Rad AI, an AI-powered radiology workflow solutions provider, has appointed Leonard Law as Chief Product Officer. Law previously served as CPO at enterprise AI platform Primer and held senior product roles at Coinbase, Google Cloud, and YouTube. The appointment comes as Rad AI reports its strongest quarter, driven by enterprise adoption including Yale New Haven Health System, which saw a 12% boost in reporting efficiency within the first week of implementation. Law will work with co-founder Jeff Chang to scale Rad AI's suite of products, including Rad AI Reporting, Impressions, and Continuity. The company recently launched next-generation speech recognition technology and partnered with RSNA Ventures. Rad AI serves health systems and radiology practices representing over half of the US radiology market.

Exclusive: Rad AI expands partnership with flagship health system Yale New Haven. Yale New Haven Health will deploy Rad AI's radiology tools across its multi-network health system while co-developing and clinically testing new radiology technologies. California-based Rad AI, a generative AI radiology workflow company, is extending its partnership with Connecticut-based Yale New Haven Health System (YNHHS) to deploy its reporting technology across the health system's imaging network. "Yale was our first big academic site," Dr. Elizabeth Bergey, radiologist and chief clinical officer at Rad AI, told MobiHealthNews. "It's been a partnership where we've grown to trust each other and work with each other very closely to help develop a big part of what Rad AI provides now," Bergey said. Rad AI offers radiologists generative AI tools to help streamline workflows, such as physician dictation and follow-up care management. Its genAI-enabled reporting software helps radiologists create reports quickly by automating portions of the reporting process and reducing dictation time. The company's Omni Impressions generates AI-enabled report impressions after a radiologist dictates the raw findings, a tool that makes Rad AI stand out to radiologists, YNHHS' Dr. Melissa Davis, vice chair for imaging informatics, radiology and biomedical imaging and associate professor of radiology and biomedical imaging, told MobiHealthNews. "Rad AI is at the forefront, especially with its Omni Impressions tool," Davis said. "This is the one tool that if you ask a radiologist broadly, 'What's made my day better?' An impression generator is what it is, and Rad AI was at the forefront of that market." Yale New Haven will implement several of Rad AI's technologies, including Omni Impressions, across its multi-site network, which includes more than 16 outpatient imaging centers and five hospital campuses. Still, Davis said, the health system's goal is not to implement products but rather to co-develop new technologies with Rad AI that meet its specific needs. In addition to implementing Rad AI's existing tech into YNHHS, the company's now flagship healthcare system, the organizations will co-develop new specialized radiology-focused technologies that will be used and clinically tested at YNHHS. "There's a lot of questions that I think that they can help us solve on the ground, and so I'm not really thinking about it from a product standpoint at this point," Davis said. "Yes, we needed the reporter, like, that was the product for us, but my hope is that we can generate an actual partnership that starts to align with our needs and with their roadmap going forward." Davis said one of the health system's pain points, though Rad AI has not been solidified as the point of collaboration for this specifically, is data management. "We have a lot of information, and we don't know how to surface the questions that we actually need answered," Davis said. "How many radiologists should we have today? Are there certain services where we should staff up on certain times of the day? How can we do more predictive analysis, so that our radiologists can be more flexible in their scheduling is a huge pain point for us. How do we track productivity appropriately?" The partners say future technologies that will be developed and tested have yet to be nailed down, but many ideas are being explored regarding how to co-develop those technologies, and Rad AI said it has a system to meet such challenges. "We are hiring the best scientists there are. We are known for making a personalized model for impressions, and that kind of base skill set is what we have, which is building custom models for radiology-specific tasks," Bergey said. "We have some great ideas, and I'm very excited on how we can expand together because it is a great partnership." HIMSS is hosting the one-day AI Executive Leadership Summit in Boston June 24, 2026, followed by its AI in Healthcare Forum June 25-26. Register separately for the two events here and here.