Gaps in colonoscopy skills among endoscopists, primarily due to experience, have been identified, and solutions are critically needed. Hence, the development of a real-time robust detection system for colorectal neoplasms is considered to significantly reduce the risk of missed lesions during colonoscopy. Here, we develop an artificial intelligence (AI) system that automatically detects early signs of colorectal cancer during colonoscopy; the AI system shows the sensitivity and specificity are 97.3% (95% confidence interval [CI] = 95.9%–98.4%) and 99.0% (95% CI = 98.6%–99.2%), respectively, and the area under the curve is 0.975 (95% CI = 0.964–0.986) in the validation set. Moreover, the sensitivities are 98.0% (95% CI = 96.6%–98.8%) in the polypoid subgroup and 93.7% (95% CI = 87.6%–96.9%) in the non-polypoid subgroup; To accelerate the detection, tensor metrics in the trained model was decomposed, and the system can predict cancerous regions 21.9 ms/image on average. These findings suggest that the system is sufficient to support endoscopists in the high detection against non-polypoid lesions, which are frequently missed by optical colonoscopy. This AI system can alert endoscopists in real-time to avoid missing abnormalities such as non-polypoid polyps during colonoscopy, improving the early detection of this disease.

In recent years, advances in artificial intelligence (AI) technology have led to the rapid clinical implementation of devices with AI technology in the medical field. More than 60 AI-equipped medical devices have already been approved by the Food and Drug Administration (FDA) in the United States, and the active introduction of AI technology is considered to be an inevitable trend in the future of medicine. In the field of oncology, clinical applications of medical devices using AI technology are already underway, mainly in radiology, and AI technology is expected to be positioned as an important core technology. In particular, “precision medicine,” a medical treatment that selects the most appropriate treatment for each patient based on a vast amount of medical data such as genome information, has become a worldwide trend; AI technology is expected to be utilized in the process of extracting truly useful information from a large amount of medical data and applying it to diagnosis and treatment. In this review, we would like to introduce the history of AI technology and the current state of medical AI, especially in the oncology field, as well as discuss the possibilities and challenges of AI technology in the medical field.

Seiichiro Abe, Shih Yea Sylvia Wu, Mai Ego, Hiroyuki Takamaru, Masau Sekiguchi, Masayoshi Yamada, Satoru Nonaka, Taku Sakamoto, Haruhisa Suzuki, Shigetaka Yoshinaga, Takahisa Matsuda, Ichiro Oda, and Yutaka Saito. Gut and Liver 1999;0:. https://doi.org/10.5009/gnl19266

Background: Magnifying Narrow Band Imaging (NBI) during colonoscopy is a reliable method for differential and depth diagnoses of colorectal lesions. This study examined the diagnostic yield of magnifying NBI based on the Japan NBI Expert Team (JNET) classification in a clinical setting using a large‐scale clinical practice database. Types 1, 2A, 2B and 3 correspond to the histopathological classifications of hyperplastic polyp/sessile‐serrated polyp, low‐grade intramucosal neoplasia, high‐grade intramucosal neoplasia/shallow submucosal invasive cancer, and deep submucosal invasive cancer, respectively.Methods: The prospective records of colonoscopy reports and pathological data of 1558 consecutive superficial colorectal lesions removed by colonoscopy were retrospectively analysed. After excluding 156 lesions, the documented JNET classifications of the remaining 1402 colorectal lesions were analysed. Diagnostic yield was analysed and also compared between expert endoscopists and nonexpert endoscopists.Results: The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy were respectively 75%, 96%, 74%, 96% and 93% for type 1; 91%, 70%, 92%, 67% and 87% for type 2A; 42%, 95%, 26%, 98% and 93% for type 2B; and 35%, 100%, 93%, 98% and 98% for type 3. Nonexpert and expert endoscopists alike had specificity, NPV and accuracy >90% for types 1, 2B and 3, and a sensitivity and PPV >90% for type 2A. Type 2B had a low sensitivity of 42% because it included various histological features.Conclusions: The JNET classification proved useful in a clinical setting both for expert and nonexpert endoscopists, as was expected from the original JNET definition, but type 2B requires further investigation using pit pattern diagnosis.