Steps to Use Artificial Intelligence in Imaging : Pubrica.com

1. STEPSTO USEARTIFICIAL INTELLIGENCE IN IMAGING An Academic presentationby Dr. Nancy Agens, Head, Technical Operations, Pubrica Group: www.pubrica.com Email:sales@pubrica.com

2. Today'sDiscussion Outline ofTopics In brief Introduction Minimal or Manual Integration CompletelyAutomatic PlatformIntegration Integration by Tagging With Other Vendors Conclusion

3. InBrief 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 themankind. Artificial Intelligence is one such innovation by man that helps in fulfilling the above. 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, disruptionsin workflow, interventions needed for treatment.

4. Introduction Many diseases are detected by researcherswho publish advanced algorithms but how to get these findings to theclinic? At the outset, these algorithms should be fused into software packages which are user- friendly and an approval by a regulatory board has to beobtained. 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 intelligencein imagingstudies. Integrating Artificial Intelligence for imaging right from manual or minimal to fully integrating it is a challenge byitself.

5. Minimal or ManualIntegration In order to access the results of artificial intelligence findings by the radiologists, he has the option of installing software onto aworkstation. 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 orworkstation. It is just like visiting a website, installing the software after downloadingit. 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 thereport.

6. CompletelyAutomatic A completely automatic integration of AI is very easy and effective way ofimplementing. The results are returned back to the RIS (Radiology information system) or PACS as a report where there is no option to validate oredit. 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 finalresults. As there is no human intervention and no manual checks done by the Radiologist, showing outstanding and correct results becomes mandatory for the AIsoftware.

7. PlatformIntegration The integration of several software programs is not a very task and it is very timeconsuming. Hiring the services of companies who take it on themselves to standardize the whole process of integration is a greatoption. By managing all the algorithms of AI with their own platform and then integrating this into the hospital network makes things easier andefficient. 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 theirbusiness.

8. Integration by Tagging With OtherVendors RIS, EMR software, voice recognition software, PACS viewers, etc are some of the solutions in the workflow. 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 reportwhatsoever. 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, platformcompanies.

9. Conclusion 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 clinicalpractices. 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 eachother. 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 efficientlyalways.

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