New framework renders AI more trustworthy for cancer subtyping

by Vanderbilt University Medical Center

edited by Sadie Harley, reviewed by Robert Egan

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Medical artificial intelligence (AI) faces a fundamental challenge: uncertainty quantification. Artificial neural networks are largely unaware of the limits of their training data and can become overconfident when confronted with unfamiliar inputs. Suppose you train a neural network to distinguish among African mammals. If you then present it with an image of a South American jaguar—an animal it has never encountered—the model cannot say "unknown." Instead, it may confidently declare the jaguar to be a leopard.

Reported in Nature Biomedical Engineering, researchers at Vanderbilt Health and centers in Hong Kong have created a versatile, uncertainty-aware AI framework broadly adaptable as a wrapper for digital pathology AI systems. (An AI wrapper acts as an interface layer that customizes, formats and automates how users interact with the underlying intelligence.)

They demonstrate their wrapper, called TRUECAM, primarily with reference to non-small cell lung cancer (NSCLC) subtyping using whole-slide images.

How TRUECAM screens noisy inputs

TRUECAM is designed not only for identifying out-of-scope inputs, but also to filter out noninformative regions—normal or poorly stained tissue, for example—that could distort slide-level inference. According to the paper, these complementary capacities allow TRUECAM to provide customizable accuracy guarantees for cancer subtype classifications.

The team tested TRUECAM as a wrapper for a widely used AI architecture for NSCLC subtyping and four newer, more generalized digital pathology AI foundation models.

Testing involved NSCLC whole-slide images from two geographically diverse cancer research consortia, a constructed set of clinically meaningful out-of-scope images and a sequence of real-world images from Queen Mary Hospital in Hong Kong. Testing also extended to cancer tissue spanning multiple organs, such as breast, brain and kidney.

Compared with other solutions for trustworthy digital pathology AI, the authors say TRUECAM performs not only with greater accuracy but also relatively rapidly and efficiently, without adding substantial costs.

Safer deferrals and broader reach

"Achieving trustworthy AI in the medical domain is requisite for realizing the potential of this transformative technology," said one of the paper's three corresponding authors, Bradley Malin, Ph.D., professor of biomedical informatics, biostatistics and computer science, and holder of the Accenture Chair.

"It's not only that a patient's clinical profile can fall out of scope of your model's training data, but other sources of variation, such as your institution's method of collecting and staining specimens, or artifacts and irregularities that tend to arise in tissue preparation, can also prompt your model to arrive confidently at a mistaken conclusion. TRUECAM provides a thoroughgoing, versatile and efficient solution to these potentially unsafe shortcomings."

The team reports that TRUECAM proved more accurate and efficient than existing approaches to digital pathology AI uncertainty quantification, reliably detected out-of-scope inputs, abstained from classifying challenging inputs (allowing deferral to pathologists), delivered error rates that reliably met prespecified accuracy targets, improved fairness across sex and race, and proved generalizable to data sets beyond lung cancer.

Chao Yan, Ph.D., MS, research instructor in biomedical informatics, is among the paper's three lead authors.

"Perhaps our most striking finding," Yan said, "was that, with ambiguous patches and normal regions often found to dominate a pathology slide, TRUECAM's targeted elimination of this noise, and its resulting focus on sometimes comparatively small patches in an image, allows it to proceed efficiently to accurate and fairer cancer subtype classification, with the model focusing on the same regions pathologists identify as diagnostically relevant. This goes beyond current approaches, and the practical implications appear to have broad import."

Publication details

Implementing trust in non-small cell lung cancer diagnosis with a conformalized uncertainty-aware AI framework, Nature Biomedical Engineering (2026). DOI: 10.1038/s41551-026-01694-8

Journal information: Nature Biomedical Engineering

Key medical concepts

Carcinoma, Non-Small-Cell LungMalignant neoplasm

Clinical categories

OncologyLaboratory medicine Provided by Vanderbilt University Medical Center Who's behind this story?

Sadie Harley

BSc Life Sciences & Ecology. Microbiology lab background with pharmaceutical news experience in oil, gas, and renewable industries. Full profile →

Robert Egan

Bachelor's in mathematical biology, Master's in creative writing. Well-traveled with unique perspectives on science and language. Full profile →

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