Rad AI this week announced its new collaboration with Google, which will see the radiology startup using the cloud giant's artificial intelligence capabilities to further help streamline reporting and reduce administrative burden.

WHY IT MATTERS
Under the partnership, Rad AI using Google Cloud's platform and tools such MedLM, a family of foundation models fine-tuned for healthcare industry use cases, including Gemini-based models in the future.

Google Cloud, meanwhile, will become Rad AI's preferred cloud provider, helping the startup build out and enhance its platforms, Rad AI Omni Impressions and Rad AI Reporting, with domain-aligned generative AI models.

That will help Rad AI be able to automatically generate more of the radiology report, customized to each radiologist's preferred language and style – saving them time while helping improve the quality and consistency of reports.

Rad AI will also be able to scale up the size and complexity of its Rad AI Omni Impressions and Rad AI Reporting genAI models, the company says – enabling increased clinical accuracy, personalization and performance gains.

"This partnership represents an exciting leap forward in our commitment to transforming the radiology reporting landscape," said Doktor Gurson, cofounder and CEO of Rad AI, in a statement. "Through this unique collaboration with Google, we can dramatically accelerate our mission of reducing radiologists' burnout, streamlining workflow, and ultimately improving the quality of patient care."

THE LARGER TREND
Imaging data accounts for about 90% of all healthcare data, and the number of imaging exams conducted each year is only increasing – piling onto an already strenuous workload for radiologists, who spend hours dictating reports based on these images. Rad AI says its tools can reduce words needed for dictation by as much as 90%.

Artificial intelligence is transforming all aspects of the radiology reading and reporting process and reshaping imaging professionals' workflows, as Dr. Benoit Desjardins, professor of radiology at Penn Medicine, explained recently.

Meanwhile, new FDA approvals, homegrown imaging advancements developed at academic medical centers and new tools from other vendors are advancing the AI-enabled transformation of radiology practices.

Still, there are challenges to guard against, related to bias, security and more.

ON THE RECORD
"Radiology is a field that stands to see immediate, high-value impact from advancements in generative AI, and radiology reporting is an area where this technology can have a meaningful impact," said Aashima Gupta, global director of healthcare strategy and solutions at Google Cloud. "As the number of medical images continues to grow, our goal is to enable the ecosystem and help our customers equip radiologists with the latest generative AI capabilities not only to help manage workflows but also to expedite patient treatment through faster and more accurate diagnoses."

Mike Miliard is executive editor of Healthcare IT News
Email the writer: mike.miliard@himssmedia.com
Healthcare IT News is a HIMSS publication.