Traditional Chinese Medicine (TCM) is a natural, safe, and effective therapy that has spread and been applied worldwide. The unique TCM diagnosis and treatment system requires a comprehensive analysis of a patient's symptoms hidden in the clinical record written in free text. Prior studies have shown that this system can be informationized and intelligentized with the aid of artificial intelligence (AI) technology, such as natural language processing (NLP). However, existing datasets are not of sufficient quality nor quantity to support the further development of data-driven AI technology in TCM. Therefore, in this paper, we focus on the core task of the TCM diagnosis and treatment system -- syndrome differentiation (SD) -- and we introduce the first public large-scale dataset for SD, called TCM-SD. Our dataset contains 54,152 real-world clinical records covering 148 syndromes. Furthermore, we collect a large-scale unlabelled textual corpus in the field of TCM and propose a domain-specific pre-trained language model, called ZY-BERT. We conducted experiments using deep neural networks to establish a strong performance baseline, reveal various challenges in SD, and prove the potential of domain-specific pre-trained language model. Our study and analysis reveal opportunities for incorporating computer science and linguistics knowledge to explore the empirical validity of TCM theories.

In the fall of 2018, corporate finance advisory firm Hampleton published a report titled, "The healthtech sector is currently one of the most dynamic in technology M&A." As a summary of the report notes, "aging populations, increasing patient demands and the rise of lifestyle diseases, coupled with pressure on the costs for delivering care are forcing healthcare providers to innovate to improve the quality of their services and lower their prices." Those innovations are made possible by technologies that range from blockchain and artificial intelligence to big data analysis and advanced sensors. IoT connectivity plays a key role, too. And data is central, but not on its own.

AI is transforming the practice of medicine. It's helping doctors diagnose patients more accurately, make predictions about patients' future health, and recommend better treatments. As an AI practitioner, you have the opportunity to join in this transformation of modern medicine. If you're already familiar with some of the math and coding behind AI algorithms, and are eager to develop your skills further to tackle challenges in the healthcare industry, then this specialization is for you. No prior medical expertise is required!

"Listen to your patient; they are telling you the diagnosis," an aphorism attributed to Dr. William Osler, the founder of modern medicine, still holds true today. The disappearance of patients' stories from electronic health records could be one reason that artificial intelligence and machine learning have so far failed to deliver their promised revolution of health care. The medical industry's fascination with artificial intelligence is understandable. Advancements in medicine have dramatically improved patient outcomes, and there is every reason to believe that machine learning, deep learning, artificial intelligence, and the like will do the same. But before we jump on the AI bandwagon, I offer this caution: consider the source of the data it is dependent on.

"Listen to your patient; they are telling you the diagnosis," an aphorism attributed to Dr. William Osler, the founder of modern medicine, still holds true today. The disappearance of patients' stories from electronic health records could be one reason that artificial intelligence and machine learning have so far failed to deliver their promised revolution of health care. The medical industry's fascination with artificial intelligence is understandable. Advancements in medicine have dramatically improved patient outcomes, and there is every reason to believe that machine learning, deep learning, artificial intelligence, and the like will do the same. But before we jump on the AI bandwagon, I offer this caution: consider the source of the data it is dependent on.

SHANGHAI – A crowd gathers at a Shanghai hospital, queuing for remedies made with plant mixtures and animal parts including scorpions and freeze-dried millipedes -- medicines that China hopes will find an audience overseas. With a history going back 2,400 years, traditional Chinese medicine is deeply rooted in the country and remains popular despite access to Western pharmaceuticals. Now the authorities are hoping to modernize and export the remedies, but they face major obstacles. Some leave with boxes of pills, others take away plastic sachets filled with herbal extracts. Lin Hongguo, a 76-year-old pensioner, has bought herbal remedies that he will boil to make a tea to treat his "slow beating heart."

When we think of breakthroughs in healthcare, we often conjure images of heroic interventions -- the first organ transplantation, robotic surgery, and so on. But in fact many of the greatest leaps in human health have come from far more prosaic interventions -- the safe disposal of human excrement through sewage and sanitation, for example, or handwashing during births and caesarians. We have a similar opportunity in medicine now with the application of artificial intelligence and machine learning. Glamorous projects to do everything from curing cancer to helping paralyzed patients walk through AI have generated enormous expectations. But the greatest opportunity for AI in the near term may come not from headline-grabbing moonshots but from putting computers and algorithms to work on the most mundane drudgery possible.

However, what seems to be great news at first poses some huge challenges for current and upcoming generations. For instance, Eurostat found out that while population and average span of life have increased, the years spent in good health have not. This means a lot more people will rely on several extra years of medical support. Especially, degenerative diseases like dementia will become growing issues. For instance, the World Health Organization (WHO) estimates that the number of people living with dementia will increase from currently about 47 million to 75 million by 2030 and triple by 2050. The projection of such stark reality raises a number of questions about the future of healthcare.