installing

software onto to a workstation. But if there

is no further steps taken to integrate it, then

this acts just like another software which has

Copyright © 2020 pubrica. All rights reserved 2

been installed in your computer or

workstation . It is just like visiting a website,

installing the software after downloading it.

The results of the AI have to be either

manually entered to the reports which is

prone to human errors or kept as an

appendix at the end of the report. Definitely

not user-friendly.

III. COMPLETELY AUTOMATIC

As the name suggest, a completely

automatic integration of AI is very easy and

effective way of implementing. Here, the

results are returned back to the RIS

(Radiology information system) or PACS as

a report where there is no option to validate

or edit. This is quite user-friendly, results

are automatically displayed without much

intervention on the part of the radiologist.

This can be used where the AI results are

being used as an appendix to the radiology

report or if the results are considered as just

another input to your final results. But with

the option of editing not available, it proves

to be a downside. However, these results

can be forwarded to referring clinicians to

consider them as additional information to

aid in the process of diagnosis. As there is

no human intervention and no manual

checks done by the Radiologist, showing

outstanding and correct results becomes

mandatory for the AI software.

IV. PLATFORM INTEGRATION

The integration of several software

programs is not a very task and it is very

time consuming. Hiring the services of

companies who take it on themselves to

standardise the whole process of integration

is a great option. By managing all the

algorithms of AI with their own platform

and then integrating this into the hospital

network makes things easier and efficient.

Here, the radiologists have an option to

access different algorithms from several AI

vendors via a single platform and the

vendors can integrate the software just once

onto the hospital network and run their

business.

V. INTEGRATION BY TAGGING WITH

OTHER VENDORS

The workflow of Radiology includes

several software solutions. RIS, EMR

software, voice recognition software, PACS

viewers, etc are some of the solutions in the

workflow (Morra, Delsanto, & Correale,

2019). Once AI is completely integrated

with other solutions, then there will be more

standardization in the field of radiology.

Finally, if the AI software gets

integrated with multiple departments like

CIS or EMR, there will be error free and

instant communication from the hospital to

the referring clinicians with no inconsistency

in the final report whatsoever.

Understanding the exact

requirements for both quantitative and

qualitative analyses for the deployment of

AI in clinical practices either cloud-based or

local is essential in the usage of AI

algorithms in the setting of an actual practice

by any clinician or hospital. Every

stakeholder should ensure that the

algorithms are non-biased, accurate, safe,

and this should be done by working along

with different government agencies, IT

personnel, platform companies.

Copyright © 2020 pubrica. All rights reserved 3

VI. CONCLUSION

Even though there are plenty of

barriers to cross, the usage of Artificial

Intelligence in the field of imaging is taking

prominence by the day. The medical

imaging fraternity which includes

radiologists, institutions of academic

importance, etc should participate in the

development of standardization of Artificial

Intelligence so that there is consistency in

clinical practices. In order to create models

that monitor and validate AI algorithms in

imaging and to minimize bias, industry

developers, researchers, clinicians and

government agencies should collaborate

with each other. This will ensure that the

technology is secure, safe, effective and

efficient.

Even though radiologists find it a

little uneasy to embrace the concept of AI in

their profession, facing challenges is not

new to radiologists as since the inception of

the concept of radiology, they have been

facing challenges efficiently always.

REFERENCES

[1] Morra, L., Delsanto, S., & Correale, L. (2019). Artificial

Intelligence in Medical Imaging: From Theory to

Clinical Practice. CRC Press. Retrieved from

https://books.google.co.in/books?id=IX3ADwAAQ

BAJ

[2] Ranschaert, E. R., Morozov, S., & Algra, P. R. (2019).

Artificial Intelligence in Medical Imaging:

Opportunities, Applications and Risks. Springer

International Publishing. Retrieved from

https://books.google.co.in/books?id=ss6FDwAAQB

AJ