Hundreds of samples need to be processed under the microscope by the naked eyes of experienced doctors each day at Nanfang hospital, the No.1 hospital in China for cervical cancer diagnosis and treatment.<\/p>\n
Cervical cancer is the third most common and among the deadliest cancers in women worldwide, with a third of new cases occurring in China. Due to screening efforts and improved treatment championed by the government, cervical cancer has seen a steady decrease in death over the past few decades. However, the doctors are still in scarcity, especially in rural areas.<\/p>\n
\u201cIt is normal for us to stay past midnight looking at those samples.\u201d One doctor at Nanfang Hospital said, \u201cWe don\u2019t have enough people to read the number of samples we receive each day.\u201d<\/p>\n
In October 2017, China\u2019s State Council announced the aim of becoming a global leader in artificial intelligence, which worth $150 billion domestically by 2030. Given the government support, hospitals started to welcome AI startups to collaborate on developing the best in class medical imaging AI technologies. The country’s medical AI industry scale is estimated to reach 20 billion yuan in 2018, surging 53.8%. Nanfang hospital led the initiative in cervical cancer. The AI startup partners benefited from the large sample size of cervical cancer images (>100,000) to train their algorithms. It takes three very experienced doctors to collectively diagnose for hours to reach the accuracy of 83%, whereas the new technology Nanfang hospital and its AI startup partner has co-developed could reach an accuracy of 95%+ in just 2 seconds.<\/p>\n
\u201cIt is a learning process, not just for machines, but for doctors and software engineers.\u201d The doctors at Nanfang hospital hosted a month-long workshop to teach the CTO and lead engineers of their medical AI technology partner the pathology and etiology of cervical cancer.<\/p>\n
\u201cNone of us had biology or medical background. It was very difficult to understand. So, we went back, bought textbooks of molecular biology, and other medical books ourselves to read.\u201d The CTO of the medical AI startup said. \u201cAfter feeding the data, we need to teach the doctors how to read those results and describe how the machine learning and the algorithms work. If pathology is hard for us, coding is equally hard for them doctors. But once the doctors understand how it works, they provide tremendous value on how we could improve our algorithms.\u201d<\/p>\n
\u201cAI imaging is just the first step. What we envision is the hospital of the future, where machine learning is improving the patient\/doctor interface, diagnosis process, treatment, as well as drug discovery. Patients do not need to travel all the way to our hospital to get diagnosed. We\u2019d like to develop equipment that can help doctors to diagnose remotely and virtually.\u201d The department head of Nanfang hospital said.<\/p>\n
In the short term, Nanfang hospital wants to improve the accuracy of the image reading process by leveraging the AI startups. The biggest opportunity here is for those negative readings, given that majority of the readings will be negative, it drastically decreases the time needed from doctors. \u201c95%+ is not enough. We need the technology to be 99%+ accurate, if not 100%, to have real impact.\u201d The head of the department said.<\/p>\n
In the long term, Nanfang hospital wants to utilize machine learning and AI to solve the talent gap problem. The long hours of doctors nowadays make students turn away. With machine learning technologies, Nanfang hospital hopes to improve the work\/life balance of doctors, therefore to attract more talents. Nanfang hospital plans to build its own college of pathology, equip them with both the medical knowledge as well as cutting edge, disruptive technologies including machine learning and artificial intelligence.<\/p>\n
Machine learning is still in its early days, how to make it practical and scalable in the hospital context is still a big question that Nanfang hospital wants to tackle. \u201cThere is a knowledge gap between medical imaging AI vs. reality.\u201d The doctor said, \u201cBreast cancer is fairly easy to diagnose. Most medical AI companies developed their algorithms for breast cancer because it is not that hard, which in real life, does not add much value because there is not a bottleneck there. On the other hand, the harder problems, including pathology, really needs talent and technology to improve the accuracy and efficiency. \u201d Nanfang hospital is fully aware of the buzz around medical AI companies vs. real applications in hospital settings. In the median terms, I\u2019d recommend them to keep the focus on bringing along the software engineers to understand the medical field, to develop technologies that have actual impact.<\/p>\n
Do you think Nanfang hospital\u2019s grand vision to build hospital of the future using cutting edge technologies including machine learning is achievable?<\/p>\n
(800 words)<\/p>\n
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Das, R. (2018).\u00a0China And Israel Are Ready To Battle: Who Leads the Medical Imaging Artificial Intelligence Market?<\/i>. [online] Forbes. Available at: https:\/\/www.forbes.com\/sites\/reenitadas\/2018\/06\/26\/china-and-israel-are-ready-to-battle-who-leads-the-medical-imaging-artificial-intelligence-market\/#e8d99ae59fc3 [Accessed 13 Nov. 2018].<\/p>\n
South China Morning Post. (2018).\u00a0Can AI help fix China\u2019s ailing health care system?<\/i>. [online] Available at: https:\/\/www.scmp.com\/tech\/china-tech\/article\/2153144\/china-counts-ai-find-cure-its-ailing-health-care-system [Accessed 13 Nov. 2018].<\/p>\n
Sun, Y. (2018).\u00a0AI could alleviate China\u2019s doctor shortage<\/i>. [online] MIT Technology Review. Available at: https:\/\/www.technologyreview.com\/s\/610397\/ai-could-alleviate-chinas-doctor-shortage\/ [Accessed 13 Nov. 2018].<\/p>\n
Xinhuanet.com. (2018).\u00a0China Focus: AI beats human doctors in neuroimaging recognition contest – Xinhua | English.news.cn<\/i>. [online] Available at: http:\/\/www.xinhuanet.com\/english\/2018-06\/30\/c_137292451.htm [Accessed 13 Nov. 2018].<\/p>\n","protected":false},"excerpt":{"rendered":"
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