Steps to Use Artificial Intelligence in Imaging -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.

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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