Shamie Kumar describes how AI fits into a radiology clinical workflow and her perspective on how clinical radiographers could use this to learn from and enhance their skills.

AI in radiology and workflow 

We all know that AI is already here, actively being implemented and used in many trusts in seeing its real world value supporting radiology departments to solve current challenges.Often this is focused on benefits to radiologist, clinicians, reporting radiographers, patients, and cost savings, but what about clinical non-reporting radiographers undertaking the X-ray or scans – can AI benefit them too?Let’s think about how AI is implemented and where are the AI outputs displayed?

If the AI findings are seen in PACS, how many radiographers actually log into PACS after taking a scan or X-ray? Good practice is seen to have PACS open to cross-check images that have been sent from the modality. Often this doesn’t happen for various reasons but maybe it should be a part of the radiographers’ routine practice, just like post-documentation is.

Can Radiographers Up-Skill?

Given the view it does happen, radiographers will have the opportunity to look at the AI outputs and potentially take away learnings on whether the AI found something that they didn’t see initially or whether there was a very subtle finding. We all know people learn through experience, exposure, and repetition, so if the AI is consistently picking up true findings, then the radiographer can learn from it too.

But what about when AI is incorrect – could it fool a radiographer, or will it empower them to research and understand the error in more detail?

As with many things in life, nothing is 100% and this includes AI in terms of false positive and false negatives. The radiographers have the opportunity to research erroneous findings in more detail to enhance their learning, but do they actually have time to undertake additional learning and steps to interpret AI?

CPD, self-reflection, learning through clinical practice are all key aspects of maintaining your registration, and self-motivation is often key to furthering yourself and your career. The question remains: are radiographers engaged and self-motivated to be part of the AI revolution and use it to their professional benefit with potential learnings at their fingertips?

There have been a few recent publications that share insight on how AI is perceived by radiographers, what is their understanding, training and educational needs.

Many Universities like City University London and AI companies like Qure.ai are taking the initial steps in understanding this better and taking active efforts in filling the knowledge gap, training and understanding of AI in radiology.

Radiographers who are key part of any radiology pathway, are yet to see the real-world evidence on whether AI can upskill radiographers, but there is no doubt this will unfold with time.

About Shamie Kumar

Shamie Kumar is a practicing HCPC Diagnostic Radiographer; graduated from City University London with a BSc Honors in Diagnostic Radiography in 2009 and is a part of Society of Radiographers with over 12 years of clinical knowledge and skills within all aspects of radiography. She studied further in leadership, management, and counselling with a keen interest in artificial intelligence in radiology.