隨著電腦運算技術的進加上大量醫療資訊電子化之後，首先，大數據資料分析變得熱門，這部分可利用Google Trends來搜尋相關的關鍵字，可發現大數據的從2014年開始變得熱門，而人工智慧的搜尋度則是從2016年開始增加，再加上辨識技術能力的突破，深度學習及醫療影像輔助診斷如雨後春筍般增加，因此，醫療生態將進入另一波的演化。
大數據的分析結果影響了決策行為，醫療體系該如何面對人工智慧的這波浪潮，醫療輔助診斷系統及影像辨別系統是否能取代臨床醫師的工作，醫院經營者面對這巨大的變革該有何作為，面對深度學習與人工智慧的進攻，臨床工作者及醫院經營者應該有何策略來面對它及接受他，亦或是利用它來制定政策呢？正是本研究欲討論的課題。
醫療診斷是醫療照護最關鍵的步驟，此步驟出錯將會造成病患的治療失當或延誤，這也是醫師在執行業務過程最重要的能力，而這個能力會受到經驗不同而有所差別，因此如何減少診斷上的錯誤，則是醫師執業過程最需注重的能力。
人工智慧有快速、精確、可重覆性及低成本的特性，越來越受到各領域的重視，醫學日新月異講求實證醫學，透過電腦運算技術及能力，利用之前的醫學資料庫為本進行學習，將其運用在疾病診斷上，可以幫助臨床醫師快速整合，創造新的資訊及預測數據，亦可讓疾病診斷的準確度提高並快速，將其節省下的時間轉為對病患的關懷，減少病患及家屬在生病過程的憂慮，改善醫病關係。

With the advancement of computer technology and the digitization of a large amount of medical information, big data analysis has become popular. We can use Google Trends to search for relevant keywords. It can be found that the search volume of big data begun in 2014 has become popular, and the popularity for artificial intelligence has increased from 2016. Breakthroughs in deep learning and medical imaging-assisted diagnosis coupled with recognition technology capabilities have sprung up, so the medical ecology will enter another series of revolutions.
The analysis results of big data influence decision-making process. How the medical system should face artificial intelligence? Whether the medical assistant diagnostic system and image recognition system can replace the work of clinicians, and hospital management what should be done? In the face of the offense of deep learning and artificial intelligence, what strategies should clinicians and hospital management can to face and accept it, or use it to formulate policies?
Medical diagnosis is the most critical step in medical care. Make a mistake in this step will cause improper or delayed treatment of patients. This is also the most important ability of physicians in medical practice. This ability will be different according to clinician’s experience. Reducing medical mistakes is the most important ability for physicians to practice.
Artificial intelligence has many characteristics including fast, accurate, repeatable, and low-cost. It is getting more and more attention in various fields. Computing technology and capabilities are used to learn from previous medical databases. Applying it to medical diagnosis can help clinicians quickly integrate and create new information and then predict. It can turn the saved time into care for patients and improves the doctor-patient relationship.

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