共赴“東方之約”，同享“中國機遇”的第二屆中國國際進口博覽會在國家會展中心（上海）圓滿落下帷幕。在為期六天的展會上，進博會散發出越來越大的魅力。
在進博會期間，杏脈科技首席科學家傅琪鉦博士受邀接受中國國際電視臺（CGTN）新聞直播間采訪，與主持人分享AI賦能未來醫療等話題。
Ｑ1：人工智能在醫學領域的水平，可以達到哪些醫學上人類無法做到的事？
FU：AI has great potentials in improving medical care, which still requires continuous innovation and development. Although AI may not be the solution to everything, in certain medical diagnosis AI can do much better jobs than humans. With deep learning, AI algorithms can distinguish small features and correlate different types of images or models, which are hard to be done by naked eyes. I would like to share two examples of what AI can achieve. 人工智能在醫療領域的應用仍需不斷的創新和發展，同時潛力巨大。盡管目前人工智能還無法解決全部問題，但是在人工智能醫學診斷方面已經表現出比人類更出色的能力。運用深度學習技術，人工智能算法對于醫學影像的微小的、高階的變化比人類敏感，在這里我想和大家分享兩個相關案例。
The first example is that AI is good at correlating images between different tests, such as MRI and CT scans. In the diagnosis of certain cases of stroke, MRI images have a high single-to-noise ratio but hard to acquire. On the other hand, CT images have a low single-to-noise ratio but easy to acquire. By AI deep learning, it is possible to use CT images to generate virtual MRI images which can provide an accurate and timely diagnosis. 第一個例子是人工智能善于將不同測試之間的圖像進行關聯，例如MRI和CT掃描。比如說對于某些缺血性腦卒中MR影像為金標準是因為其擁有高S/N比，但卻很難獲得。相反的，CT的影像雖然S/N比低，卻容易獲得。利用深度學習我們可以基于CT的影像模擬出MR的影像，這是人類無法做到的。
The second example is about the power of AI in data integration. In the diagnosis of coronary artery diseases, we need to obtain measurements, such as blood flow rate, pressure, wall shear force, and calculate a parameter as known as Fractional Flow Reserved (or FFR) based on the CT images. Those comprehensive measurements require integration of image recognition, segmentation, and computational fluid dynamics. For this kind of multidisciplinary task, AI is much more powerful than mankind. 第二個例子是關于多模態的綜合分析。比如我們無創冠心病篩查項目中，人工智能從CT影像中能自動分割提取冠狀動脈、自動網格劃分、計算血流動力學，從而模擬得到冠脈上任何一點流速、壓力、wall share force與最后計算出Fractional Flow Reserved, FFR。這種結合影像分割、流體動力學的多模態定量分析是人類無法做到的。
Besides the strength of AI, one important task is how to leverage the power of AI and doctors to improve the workflow of medical diagnosis.
除了以上說的這些，一個重要的任務就是如何利用人工智能和醫生的力量來提高醫學診斷的工作流程。
Ｑ2：人工智能給醫療領域帶來哪些改變，對老百姓的生活帶來怎樣的影響。
FU：The first is about the timely diagnosis. China has over a million cases of stroke each year. For stroke patients, time is brain and AI can greatly improve medical care in such cases of emergency. Although the gold standard for some stroke diagnosis is using MRI, it often takes days of waiting for available machines. CT scanners, however, are standard and readily accessible settings in most of stroke centers. Therefore, our CT-based virtual MRI technology will ease the time pressure by providing a precise and fast diagnosis that is shortened from days to an hour. 首先，對于時效性要求很高的疾病。如腦卒中 time is brain（最好能小于6HR），AI能在最短的時間內輔助醫生(尤其是缺乏經驗的)確診腦卒中類型，進而采取正確的治療方案，對于中國每年新發患者超過百萬的腦卒中病患能及時提供正確的診療建議。
The second revolution AI can bring us is to facilitate universal and affordable health care. One good application of AI-assisted diagnosis is to replace invasive tests with non-invasive ones. In the traditional diagnosis of coronary heart diseases, we need to insert a pressure sensor into a coronary artery to obtain FFR value. It is invasive and expensive- a sensor costs about ten-thousand RMB. So, we believe the CT-based FFR technology will provide non-invasive and low-cost diagnosis and help more patients. 人工智能帶來的第二個革命是為我們帶來了可負擔的醫療服務。人工智能輔助診斷的一個優勢在于無創。在傳統FFR測量是侵入式的壓力導絲，不僅費用昂貴且對患者的心理壓力也大。如今人工智能的FFR CT不僅價格便宜，因其非侵入式的優點老百姓的接受度相對也會比較高，對于冠心病篩查的廣譜性推廣有很大的幫助。
Aitrox Tech has cooperated with tens of hospitals for the R&D and clinical tests on these advanced AI technologies. We hope soon to launch these tools that allow more patients to receive better health care, especially in regions lacking medical resources. And these are some of the great benefits AI can bring to the world. It’s my pressure to be here and thanks for the opportunity to share our vision with the listeners. 以上兩個例子，我們杏脈科技正在與幾十家醫院合作R&D與臨床實驗，相信很快會應用與幫助到老百姓，尤其在醫療資源匱乏地區。這些都是人工智能給世界帶來的一些益處。再次感謝有機會與大家分享科技賦能醫療的愿景。