Ever wondered how a RIS might handle multiple AI inputs for the same study?

Whilst at RSNA, and walking the hall with all of the AI vendors, it occurred to me that I hadn't, as yet, considered how we might manage the inevitable scenario of "what happens when a study gets processed by multiple AI engines, all looking at different (and potentially the same) pathologies. And what do we do when we get multiple inputs of AI findings that are contradicting each other for the same study?"

This is a question I put to our CIO Paul Wierzbicki, who of course has already given this consideration and built the management of multiple AI findings into the RIS Reporting Engine workflow. Paul explains how the Aptvision RIS manages this below:

"The Aptvision RIS Reporting Module is dynamically updating in real time based upon new data coming in from modalities to the connected PACS system(s). Often a study will be post processed by an AI engine and we receive a HL7 or DICOM SR update from the AI software itself, giving us a finding status which we map to a normalised scale and filterable severity status within the RIS. The actual finding details are also recorded in a separate field so the radiologist can see what the AI is suggesting it has found.

In the scenario of a single study being passed through multiple AI engines then we will receive the corresponding number of findings. If the findings are all produced via a single AI platform then potentially we will receive a single result with all of the findings or alternatively if the customer has multiple standalone AI engines then we will receive these individually and aggregate the findings.

We recognise the potential for differential findings across these AI engines and therefore have configured the workflow to prioritise the study based upon the highest scoring abnormality flag that we receive. Therefore if 3 different engines process a Chest Xray as an example and two find it normal and one finds an abnormality then the study will be flagged in the RIS as having the highest corresponding level of severity. The other findings are also displayed in the worklist for completeness."

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UKIO 2026

By Website Admin • May 25, 2026

Join Aptvision at UKIO 2026 – Stand B16/17 We are excited to announce our participation at UKIO 2026 , taking place from 8 –10 June at the ACC Liverpool . This premier event brings together professionals from imaging and oncology for a comprehensive programme of multidisciplinary sessions, hands-on workshops, and an exhibition showcasing the latest in healthcare technology. ( UKIO 2026 – UKIO ) What is new? Since our participation in UKIO 2025, Aptvision has achieved substantial growth and recognition within the healthcare sector. Notably, we have been awarded RIS providers for significant NHS regions, including the UK and Scotland. Our NHS-proven RIS is specifically engineered to address the critical challenges of NHS waiting lists by providing a secure, web-based, and demonstrably cost-effective technology infrastructure. Our robust system delivers tangible improvements in operational efficiency and provides significant benefits for patients through faster access to care , for clinicians by optimizing their workflows and providing advanced tools, and for healthcare providers through enhanced resource management and data-driven insights. Building on our success within the NHS, Aptvision has also forged strong partnerships with key stakeholders in the private healthcare sector. We have successfully deployed our comprehensive solution across major private hospital groups in both Ireland and the United Kingdom, and soon to be live in Canada, and Spain, demonstrating the adaptability and scalability of our platform to diverse healthcare environments. Discover Aptvision’s visionRIS at Stand B16/17 Experience firsthand how visionRIS can transform your radiology operations. Book a demo date and time via Google Calendar Appointment Scheduling Link Or contact us on [email protected] and during the event. We look forward to welcoming you at Stand B16/17!

Have you wondered how RIS driven reporting handles multiple AI image inputs for a single study?

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