One Step Beyond: Artificial Intelligence for Image-Based Prognostication


Leveraging artificial intelligence for tumor detection and prognostication

By: Nicolas Orsi, Elizabeth Walsh, Katie Allen

4D Path Chief Pathologist Nicolas Orsi recently partnered with his University of Leeds’ colleagues Elizabeth Walsh and Katie Allen to author an article about leveraging artificial intelligence for cancer diagnostics. Below is an excerpt, followed by a link to the full article in The Pathologist.

It can be disheartening to hear pathologist colleagues say, “With artificial intelligence, I’ll be out of a job in 10 years.” It is reminiscent of 1999, when doomsayers expected Y2K to unleash technology Armageddon. The reality, of course, is more nuanced, but there is no denying that AI-related innovations will have a transformative effect on our discipline. Though many of its perceived benefits focus on improving diagnostic services, there is also scope to harness these innovations to bridge the gap between pathology and oncology.

When digital pathology and whole slide image (WSI) analysis transitioned from the research space into the clinical environment, they received a lukewarm welcome. Reluctance to engage with this technology is traceable to a lack of familiarity or training; belief that digital diagnosis is inefficient; higher levels of confidence in light microscopy; and individuals’ thresholds for embracing new technology. However, as scanners, algorithms, and perspectives have matured, WSI-based diagnosis has proven comparable to that with light microscopy and numerous large centers across the globe now run partially or fully digitized pathology workflows.

Read the full article to learn about the potential for using AI solutions for tumor detection and prognostication.

4D Path’s Satabhisa Mukhopadhyay, founder and chief scientist, and Tathagata Dasgupta, founder and CKO/CTO, also recently co-authored an article in The Pathologist addressing the application of artificial intelligence to extract “the right kind of data” from digital H&E images, which can potentially revolutionize pathology-oncology crosstalk. Read their “Extracting the Right Data for Patient Care” article here.