Important topics as perceived by development versus integration focused employees. In the next step, there will be a closer look at the importance the two groups lay on the

individual drivers and what they associate with them. To compare their opinions based on the experiences they made in the field, some broad topics that dominated the interviews have been chosen. The view they take on the different main questions is summarized in Table 2 below.

Table 2

Different Perceptions of Integration vs Development Department

4.2.2.1 Future outlook. From the table above, it can be seen that the two groups identify the core challenge of the adaption of medical imaging software differently. One similarity that they share is that they both expect AI-CAD to be applied more often in the close future and that there will be a redefinition of tasks that a radiologist or an algorithm is responsible for. People on the

integration side have a slightly more reserved attitude toward the importance within the radiologist’s workflow than people from the integration side. One interesting comment has been made by the medical applications engineer who works for a PACS company. P1 suggests that a possible solution in the years to come would be that people who are not trained radiologists could take over some simple tasks from radiologists to free up capacities: “Maybe even tasks that are being done by radiologists now can be done by technicians for example using AI, because the tasks are also getting simpler in that kind of way. The simple tasks are being done then by the technicians and the radiologist will then need to make the diagnosis obviously”.

In the next category, the researcher aimed to retrace the challenges perceived by each party for themselves, but also of the other group. By doing so, one could compare if they identify the same core challenges faced by the product development as well as the product integration.

4.2.2.2 Challenges for the development. Considering the product development first, employees on the integration versus the development side identify slightly different core challenges. The employees focused on the product development indicate that the main issue they see for the product development is not the improvement of their product per se, but rather the acceptance and consequently the adoption by patients. When it comes to the algorithm performance, they do see room for improvement moving ahead, but the cause of low adoption rates is not insufficient product performance. P3 claims: “Technology is becoming ready and now it’s really a human thing”. They rather see further improvement of the algorithm (mainly through the improvement of training data) as an opportunity moving forward to increase applicability of different patient cases and to enable autonomous decision making for all. One interviewee highlights that different ethnic groups run different risks of developing cancer throughout their

lives and their chest might be anatomically different. For this reason, they underline the importance of accurate representment of such cases in the training datasets: “So in the long term we will really have to make sure to remove all these bias as much as possible. And this will of course be a very important step before more autonomous decisions [algorithms are possible]. If we say okay, we have 100% accuracy but it is not applicable on all women but only to some ethnicities, then there is a real problem in terms of applicability”. P3 also mentions the option of combining different information sources in one algorithmic diagnosis to optimize accuracy: “The question behind is what do we want to do, because you can add many different parts. You can combine different types of exams, like ultra sound, MRI to get even better performances”.

On the other hand, the biggest challenge for the product development as perceived by people from integration side are privacy regulations that are in place as well as certain functionalities that enable easy integration of the product in the workflow.

4.2.2.3 Challenges for the integration. Concerning the integration, the software developers see less of a concern. P3 states “Also, going back to integration, as it is a web page, we are very easy to integrate in practice, which is a big advantage”. Interviewee P4 mentions that due to mammography solutions being the most established ones in the market, processes are already standardized and if implemented correctly by everyone, integration should not be a big problem:

“[…] basically in mammography, because of CAD that has been the first radiology application where AI was used years ago, the standard is already there. It’s basically people implementing and making use of this and of course, some are specific things that are more happening. Especially in 3D [tomosynthesis], those things are kind of enhanced navigation through the slides for the standardization, that are just implementations of these features that are needed”. An opportunity

developers see is the integration of other systems to improve diagnoses even further in the future.

P3 says “You can combine different types of exams, like ultra sound, MRI to get even better performances”. All these statements suggest that integration is not seen as a big hurdle or concern from the development side, but rather as an opportunity.

The comments made by people focused on integration have been quite opposing to this.

One big challenge they see is the integration of different vendors of PACS systems or software solutions. One possible solution to this could be more standardization in the process according to P2: “I think one of the most challenging parts is the different integrations that we have. For instance, there are a lot of different PACS systems that we use. Each PACS system will require Company X to do the integration in a specific way. So there is not really a guideline or a golden standard. The only standard that is there is the DICOM standard. So each system will use DICOM but all PACS systems want something else or cannot handle, for example, structured reports or are not able to fetch the score from the study so they cannot prioritize worklists”.

The same respondent also indicates that AI vendors are limited in their functionalities when they integrate with a PACS vendor, as the software can only provide solutions that are supported by the PACS system. For instance, if the PACS vendor does not have an integrated viewer for mammograms, then the image solutions from the AI vendor cannot be displayed through the PACS. As a possible solution to this, the employee mentioned that their company is planning to develop viewers for their solutions that work without the support of the PACS. However, they are also aware that this comes at a trade-off for user friendliness: “For a radiologist it’s a nightmare.

Having different viewers for everything, dedicated viewers for everything – it is a real nightmare.

When it is completely integrated – some viewers can be completely integrated in their PACS – then the radiologist won’t really mind. But normally that is not the case” (P2). An alternative

solution for this issue the interviewee mentions are platforms that handle the communication between the PACS and the algorithm: “[…] it’s really a platform that will be offered to a customer with any PACS system and the platform will handle all the communication with the AI algorithm.

Meaning that when a customer wants a certain AI algorithm, the platform will be installed, the PACS will only communicate with that platform and not with the algorithm. So there will be a solution in between handling all the exotic stuff and making sure that the integration in the customer PACS is working”. However, they also see another restriction to this solution, as several platforms can again cause friction. P2 says “Of course that can also create fragmentation again.

When in the Netherlands a certain platform is used and in Germany two others, that makes it hard for vendors to be included in all these platforms, I think. But I think that’s one of the developments that will come in the upcoming months, years”.

4.2.2.3 Acceptance of stakeholders. Opposing to what theory suggests, the respondents of both parties believe that the acceptance of the product by radiologists in general and the threat of job replacement in specific is no major concern for radiologists. As P1 phrases it: “I think the general perception of AI is quite good and they see that they [the radiologists] can improve diagnoses and help the patients. That’s what you’re doing it for, right?”. The difference between employees focused on product development and those focused on product integration is that the former see a bigger issue with patient acceptance. As mentioned earlier, they believe that different human factors are the main hurdle for AI-CAD usage. While there might be other drivers, they see this as the main obstacle to face.

In summary, the main issues spotted by the integration side is the seamless integration of the software in the hospital IT and ultimately in the clinician’s workflow. They see the issue of

low standardization and user friendliness of the product in the foreground and are very aware of the interdependence between software vendors, PACS vendors (which provide IT infrastructure for clinics) and hospitals. On the other hand, the core issue as identified by software vendors are product acceptance by people along the product chain. They believe that the core hurdle to autonomous software is the acceptance of patients. They do not see major constraints on the side of algorithm performance or the practical implementation of the product at the client.

5 Discussion