Spotlight on Dr. Yashbir (Yash) Singh, M.E., Ph.D.

We’re pleased to feature Dr. Yashbir Singh, a biomedical engineer and cancer data scientist at the Mayo Clinic. His work centers on using AI-driven medical imaging to enhance cancer diagnostics and treatment planning, with a particular focus on cholangiocarcinoma.
Under the guidance of Dr. Greg Gores and Jesper Andersen, Dr. Singh discusses in this blog how innovative approaches are being advanced to improve diagnostic accuracy and treatment planning in cholangiocarcinoma.

Dr. Singh is also an early-career researcher in the ICRN Mentorship Program—an initiative of the International Cholangiocarcinoma Research Network (ICRN), ENSCCA, and the Precision-BTC Network (COST Action CA22125). The program fosters global collaboration and career development for promising young investigators. We’re proud to support Dr. Singh’s contributions to the future of precision medicine in BTC.
The Dawn of AI Transforming Cholangiocarcinoma Care
Cholangiocarcinoma has long challenged medical professionals due to the complexities of imaging this tumor. Today, artificial intelligence (AI) stands at the threshold of transforming how we approach this disease, offering new possibilities for patients, clinicians, and researchers alike. At its core, the power of AI lies in its ability to recognize radiologic imaging patterns that the human mind simply cannot process. For cholangiocarcinoma patients, this could mean earlier detection through AI-enhanced imaging that can identify subtle abnormalities long before they become visible to the human eye and likely also before a molecular alteration becomes detectable at least in non-invasive liquid biopsies. When every day counts for a later prognosis, an early warning system could dramatically improve patient outcomes (overall survival).

Perhaps most importantly, this technology centers on the patient experience. AI-powered patient monitoring systems can track symptoms, small changes in behavior, and side effects in real-time, alerting care teams to issues before they become emergencies. Digital companions can record and answer questions day or night, ensuring patients never feel alone in their journey and significant data points are not recorded. The future of cholangiocarcinoma care is not about replacing personal interaction and empathy with cold technology. Instead, it is about creating a symphony where AI routinely handles the data-intensive tasks, allowing medical professionals to focus more fully on what they do best: providing compassionate, personalized care.
Perhaps most importantly, this technology centers on the patient experience. AI-powered patient monitoring systems can track symptoms, small changes in behavior, and side effects in real-time, alerting care teams to issues before they become emergencies. Digital companions can record and answer questions day or night, ensuring patients never feel alone in their journey and significant data points are not recorded. The future of cholangiocarcinoma care is not about replacing personal interaction, empathy, with cold technology. Rather, it is about creating a symphony where AI routinely handles the data-intensive tasks, allowing medical professionals to focus more fully on what they do best: providing compassionate, personalized care.
As we stand at this crossroads of innovation, one thing is clear: by embracing AI as a partner in our fight against cholangiocarcinoma, we create new possibilities for hope, healing, and ultimately, life.
Read more:
1.Singh, Y., Eaton, J. E., Venkatesh, S. K., Welle, C. L., Smith, B., Faghani, S., … & Erickson, B. J. Deep learning analysis of magnetic resonance imaging accurately detects early-stage perihilar cholangiocarcinoma in patients with primary sclerosing cholangitis. Hepatology, 10-1097.
2. Eaton, J. E., Welle, C. L., Bakhshi, Z., Sheedy, S. P., Idilman, I. S., Gores, G. J., … & Venkatesh, S. K. (2021). Early cholangiocarcinoma detection with magnetic resonance imaging versus ultrasound in primary sclerosing cholangitis. Hepatology, 73(5), 1868-1881.
3.Calderaro, J., Ghaffari Laleh, N., Zeng, Q., Maille, P., Favre, L., Pujals, A., … & Kather, J. N. (2023). Deep learning-based phenotyping reclassifies combined hepatocellular-cholangiocarcinoma. Nature communications, 14(1), 8290.4.Zerunian, M., Polidori, T., Palmeri, F., Nardacci, S., Del Gaudio, A., Masci, B., … & Caruso, D. (2025). Artificial Intelligence and Radiomics in Cholangiocarcinoma: A Comprehensive Review. Diagnostics, 15(2), 148.