Researchers at Harvard Medical School have developed a versatile AI model, similar to ChatGPT, that can perform a wide range of diagnostic tasks across various types of cancer.
The new model, CHIEF (Clinical Histopathology Imaging Evaluation Foundation), was trained on 15 million image sections and 60,000 whole-slide tissue images from various organs for more comprehensive analysis.
The researchers noted that the new AI system surpasses current methods by handling a wider range of tasks and being tested on 19 cancer types, offering flexibility similar to large language models like ChatGPT. The research team noted that the findings support growing evidence that AI can help clinicians evaluate cancers more efficiently and identify patients who may not respond to standard therapies.
“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, senior author of the study and assistant professor at Harvard Medical School.
The researchers plan to improve CHIEF by training it on rare diseases, non-cancerous and pre-malignant tissues, and incorporating more molecular data. They also aim to enhance its ability to predict the benefits and side effects of both standard and novel cancer treatments.
“If validated further and deployed widely, our approach, and approaches similar to ours, could identify early cancer patients who may benefit from experimental treatments.” he added, empahsising on how targeting specific molecular variations is a capability not universally available worldwide.
Detecting Cancer with AI
Cancer detection CHIEF achieved nearly 94 percent accuracy in cancer detection and significantly outperformed current AI approaches across 15 datasets containing 11 cancer types including esophagus, stomach, colon, and prostate.
The team’s latest work expands on Yu’s previous research in AI for cancer evaluation.
Tested on 19,400 images from 32 datasets across 24 hospitals, CHIEF outperformed other state-of-the-art AI methods by up to 36 percent on the following tasks: cancer detection, tumor origin, outcome prediction, and gene identification.
Its versatility allows it to perform equally well across different clinical settings, regardless of how tumor samples were obtained or digitised.
Others
Google’s AI detects breast cancer in anonymised screenings with greater accuracy and fewer false negatives or positives than human experts. MIT researchers at the Jameel Clinic for Machine Learning and the Computer Science and Artificial Intelligence Laboratory (CSAIL) developed an AI model that can detect breast cancer up to five years before clinical diagnosis.
Back home, Indian Institute of Technology Madras (IIT Madras) researchers developed an Artificial Intelligence-based tool, ‘PIVOT’, that can predict cancer-causing genes in an individual.
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