Alibaba Group's DAMO Academy, in collaboration with Shengjing Hospital of China Medical University and other institutions, has developed a liver cancer diagnostic AI model named DAMO LiON that can identify tiny liver lesions through CT imaging. In a two-month real-world prospective clinical trial, the AI model uncovered 15 malignant tumors that had been previously overlooked, with the vast majority measuring around 1 centimeter in size, enabling patients to receive timely surgical or pharmaceutical treatment. The related research paper was published in the prestigious international academic journal Nature Medicine.

The DAMO Academy leveraged years of technical expertise in medical imaging AI to develop the DAMO LiON model, which serves as an "AI safety officer" assisting radiologists in image review. It not only accurately identifies primary liver cancer but also excels at detecting easily overlooked liver metastases. Experimental results demonstrated that the AI model achieved higher accuracy in identifying malignant tumors compared to radiologists. When physicians used the AI model to assist in image interpretation, reading time decreased by 27%, while sensitivity to malignant tumors improved by 11.5%, effectively reducing missed diagnoses. With AI assistance, junior doctors reached the performance level of senior specialists.

The research team subsequently deployed the AI model in hospitals for routine image reading. When the AI's conclusions differed from a doctor's initial diagnosis, the case was escalated to senior radiologists for review and, when necessary, advanced to multidisciplinary team (MDT) discussions. Within two months, the AI reviewed contrast-enhanced CT images from over 10,000 patients, helping physicians identify 15 cases of liver metastases that had been previously ignored, thereby altering the treatment plans for these patients.

Yan Ke, an algorithm expert at DAMO Academy, noted that the malignant lesions identified by the AI were generally "small, faint, and atypically located": with an average diameter of about 1 centimeter, low contrast against liver tissue, or situated in less common anatomical positions. Underlying this capability, the DAMO LiON model employs an improved network architecture that captures the relationship between lesions and the entire liver while effectively preserving local texture and boundaries, enhancing performance on challenging cases such as fatty liver, cirrhosis, and post-operative livers. Additionally, the AI iteratively fuses images from different contrast phases, effectively capturing pixel-level differences between phases to precisely pinpoint subtle lesions that appear transiently during contrast-enhanced CT scans.