在救命这件事上，AI开始做医生做不到的事了。

Core Viewpoint - The article highlights the advancements in AI technology for early cancer detection and diagnosis of acute aortic syndrome, emphasizing the potential of AI to save lives through faster and more accurate medical assessments [2][48][53]. Group 1: AI in Cancer Detection - The collaboration between Alibaba's DAMO Academy and Ningbo University Affiliated People's Hospital has led to the development of the PANDA model, which can detect pancreatic cancer through a standard CT scan [2][5]. - Following this, the GRAPE model was introduced for gastric cancer screening, also utilizing a regular CT scan, demonstrating the capability of AI to identify high-risk patients effectively [3][4]. - The GRAPE model showed a detection rate of 24.5% and 17.7% for gastric cancer in two regional hospitals, with significant early-stage detection rates [4]. Group 2: AI in Diagnosing Acute Aortic Syndrome - The iAorta model was developed to diagnose acute aortic syndrome (AAS) using non-contrast CT scans, achieving a sensitivity of 95.5% and specificity of 99.4% during clinical trials [48][50]. - The average time from admission to diagnosis for AAS was reduced to 1.7 hours with the use of iAorta, compared to the international average of 4.3 hours, significantly decreasing the risk of mortality [50]. - The model was able to identify AAS in a patient who was initially misdiagnosed with gallbladder stones, showcasing its potential to prevent misdiagnosis and expedite treatment [50]. Group 3: Broader Implications of AI in Healthcare - The article emphasizes the importance of timely diagnosis in critical conditions, stating that every minute counts in saving lives, particularly in cases like AAS and heart attacks [19][58]. - It advocates for the widespread deployment of AI models in hospitals and clinics across the country to ensure that patients in remote areas have access to advanced diagnostic tools [55][59]. - The narrative underscores the transformative impact of AI in healthcare, suggesting that it can bridge the gap in medical disparities and enhance early detection of life-threatening conditions [60][63].