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.

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.
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 been installed in your computer or
workstation .
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.
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
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.
Conclusion
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.
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