Human Bytes

Implementing AI

Our learnings and views on AI in healthcare

We have extensive experience with implementing AI, and can support with the most commonly asked questions and concerns, whether solutions are deployed on-prem or cloud. 

We offer regulatory approved Software-as-a-Medical Device (SaMD). All our solutions are backed by strong scientific evidence and in clinical use in multiple countries, supported by strong value cases for patient outcomes and health economics. 

The solutions are standard software, hence using standard data formats and integrates as part of existing work flows. That makes implementation of AI a relatively simple task, especially when deployed via cloud. We have successfully completed end-2-end large-scale installations within a few weeks delivering real-world value to patients, HCPs and health systems. 

Training, on-boarding and change management is actually quite simple, since our AI solutions integrate into existing workflows or by operating in the background, hence not making significant changes to current ways of working.  

Often, procurement and implementation of AI is made too complicated, and resources and costs are heavily over-estimated. Here are some of the myths, barriers and misunderstandings we often hear.  

Myths, barriers and misunderstandings
”AI is developing outside control. AI is using local patient data to learn from”

AI software is a static clinical software developed with AI but not learning from any local data. This means, that the AI software does not change, and any result can be reproduced and quality controlled. AI software is a regulatory approved medical device, like an MRI scanner and a patient monitor, or any other medical device or software used in healthcare.

”Will AI take responsibility for a wrong decision?”

AI software has a normal product liability responsibility, as all other medical devices. AI software is most often decision support for the clinical staff, but autonomous AI software is now seen approved as well. Here AI as has responsibility for the final result or score.

”AI software must be validated locally for each hospital”

For any medical device, also clinical AI software, there is an intended use that defines what the solution can be used for, or not used for, and on whom. As example, most AI solutions would in their intended use have a definition for which type of patients can be analyzed (e.g. pediatrics), or which type of equipment and exams (e.g. scanners or protocols)

“AI needs to be as good as our experts to give us value”

AI is often compared to the gold standard, e.g., the best radiologist or most difficult patient case. However, AI should be compared to representative clinical data and the actual use case. We see that AI can support in handling or triaging normal or routine cases freeing up time for the clinical staff.

Proven technology with a clear value case

AI is standard software, regulatory approved as a Medical Device. Our solutions are vendor agnostic and use standard data exchange format to ensure fast and seamless integration within scanners, devices, PACS, LIS and other clinical systems. Too often we see IT departments thinking it is complex and a large change exercise, but in fact you should consider it more like implementing a MRi license.

 Change management and onboarding of staff, is easy and often only requires few hours of training. Reason is that most of our AI solutions are deeply integrated in your normal workflows or operate in the background and only flagging if anything not normal.

Cloud or Local On-Prem

Our solutions can be installed either as Cloud or as a local On-Prem. The benefit of the cloud is often more secure, scalable and future proof. We have significant experience within cloud for the Nordic healthcare, including the many questions and concerns about privacy and data protection. Before you decide on one or the other, then we recommend you to take an initial discussion with us.

More than 1,000 sites

Our solutions are running in more than 1,000 sites across the world, including several in the Nordics. We have a vast experience of handling large scale implementation, across multiple sites and various legacy systems. We recommend involving your IT department very early in your procurement considerations, to de-mystify the task and create an aligned project plan.

Importance of knowledge

We often come across hospitals who develops own AI solutions, especially at university hospitals, which is important to build knowledge about how AI works and the opportunities it will bring. However, very seldom these solutions can be used in clinical operation, especially not with the new Medical Device Regulative (MDR). MDR clearly says that own solutions also should be regulatory approved or get a section 5.5 exceptions. Failure to comply places clear responsibility for any patient harm with the local user and management.