In late 2019,UTHealth School of Biomedical Informatics(SBMI) Dean, Professor, and the Glassell Family Foundation Distinguished Chair in Informatics ExcellenceJiajie Zhang，博士，写的博客文章that made this analogy; “AI is to Medicine Today What the X-ray was to Medicine a Century Ago.” Since writing that blog, the health care industry has been turned upside down by the strain of the COVID-19 pandemic. However, technological advancement did not stop during the pandemic – a large part of it received a boost because of the pandemic. Over the past one to two years, AI applications have been developed and utilized at a much higher speed and at a much larger scale to help improve and transform how medical care is provided.
Dean Zhang在有关医疗AI的最新演讲中，更深入地了解了他做出类比的“ AI今天对医学的类比，一个世纪前对医学的医学是什么”。他还讨论了医学AI在历史和经济环境中的重要性，并使用具体的例子来证明医学AI从根本上转化医学和医疗保健。
To access the slide deck of Dean Zhang’s presentation,点击这里. For the transcript of the presentation in pdf,点击这里.
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Hi, everyone. I am Jiajie Zhang, Dean of the School of Biomedical Informatics at The University of Texas Health Science Center at Houston. Our school is one of the largest academic programs of biomedical informatics in the nation and the only one as a free standing school. We are located in the Texas Medical Center, the largest healthcare cluster in the world. At our school, we do education, research, and application in Medical AI and Data Science, Clinical and Health Informatics, and Bioinformatics and Systems Medicine.
Today, I talk about Artificial Intelligence applications in medicine. I want to tell you whyAI is to Medicine Today What the X-ray was to Medicine a Century Ago, and Much More…
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这是我将要经历的子主题：
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医疗AI是21世纪的X射线。
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一个世纪前，X射线使医生能够看到体内的无形结构。
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Today, AI is enabling doctors to not only see, but predict, previously unidentified patterns within massive medical and biological data. This is a futuristic picture of what medicine in the hi-tech world looks like. The key physiologic states of the body are patterns of data that intuitively visible and easily actionable, with the touch of a finger. The data are dynamic, can go down to molecular levels and can go up to population levels. The data can also go back in medical history and go forward as predictions of health status of the patient. Diagnosis can be made and treatment plans can be generated, with the help of artificial intelligence.
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Artificial intelligence, since its beginning in the 1950s, promised a lot of things. But nothing really worked, including expert systems for medicine, which is a big part of early AI. Today, three drivers made AI a reality and capable of solving real world problems, sometimes better and faster than people. These three triggers are massive, massive data in electronic and computable forms, powerful and affordable computing, and universal connectivity.
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Let us take a look at what happened over the past 5 to 10 years. The amount of data are increasing exponentially; for medicine, adoption of electronic health records, or EHR, is almost universal. The cost of sequencing the entire human genome dropped from the cost of a Boeing 737 to the cost of a smartphone. Of course the speed to internet connection and the speed of computing are both massively increased. Whether you talk about Industry 4.0 or Health 2.0, we are at a historically unique moment – the cognitive revolution that is liberating people from the cognitive labor. This is as fundamental as the industry revolution that started more than 200 years ago and liberated people from physical labor; and the agricultural revolution more than 10 thousand years ago that provided people with food security through the transformation from hunting and food gathering to farming.
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US Senator Ben Sasse summarized the moment we are in today very nicely in an article in the Wall Street Journal: “The past 20 or 30 years, and the next 20 or 30 years — really is historically unique. It is arguably the记录的人类历史上最大的经济破坏.” Computing technology has transformed or has been transforming all major industries, from information access, communication to retail, entertainment, to travel, finance, and to knowledge intensive education.
今天，它终于进入了医疗保健行业，从根本上改变了我们如何治疗患者，如何做出诊断，如何预防疾病以及如何做出生物医学发现。
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医疗AI应用程序列表正在迅速扩展。成像分割，成像注释和基于成像的诊断正在成为AI应用的早期操场。预测是机器学习的另一个主要优势。例子包括在常规算法检测到败血症之前的预测，预测疾病进展，计算各种疾病（例如心肌感染）的风险和心力衰竭，发现计算生物标志物以检测诸如帕克森（Parkinson）等医疗状况，例如在键盘上打字。当然，自然语言处理是AI的非常活跃的应用领域。医疗AI系统甚至可以参加医疗许可考试，表现高于80％或更多的人类接受者。该列表不断，可能是无限的。让我们看一下我们在休斯顿的得克萨斯大学健康科学中心的生物医学信息学研究人员开发的一些医学AI应用程序。beplay苹果手机能用吗
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Developing a new drug typically takes more than 10 years and costs more than a billion dollars. If an approved drug on the market for one disease has functions that can be potentially used to treat a different disease, that is, repurposing an existing drug, it will benefit the patients and the medical community. Medical AI can help with discovering the signal for a new disease through data mining of massive medical records.
