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Philips and Ibex Expand Partnership to Enhance AI-Enabled Pathology Workflows

By LabMedica International staff writers
Posted on 25 Mar 2025

Royal Philips (Amsterdam, The Netherlands) has expanded its partnership with Ibex Medical Analytics (Tel Aviv, Israel) and released the new Philips IntelliSite Pathology Solution (PIPS) to further accelerate the adoption of AI-enabled digital pathology to address the pressure pathologists face because of the global shortage of pathologists and the growing number of cancer patients. Following the start of the partnership in 2021, Philips and Ibex can offer even further interoperability of its solutions, aiming to enhance diagnostic workflows for clinicians and enabling better care for more people.

Ibex's AI and Philips’ digital pathology solution have shown to improve reporting efficiency and drive productivity gains of up to 37%. Through this partnership, pathology laboratories are able to utilize PIPS, which includes the full portfolio of scanners and Image Management System, and Ibex’s platform of AI-powered tools for prostate, breast and gastric cancer diagnostics. Expanding access and streamlining the use of AI-powered tools in digital pathology will further enhance the efficiency and effectiveness of pathology labs. Enabling AI directly in digital pathology workflows empowers clinicians to generate objective, reproducible results, increasing diagnostic confidence and enabling the productivity improvements needed to address the growing demand for pathology-based diagnostics. By further integrating Ibex's AI with PIPS, the solutions can offer optimized workflows, including help with case prioritization, AI-powered cancer detection and reporting, providing easier integration of productivity enhancing tools for more confident diagnosis, supporting better patient care.

Philips is also introducing Philips IntelliSite Pathology 6.0, including several new features and AI capabilities into the system. By providing advanced analysis and insights based on the context of the scanned images, pathology teams can access a seamless, AI-powered clinical workflow solution, from case management through to diagnosis and reporting. PIPS 6.0 features improved workflows and enhancements including full scan mode for the pathology SG scanner and contextual launch of AI [2] and counter marker tool in the image management system, as well as improved scalability. AI focused worklists show AI processing status at a case level and slides can automatically be shared with Ibex AI applications, for processing right after digitization. This new version of the Philips digital pathology solution offers optimized workflows, designed to enhance the efficiency and improved patient care. Based on Philips’ collaboration with Amazon Web Services, offerings for cloud archival services will allow pathology labs to efficiently and securely store, manage, and analyze growing volumes of digital pathology data.

“Pathology provides key information for a precise diagnosis. The Philips and Ibex solutions are especially beneficial to the diagnosis of cancer cases because they aid our pathologists in making an accurate and quick diagnosis,” said Sam Terese, President & CEO of Alverno Laboratories. “These integrated pathology solutions help us provide the highest possible quality result that any lab can deliver, getting answers back to our clinicians quickly and saving patients sleepless nights, even on complex cases.”

“The enhanced interoperability between Philips and Ibex platforms, which are now more seamlessly integrated into our diagnostic workflows, will accelerate the adoption of augmented pathology tools in which we strongly believe,” said Stéphane Rossat, Innovation & Scientific Director and Project Management at Medipath. “PIPS 6.0 promises to facilitate adoption by our physicians, thanks to its streamlined navigation and optimized workflows. We are eager to contribute to the evolution toward true digital "cockpits," which will become the reference interface for our pathologists.”

Related Links:
Royal Philips
Ibex Medical Analytics

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