1. Introduction   

Beginning with Emperor Yan, followed by Shen Nong tested a hundred varieties of herbs, then operations  and powder for anesthesia invented by Hua Tuo, to the <Compendium of Materia Medica> which is written by Li Shizhen. TCM(Traditional Chinese Medicine) is changing in a visible way while some other trades are replaced by AI(Artificial Intelligence). The society is advancing and transforming fast. It is important for the obsolete professions to reconcile and fuse with new technologies.Traditional Chinese Medicine, is a style of traditional medicine built on a foundation of more than 2500 years of Chinese medical practice that includes various forms of herbal medicines, acupuncture, massage, exercise and dietary therapy. Artificial Intelligence, is apparently intelligent behaviors by machines, rather than the natural intelligence of humans and other animals. When people see doctors of TCM, they go for the old ones because of their experience, this lead to a lack of experience to the young ones and result in a higher possibility of false diagnosis. Doctors of TCM get skills from their master who work as a doctor of TCM for a long time. However, there may be some faults during the knowledge passing. On the other hand, AI do not forget or have any mistakes. Treatments will be much more individuation and can prevent the happening of illness but not treat them after it happens. This essay will first introduce the AI TCM doctors as well as compare them with the human ones, then analyze data that collected in the form of questionnaires, and finally predict the future of AI and TCM.

2. Literature Overview

2.1 Artificial Intelligence in Medicine(AIIM)

I will briefly start with the meaning of ‘medicine’ itself, the term Medicine is defined by Merriam Webster as the science and art dealing with the maintenance of health and the prevention, alleviation, or cure of disease. In the early days of AI in medicine, the biggest challenge was the modeling of knowledge and of reasoning techniques for the purpose of supporting tasks as diagnosis, therapy, and monitoring[2]. However, in present, the major challenge is the intelligent exploitation of heterogeneous data, including demographic data, temporal clinical and health data, biomedical signals and images, genetic data, bimolecular data, clinical pathway data, social network data. Knowledge related to these data is valuable and multifaceted for computer/AI to do deep study. In this direction, intelligent(information) systems become a key-element to support decision-based and data intensive tasks as diagnosis, therapy, prevention, monitoring of patient populations, care quality assurance, and healthcare policy assessment and definition. [3]

In spite of the shift in focus toward data-intensive applications that provide ad-hoc information for medical decision-based tasks, the ultimate objective is still the same, namely to support care providers in reaching the best possible decisions for any patient at the right time, to help them see through the consequences of their decisions/actions, and to improve and widen their knowledge and comprehension of clinical phenomena.

2.2 The role of Artificial Intelligence in Precision Medicine

The essence of practicing medicine has been obtaining as much data about the patient’s health or disease as possible and making decisions based on that. Physicians have had to rely on their experience, judgement, and problem-solving skills while using rudimentary tools and limited resources. Instead of developing treatments for populations and making the same medical decisions based on a few similar physical characteristics among patients, medicine has shifted toward prevention, personalization, and precision. These cannot be done without AI. [7]

2.3 AI in Clinical practice

The AI research branch of the search giant, Google, launched its DeepMind Health project, which is used to mine the data of medical records in order to provide better and faster health services. In 2016, they launched a cooperative project with the Moorfields Eye Hospital NHS Foundation Trust to improve eye treatment [4]. IBM Watson launched its special program for oncologists to provide clinicians with evidence-based treatment options.[5] A Dutch company, Zorgprisma Publiek, analyzes the digital invoices of hospitals and insurance companies and uses IBM Watson in the cloud to mine the data. They can tell if a doctor, clinic, or hospital makes mistakes repetitively in treating a certain type of condition in order to help them improve and avoid unnecessary hospitalization of patients [6]. This is exactly what AI doctors are, this kind of analyzing technology is used in many different trades.

2.4 The Modern TCM with AI

Started from 1980s’, Chinese scientists had tried to use AI in TCM. However the technology back then was not that advanced yet, experts could only build a limited knowledge base like an encyclopedia, those AI could not do any inventive diagnosis or prescript. The first generation of AI TCM doctor end unsuccessfully. [8]

In recent years, modern technologies allow scientists to make some more advanced AI doctors. After the first failed, Chinese experts change a way to build AI TCM doctors. AlphaGo won top Go players because of the large amount of knowledge an information, which is called ‘big data’. Big data allow AI to store informations they need to do their jobs. However, an AI TCM doctor cannot be used with only big data, it also needs deep learning. Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised. The more AI learn, the more they are ‘smarter’. AI TCM doctors are mainly in a computer. They can give diagnosis to a patient through his/her heart beat, outlook, expressions and tell the human doctor. Human doctors will also analyze whether the prescript is perfect for the patient to use. This is how an AI TCM doctor works.