华许博士,通过使用自然语言公关ocessing and data mining over millions of patient records, discovered that Metformin, which is one a front line drug for type 2 diabetes, has a potential impact on cancer treatment. He compared cancer patients who are either diabetic or not diabetic, whether they use metformin or other diabetic drugs, and discover that diabetic cancer patients who took metformin for diabetes had a much better five year survival rate than diabetic cancer patients who took other diabetic drugs and even cancer patients who are not diabetic.
只有使用大量患者数据和使用高级AI工具的可用性，这种发现才有可能。
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If you have the medical records of 50 million people for over ten years, what can you do with it? A lot! Dr. Degui Zhi used this type of dataset from Cerner Corporation to predict the risk of heart failure onset. Heart failure is a medical condition where the heart can't pump enough blood to meet the body's needs and eventually lead to death. In 2016, there are 5 million heart failure patients in US and the healthcare cost for them was $30 billion dollars. Early prediction and early prevention and treatment is the key for taking care of these patients. Dr. Zhi and his team used a deep recurrent neural network to develop a predictive model that performed really well, with an AUC around 79 to 85%.
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卢卡Giancardo博士发明了一种机器学习del to identify computational biomarkers from the movement of hands in typing on the keyboard and touching on a smart device. He was able to use the signal to identify whether a patient has Parkinson’s disease or not. This is a potentially very useful tool to do early detection and for tracking of progression of Parkinson’s disease.
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Sepsis is the bacteria infection of the blood stream. It is the leading cause of death in U.S. hospitals. 1 patient dies every 2 minutes in the US—more than breast cancer, prostate cancer and HIV combined. The good news is that 80% sepsis deaths preventable. Mortality increases 8% for every hour that treatment is delayed. So early detection of the onset of sepsis, or better yet, early prediction well before it can be detected by the current standard of care algorithms, is going to save a lot of lives. Dr. Xiaoqian Jiang, a machine learning researcher, collaborated with Dr. Bela Patel, an ICU physician, and Dr. Robert Murphy, an ER Physician, developed a machine learning algorithm based on deep LSTM that can predict the onset of server sepsis 4 hours ahead of the time. The AUC of the model achieved 92%, better than the status quo of 85% achieved by other models. Their model is currently being validated for potential deployment at our teaching hospital, Memorial Hermann Hospital at the Texas Medical Center.
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The “Eyes Are the Windows of the Soul,” as we often hear. For medical AI researchers such as Dr. Luca Giancardo and his physician collaborator Dr. Sunil Sheth, the “Eyes Are the Windows of Health.” Retina images are typically used to diagnose eye diseases. However, they are actively developing an AI system that can use the retina image to detect signs of stroke. CT imaging is the standard of practice for stroke diagnosis. In comparison with CT machines which are very expensive and not mobile, the device for taking retina images is very small, portable, and inexpensive. The AI technology of using retina images to diagnosis stroke and other medical conditions has a lot of potential.
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With the development and refinement of many high level machine learning packages such TensorFlow, Trax, and PyTorch, developing medical AI applications is becoming incredibly fast and easy. With unlimited coffee and food, students, both undergraduate and graduate, can develop machine learning algorithms that can solve real world medical problems within 24 hours. Let me show you a few Datathons that our school has hosted under the leadership of Drs. Xiaoqian Jiang, Yejin Kim, and Shayan Shams.
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在这个2019年马拉松”,学生the task of predicting Sudden Unexpected Death in Epilepsy, or SUDEP. For epilepsy patients, after a seizure, there is a change of dying and this is indicated by the onset of slow activity of their EEG signals. The students were given the EEG data of real patients and asked to build a machine learning model to predict the onset of the slow wave. The best model from the students achieved an AUC of 84%, which is quite good. The student projects from the Hackathon led to five publications in a BMC special issue. Another amazing accomplishment.
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Just like many people in the nation and in the world, when the COVID pandemic hit, we tried to help to deal with the pandemic with our expertise. We organized a student COVID-19 Datathon. The task is to Predict COVID-19 hospitalization and mortality in Houston Metro Area.
The students were given data about
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In the most recent Datathon we hosted last month, the students were asked to build a machine learning model to predict how well a stroke patient recovers over time.
Specifically, the students need to develop algorithms to predict changes in cognitive and Functional Independence Measure scores. Again the models the students developed performed well.
