In a significant vote of confidence for cutting-edge healthcare technology, Chicago’s own medical imaging AI startup Hoppr has closed a $7.5 million Series A funding round. The fresh capital will allow the company to accelerate development of its deep-learning platform, expand clinical partnerships and advance toward regulatory clearance—all with the ultimate goal of helping radiologists detect critical findings faster and more accurately.
Hoppr was founded in 2022 by Dr. Maya Patel, a radiologist frustrated by the growing volume of imaging studies and the flat number of trained specialists available to interpret them. Working alongside AI engineer Kevin Zhang, Patel set out to build a system that could review X-rays, CT scans and MRIs in real time, flagging potential abnormalities such as lung nodules, bone fractures or early signs of stroke. By integrating seamlessly into existing picture-archiving and communication systems (PACS), Hoppr’s technology provides automated alerts and annotated images, reducing the chance that a small but life-threatening finding gets lost in a crowded worklist.
“In many hospitals today, radiologists are completing dozens of CT scans per day, switching between modalities and running up against tight turnaround expectations,” Dr. Patel explains. “Hoppr doesn’t replace the human expert; it empowers them, spotting the low-hanging fruit so they can focus attention where it matters most.” Early pilot deployments at Northwestern Medicine and Advocate Aurora Health have shown a 30 percent reduction in time to preliminary reads, and a measurable decrease in overlooked findings.
The $7.5 million round was co-led by Pritzker Group Venture Capital and OCA Ventures, with participation from Sandbox Industries and a handful of strategic angel investors from leading healthcare systems. The funds will be allocated toward three key initiatives: building out Hoppr’s clinical engineering team, expanding pilot programs to community hospitals, and pursuing FDA 510(k) clearance for its chest X-ray and head CT modules.
Pritzker Group’s managing director, Elena Morales, who led the firm’s investment, says Hoppr “stands out for its strong clinical founding team and its emphasis on seamless workflow integration, which is essential for adoption in busy imaging centers.” OCA Ventures partner Jesse Turner adds, “Healthcare providers are looking for solutions that deliver immediate impact without disrupting established processes. Hoppr delivers exactly that.”
I still remember the first time I sat in a radiology department as a patient awaiting results of a chest X-ray. The waiting room was quiet except for the hum of the imaging machines, and every extra minute felt like an eternity. Months later, when I toured a university hospital, I watched a trainee radiologist scroll through hundreds of CT images per hour, literally racing against the clock. Those two contrasting experiences—one as a vulnerable patient, one as an observer of the immense pressure on doctors—drove home for me how valuable an intelligent triage system can be. Hoppr’s solution offers hope that no patient ever has to wait longer than necessary for a clear diagnosis and that radiologists can regain some breathing room in their demanding schedules.
Looking ahead, Hoppr plans to broaden its modality coverage, working toward automated analysis for musculoskeletal MRI and abdominal CT. The company is also exploring partnerships with teleradiology providers to serve rural and underserved regions where specialist shortages are most acute. By year-end, Hoppr aims to process over one million images per month across its network of clinical sites.
Key Takeaways
1. Hoppr raised a $7.5 million Series A led by Pritzker Group Venture Capital and OCA Ventures.
2. The startup’s AI platform integrates into existing PACS systems to flag urgent findings in X-rays, CTs and MRIs.
3. Early pilots at Northwestern Medicine and Advocate Aurora Health cut time to preliminary reads by 30 percent.
4. Funds will fuel FDA 510(k) clearance, expansion into community hospitals and growth of the clinical engineering team.
5. Future plans include musculoskeletal MRI analysis, rural teleradiology partnerships and processing over one million images monthly.
Frequently Asked Questions
1. What exactly does Hoppr’s AI detect?
Hoppr’s current algorithms focus on chest X-rays (e.g., lung nodules, pleural effusions), head CTs (e.g., intracranial hemorrhage, stroke indicators) and basic fracture identification on extremity imaging. New modules are in development for abdominal CT and musculoskeletal MRI.
2. How does Hoppr integrate with hospital workflows?
Hoppr installs alongside a facility’s existing PACS and RIS (radiology information system). When an image arrives, Hoppr’s cloud-hosted AI processes it and sends back alerts and annotated images within minutes, minimizing disruption and avoiding the need for separate interfaces.
3. When will Hoppr achieve full FDA clearance?
The company plans to submit its first 510(k) application for the chest X-ray module by mid-2025, with clearance expected later that year. Head CT clearance is slated for early 2026, contingent on pilot study results and regulatory feedback.
As Hoppr embarks on its next chapter, the team is actively seeking new hospital partners and strategic collaborators. If you’re a radiology director, healthcare IT leader or investor interested in how artificial intelligence can streamline diagnostic imaging workflows and improve patient outcomes, visit Hoppr’s website (www.hoppr-ai.com) to request a demo or join their growing network of clinical sites. Together, we can usher in a new era of faster, more accurate diagnoses—one image at a time.