3. Methodology

Questionnaires will be spread out in both friend circles of my mother and me. Data will be collected through Internet with many details. Research samples are about 50 people from all ages with different educational background, most of them are from Guangzhou. Guangzhou is one of the most developed cities in China, which exists both conventional and modern cultures that allow people to understand more about traditional Chinese ‘science’ as well as the modern technology. Five questions will be included in the questionnaire, varieties of questions will be in the questionnaire. Key questions which are more useful to the article will be analyzed. Data that collected from the questionnaire will be show by different kinds of charts. The answers I expected is that answerers can choose and write answers which is value and useful to the research.

4. Outcomes and Value: 

4.1 How often do you go to see a TCM doctor?

go when get ill                                 57%

go once or twice for half a year         13%

never pay any visit to a TCM doctor   11%

other possibilities                             20%

The first question is showed above. As we can see from the data, more than half of the answerers go to see a TCM doctor when they are sick. Around 10% of them had never pay any visit to a TCM doctor. The graph tells me that most of people have no conscious that the purpose of a TCM doctor is not to cure a patient, but to nurse healths of people. There is another appliance in TCM doctors of AI is a monitor which can judge health of a person when he/she is looking in to a mirror or singing a song, it is like a daily health insurance.

4.2 What type of TCM doctors would you like to see when you go to see a TCM doctors?

experienced ones                                77%

not experienced ones                            1%

whatever                                           11%

never pay any visit to a TCM doctor      11%

The pie chart illustrate that almost 80% of the answerers would like to pay a visit to those     experienced TCM doctors. At the beginning of the essay, I mentioned that people have a solid thought is that experienced TCM doctors have lower possibilities to make mistake. In fact, experienced ones are actually better, but the young ones have good capability as well. Human make mistakes while AIs do not. They can do precise calculation to avoid mistakes and may give the same or better diagnosis and prescript than human ones. So, AI TCM doctors can used as a main doctor as well as an assistant of TCM doctors.

The research I did might can introduced the concept of AI TCM doctor to a part of people, to tell them that AI TCM doctor is no longer so far away from us. The research can also spread TCM ideas, to let people remind what they have left behind them. In my opinion, traditional science should not be abandoned, their existence and wide-ranging among people is reasonable. In present, the way to make TCM alive is to fuse with AI, to help and serve people in a better way.   

5. Limitations

The number of the sample might be a boundary that is not enough to represent. What is more, the time of the research is not that enough. Besides, the samples are not that clear or professional with both AI and TCM, for example, old ones might not know about new technology such as AI while young ones do not pay a visit to a TCM doctor for a long time. As a consequence, answers they answered may not be useful or valuable to analyze. 

The culture is disappearing rapidly and some of them might have been swallowed by the new technologies. That is why we have to protect them while they are hugging the modern world to improve themselves as well as stay alive.

References

[1] Merriam Webster Dictionary, merriam-webster.com/, [accessed 04.01.17.

[2] Patel VL, Shortliffe EH, Stefanelli M, Szolovits P, Berthold MR, Bellazzi R, Abu-Hanna A. The coming of age of artificial intelligence in medicine. Artif Intell Med 2009;46(1):5–17, dx.doi.org/10.1016/j.artmed.2008.07.017.

[3] Carlo Combi.Editorial from the new Editor-in-Chief: Artificial Intelligence in Medicine and the forthcoming challenges[J].Artificial Intelligence in Medicine.2017,76：37-39.

[4] Moorfields announces research partnership [Internet]. Accessed online on the 25th of July, 2017 from: moorfields.nhs. uk/news/moorfields-announces-research-partnership 

[5] Motzer RJ, Barrios CH, Kim TM, et al. Piloting IBM Watson Oncology within Memorial Sloan Kettering’s regional network. J Clin Oncol [Internet]. 2014;32(25):2765–2772. Available from: meeting. ascopubs.org/cgi/content/abstract/32/15_suppl/e17653?sid= ad2b6d30-ec22-453b-8a8c-67c23fbd7f85%5Cnovidsp.ovid. com/ovidweb.cgi?T=JS&PAGE=reference&D=emed12&NEWS= N&AN=71523844

[6] Zorgprisma Publiek [Internet]. Accessed online on the 25th of July, 2017 from: https: www.zorgprismapubliek.nl/

[7]Bertalan Mesko.The role of artificial intelligence in precision medicine[J].Expert Review of Precision Medicine and Drug Development.2017,2(5)：239-241.

[8]黄欣荣，钟平玉，马纲.人工智能与中医智能化[J].中医杂志.2017,(24)：2076-2079，2106.

Appendix

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