在24小时内使用真实患者数据解决现实世界的医疗问题；通过本科生和研究生，其中一些人只学到了从简介到机器学习课程的基础知识。
This is exciting; but think about it, it is also kind of scaring. Having a model that makes good predictions, however, does not mean that makes an impact on the patients.
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医疗AIis still very hard.
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Most academic (and industrial) medical AI products never get deployed. There are many reasons why. After research and development, and after validation, both internally and externally, a medical AI product is ready for testing in the real clinical setting. However, getting the product into the production environment is one challenge. How to get it into the clinician's workflow is another, bigger challenge.
该产品的临床和商业公用事业是什么？如何在内部和外部进行监管过程，例如FDA？如何通过新算法修改的新数据来保持产品更新？患者安全问题呢？列表还在不断。让我们看一个真实用例，以了解将医疗AI产品进入临床环境的复杂性。
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这种情况是关于中风的CT成像。
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There are two types of stroke: ischemic stroke that is the blockage of the blood vessel and the hemorrhagic stroke that is the breakable of the blood vessel. Ischemic stroke accounts for 87% of stoke and there is a therapy that can save a patient’s life. This therapy is called endovascular stroke therapy, or EST, which is to insert a stent retriever from the groin all the way to the brain to remove the blood clot. CT Perfusion, or CTP, is the definitive guide to determine whether a patient is eligible for this procedure. CTP is an advanced procedure that is not widely available at smaller facilities. A relative simpler technique called CT Angiogram, or CTA, is more widely available but it is typically not good enough to determine EST eligibility. Dr. Giancardo, in collaboration with neurologists Dr. Sheth and Dr. Sean Savitz, developed a machine learning model that can use CT Angiogram to do what CT Perfusions’ job.
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他们成功地开发了一个基于CT血管造影的AI系统，称为DeepSymnet，其性能水平非常好。该系统从患者的ER到达到治疗决策的时间大大减少了，因为AI系统仅需1分钟即可阅读CT并将报告发送给医生进行治疗决定。
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The system generates an alert after image analysis by the AI system and sent it to the stroke team by email that has an EST eligibility score along with the CT images.
The development and implementation of this system recorded a very fast “bench-to-bedside” time. It took a year to go from idea to implementation in the hospital and this is very fast.
现在的管道系统集成4 hospitals and it is functioning in near real time. As we can see, this AI pipeline sends alert by email which is outside of the EHR platform. Getting this system integrated into the EHR platform and the workflow of the clinicians will be next step, which is non-trivial and may well be the major barrier to its wide adoption.
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A technically capable AI product needs to be deeply integrated into the clinical environment and it needs to show it’s clinical, operational, and financial utilities before it can be widely adopted to generate meaningful impact on patient care. This deep integration requires a lot more than AI algorithms. It requires an entire discipline called biomedical informatics; and it requires an insider’s job, that is, researchers and clinicians in healthcare institution.
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生物医学信息学研究数据的获取，存储，通信，处理，集成，分析，采矿，检索，解释和数据的介绍，并确定如何转换数据，这些数据是毫无意义是经过验证的信息，以及智力是可行的知识，目的是解决预防疾病，医疗保健提供和生物医学发现方面的问题。
生物医学信息学covers the entire spectrum of biological scales—from small molecules, genes, proteins, and cells, to tissues and organs, to individuals and populations. Biomedical informatics is a highly interdisciplinary field focused on collaborations with partners in clinical practice (e.g., medicine, nursing, dentistry, pharmacy); the biomedical sciences; public and community health; computer science and engineering; mathematics and statistics; cognitive science; social and behavioral sciences; healthcare management; and health IT policy and law.
Data Sciencefor medicine is a subfield of Biomedical Informatics; it focuses on all aspects of data for disease prevention, healthcare delivery, and biomedical discovery.医疗AI反过来又是健康数据科学的子领域，它的重点是机器学习，模式识别，计算表型和预测建模。
Our School of Biomedical Informatics at the University of Texas Health Science Center at Houston is the only free-standing school among 70 or so such programs in the nation, and it is one of the largest internationally.
AI革命有望成为一个令人兴奋的时代。由于几乎无限的潜力，医疗AI正在迅速发展，以产生更多越来越高级的临床应用，这些应用将大大改善患者护理，预防疾病和生物医学发现。我们为成为医疗AI的领导者而感到自豪。健康数据科学和生物医学信息学。It’s great to be part of that transformation!
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Thank you for your time. Hope you enjoyed this presentation. If you want to learn more about medical AI, health data science, biomedical informatics, or the education programs at our school, you can reach me by email or follow me in social media. Thank you.