One of the most rapidly growing startups at the intersections of technology, artificial intelligence and radiology, Rad AI, announced today that it will be partnering with Google Cloud in scaling its efforts to improve clinical radiologists’ workflows and mitigate burnout. The company has numerous offerings and is perhaps most well-known for its Reporting and Omni Impressions platforms, both of which have been developed as a means to augment clinician productivity. For example, Rad AI Omni Impressions automatically generates report impressions once the raw findings have been dictated by the radiologist. The key differentiator of this technology is that the platform continuously learns from each user and generates a personalized output that is in the stylistic tone and literary voice of each individual radiologist.
The company’s partnership with Google Cloud will provide an opportunity to scale its technology by leveraging Google’s platform, MedLM foundation models and its cloud services. MedLM, a family of foundation models fine-tuned for healthcare applications, will especially help unlock a significant amount of potential for the company by increasing the accuracy of reports, improving stylistic customizations and contributing to workflow optimization.
Though Rad AI is relatively young, it has seen significant investment and interest; its Series A funding round alone involved nearly $25 million, and the company is now working with nearly 30% of the radiology services market in the United States.
Doktor Gurson, Co-founder and CEO of Rad AI, enthusiastically explains that the company has been built by radiologists for radiologists with a few ambitious goals: reduce radiologist burnout, improve clinical workflows and ultimately, make a positive impact on clinical care delivery. He also explains that the partnership with Google Cloud is an important step forward for the company in its growth journey: “Rad AI brings the domain expertise in radiology, and Google Cloud’s MedLM now gives us the opportunity to level up and push further.”
Aashima Gupta, Global Director, Healthcare Strategy and Solutions at Google Cloud, explains that there is significant opportunity in the field of radiology to leverage artificial intelligence technology and generate content for the purposes of ultimately improving the care delivery process: “as the amount of medical imaging grows, we can equip radiologists with more robust platforms and technologies to make their workflows more efficient and accurate.”
Gupta alludes to an important aspect about the field of radiology; a key study in the European Journal of Radiology found that there has been a significant overall increase in the use of radiological imaging and corresponding workload for radiologists over the past 16 years. Another study published in Academic Radiology took a deep dive into causative factors for burnout in radiologists and found numerous systemic and workflow factors leading to increasing levels of attrition.
But the healthcare ecosystem cannot afford to lose radiologists due to burnout, given just how essential this function is for patient care. This is why companies are heavily investing with an unprecedented focus on radiology, hoping to leverage the latest in artificial intelligence technology. Notably, scientists and leaders are approaching this phenomenon cautiously: extensive studies are being conducted and funded to measure the efficacy of AI in radiology and its levels of accuracy. Regarding this factor, Gurson emphasizes that even with technologies such as Rad AI, it will ultimately be important for a human radiologist to review the outputs and provide the final sign-off, keeping the “human in the loop.”
Nevertheless, these are just early days for the intersection of artificial intelligence and radiology. Undoubtedly, the scale and speed at which this technology is growing indicates that there is still a lot of work to be done; however, if executed correctly, there is significant promise to potentially create positive impact.