Israel is fast becoming a global center of excellence in artificial intelligence (AI) in medical imaging, says Kevin Lev from Philips. He explains how this has happened and provides an update on the company's AI-enabled offering that won the Best New Radiology Software award in the 2021 EuroMinnies.
Introduced at RSNA 2020, the Advanced Visualization Workspace - IntelliSpace Portal 12 software features a wide range of new "intelligent, automated and connected" quantitative imaging and workflow features, including the most AI enhancements of any single product in the company's portfolio, according to Lev, who is marketing director for advanced visualization and AI solutions, based in Philips' Haifa office in Israel.
In a video interview with AuntMinnieEurope.com's Philip Ward, Lev elaborates on how the award-winning software was developed and looks ahead to new products currently being worked on by the vendor. He also speaks about the return of face-to-face congresses, possibly beginning with HIMSS in August, and how the tech innovation mindset that allows AI to flourish is also contributing to the success of Israel's COVID-19 vaccination program.
Further interviews with the winners of the 2021 EuroMinnies award scheme will follow next week. Now in its third year, the scheme is an annual event recognizing excellence in radiology. Candidates are nominated by AuntMinnieEurope.com members, with winners selected by an expert panel in two rounds of voting. A full list of winners in the 2021 edition of the EuroMinnies is available on AuntMinnieEurope.com.
![A normal mammogram confirmed by three-year radiologic follow-up illustrates reader-marked regions of interest (ROIs) during (A) unaided (round 1) and (B) artificial intelligence (AI)–assisted (round 2) reading. Each colored dot represents an ROI for recall by a human reader. Readers could mark more than one ROI per case, represented by multiple dots of the same color. During AI-assisted reading, the AI system displayed three visible prompts: two with suspicion of malignancy scores of 35% (left mediolateral oblique [L MLO] and craniocaudal [L CC]) and one with a suspicion of malignancy score of 10% (right craniocaudal [R CC]), shown as polygonal overlays. Without AI, six of 10 readers (60%) marked a false-positive ROI. With AI assistance, this fell to two of 10 (20%). R MLO = right mediolateral oblique.](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/07/2026-07-14-radiology-mammogram-ai-auto-bias.H0bYO8QlWs.jpg?auto=format%2Ccompress&fit=crop&h=100&q=70&w=100)
![A normal mammogram confirmed by three-year radiologic follow-up illustrates reader-marked regions of interest (ROIs) during (A) unaided (round 1) and (B) artificial intelligence (AI)–assisted (round 2) reading. Each colored dot represents an ROI for recall by a human reader. Readers could mark more than one ROI per case, represented by multiple dots of the same color. During AI-assisted reading, the AI system displayed three visible prompts: two with suspicion of malignancy scores of 35% (left mediolateral oblique [L MLO] and craniocaudal [L CC]) and one with a suspicion of malignancy score of 10% (right craniocaudal [R CC]), shown as polygonal overlays. Without AI, six of 10 readers (60%) marked a false-positive ROI. With AI assistance, this fell to two of 10 (20%). R MLO = right mediolateral oblique.](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/07/2026-07-14-radiology-mammogram-ai-auto-bias.H0bYO8QlWs.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
