Harvard Medical School’s Blavatnik Institute has developed a new artificial intelligence (AI) tool, CHIEF, that can effectively diagnose 11 cancer types and offer real-time predictive insights, outperforming current models by a staggering 36%. The tool, detailed by the study published in Nature on September 4, 2024, could radically improve patient outcomes by assisting doctors in predicting cancer prognosis and treatment responses.

Breaking the Mold of Existing AI Systems

The development of CHIEF, short for Clinical Histopathology Imaging Evaluation Foundation, represents a major step forward in cancer diagnostics. Unlike most existing tools, which focus on a single cancer type or a narrow set of tasks, CHIEF aims to provide generalized diagnostics.

“Our ambition was to create a nimble, versatile ChatGPT-like AI platform that can perform a broad range of cancer evaluation tasks,” said Kun-Hsing Yu, an assistant professor at Harvard and one of the lead researchers. “Unlike existing methods, our AI tool provides clinicians with accurate, real-time second opinions on cancer diagnoses by considering a broad spectrum of cancer types and variations.”

CHIEF was trained using over 15 million pathology images, making it robust enough to handle even atypical cancer presentations. The research team then fine-tuned the model with 60,000 high-resolution tissue images. The model has been tested on five independent biopsy datasets, where it reached 96% accuracy in diagnosing cancers like those of the esophagus, colon, stomach, and prostate. In some cases—like lung, colon, and breast cancer—the AI hit over 90% accuracy, even on previously unseen tumor slides.

CHIEF’s ability to predict patient survival based on tumor histopathology images is particularly noteworthy. It can distinguish patients with longer-term survival from those with shorter-term survival, providing crucial information for treatment planning. The model also predicts mutations linked with response to FDA-approved targeted therapies across 18 genes spanning 15 anatomical sites.

For instance, CHIEF has been shown to predict key genetic mutations in 54 genes across multiple cancer types, including the BRAF gene (in thyroid cancer) and the EZH2 gene (in blood cancers).

Moving Beyond Diagnosis

Traditional methods of histopathology image evaluation are indispensable but often limited by human error and variability. CHIEF addresses these limitations by providing consistent, high-accuracy evaluations across diverse cancer types.

But before CHIEF is introduced into hospitals, it still needs to prove itself in clinical settings. The model was tested on almost 20,000 whole-slide images from 24 hospitals worldwide, but its real-world application requires continuous validation to ensure its reliability and accuracy.

“We are launching a prospective clinical study to validate the CHIEF model in real-world clinical settings,” Yu shared. Ensuring that the model performs equally well across diverse patient demographics and different clinical conditions is essential before regulatory approval.

The development’s next phase will focus on broadening CHIEF’s applicability further by incorporating samples from rare cancers and pre-malignant tissues. The ultimate goal is to refine its molecular data analysis, enhancing its diagnostic accuracy and predictive power.

As artificial intelligence continues to transform medical diagnostics, CHIEF stands at the forefront, offering hope for more accurate, efficient cancer care. With further validation, this AI tool could one day become a standard fixture in oncology departments worldwide, helping doctors make quicker, more accurate diagnoses that improve patient outcomes.