Exploring The Application of Artificial Intelligence in Radiology Scans

Trends regarding the supplementation of radiology practice with artificial intelligence (AI) include mammography, early screening for other diseases, and the integration of AI systems into RECIST criteria, according to Arturo Loaiza-Bonilla, MD, MSEd, FACP. Loaiza-Bonilla discussed these topics in a conversation with CancerNetwork®.

First, he suggested that traditional mammography may require a significant amount of time and work, and that AI might be utilized to facilitate this process while mitigating the need to undergo a double review as frequently. Loaiza-Bonilla explained that, after data had been gathered in Europe, trials have recently started in the US to assess its own demographic of patients at risk for breast cancer.

He further highlighted ongoing work examining detection of early lung cancers through screening in addition to X-rays being used in the emergency department. He further highlighted an initiative from the Friends of Cancer Research who are seeking to integrate the use of AI systems into RECIST guidelines to embed algorithms for assessing tumor burden and characterizing disease in a scalable fashion.¹

Lastly, Loaiza-Bonilla discussed the application of AI to predictive models, such as those within the PANORAMA trial, to explore AI’s role in helping to detect early disease through CT scans in pancreatic cancer. He concluded by hoping that these models begin being implemented into real-world practice, citing the high stakes of cancer as a disease.

Loaiza-Bonilla is the systemwide chief of Hematology and Oncology at Saint Luke’s University.

Transcript:

Radiologists now, particularly in oncology, are mostly focused on 2 major trends. Mammography is one of them; [we] try to optimize times because there are many patients who require screening and [it’s a] significant amount of [time and] work. We are now using AI to assist with that, to decrease the amount of double review on those mammograms, and it’s also being run on clinical trials. We have data from Europe, but now there’s a number of efforts in the US as well, because our population is different in terms of breast density and beyond. I’m excited to see the results of those algorithms as they get deployed.

Other [trends] that we are looking [at] closely are in finding lung nodules and detecting them in real time so we can find lung cancer early through screening, even from X-rays found in the emergency department. There are some efforts doing [that]. More advancement is on the use of RECIST criteria for clinical trials. There’s an effort from Friends of Cancer Research where they are trying to do ai.RECIST criteria, where we just embed the algorithms and have been able to measure the lesions in a more effective and scalable way. Radiologists are a key part of that.

Lastly, [AI is being used in] some predictive models. There are some efforts, like the PANORAMA trial results, where they show that … CT scans can detect pancreatic cancer early.² This is one of the diseases that we want to find them early, before they really become metastatic, as most of them are either locally advanced/resectable or metastatic. Very promising on the radiology side.

What I hope for is to start using those models in real practice. As of now, many of the models that we use in radiology have been localized to those couple use cases and for the emergency department, such as finding fractures, but now, with oncology being one of those very high stakes diseases, we have an opportunity here.

References

1. Ai.RECIST Project: Artificial Intelligence-Enabled Response Evaluation Criteria in Solid Tumors Project. Friends of Cancer Research. Accessed January 28, 2026. https://tinyurl.com/rx26459z
2. Alves N, Schuurmans M, Rutkowski D, et al. Artificial intelligence and radiologists in pancreatic cancer detection using standard of care CT scans (PANORAMA): an international, paired, non-inferiority, confirmatory, observational study. Lancet Oncol. 2026;21(1):116-124. doi:10.1016/S1470-2045(25)00567-4