系統將從蹣跚學步的嬰兒逐漸成長、成熟，達到高效

????不要指望電腦一夜之間就會成為頂尖的診斷系統。它們一開始可能只是小步創新，或是看起來還很笨拙，沒有到大顯身手的時候。

????想象一下，使用AliveCor*推出的iPhone程序，每天只需不到1美元就可以做一次心電圖。與許多病人一年只在醫院做兩次心電圖相比，這類設備獲取的信息量明顯要多得多，而且費用也便宜得多。可能你做500次“自動診斷式”心電圖，都比在醫院里做一次還要便宜。今天，大多數心臟病都是在患者突然發病后才得到確診。但是有了能夠鑒定異常情況和能夠預測發病的自動學習軟件，人們就能獲得預防性的心臟治療。我們可以在突然發病或中風前，就發現大多數的潛在心臟病。而且，與發作后的治療相比，付出的醫療成本也少得微不足道。不過我們還需要幾十年的數據積累，才能實現這個目標。

????皮膚病的診斷可以交給CellScope公司生產的低成本的iPhone配件來完成，它可以掃描皮膚上的痣、皮疹，以及耳部感染，將來可能還可以掃描喉嚨和視網膜。通過計算機算法，可以對圖像進行處理，從而做出最接近的診斷。Eyenetra公司生產的一款設備，可以對人們的眼睛進行驗光，幫助人們配眼鏡，省去了許多費用和奔波的麻煩。Adamant公司目前正在研制一款芯片，能檢測人們呼吸中的幾百種氣體，因此可用來檢測甚至確診幾種不同的肺癌。它們都比現在的大型CT強得多，因為后者折騰一番之后，只會告訴你，你長了一個瘤。Ginger.io的產品可以監測人們發電子郵件、微博、短信和打電話的頻率，通過人們的社交活動，研究人們的行為變化，它可以比一個精神病專家更好地反映人們的精神狀態。

????這些創新在一開始可能顯得無關緊要，但日積月累，它們會日益壯大，形成一場革命。屆時，拿今天的科技與2020年的科技相比，就好像拿1986年磚頭一樣的大哥大與今天的iPhone相比一樣！

人的因素仍將存在

????有些質疑自動化醫療的人士指出，醫學不僅僅是輸入癥狀、輸出診斷那么簡單，醫學是建立在醫生和病人之間互動關系上的學問。人類能比機器提供更好的病床護理，更好地回答病人的問題。這當然是實情，不過一般來說，我們不一定非得拿到醫學學位才能做到這一點。許多護士、護理師、社工以及其他收費更低的非專業醫生也能做到這一點，甚至有不少人比醫生做得更好，而且他們會花更多的時間來提供個性化、有愛心的護理。筆者并不是在這里鼓吹讓醫生下崗，而是說，我們應該通過先進的機器學習和人工智能技術，打造強有力的后臺傳感器技術和診斷技術，讓它們來處理人力所不能及的龐大信息量。

Systems will start as clumsy toddlers and develop to maturity and efficiency

????Don't expect ace diagnosis systems overnight. They may start as seemingly minor point innovations or as clumsy-sounding systems not ready for prime time.

????Imagine using the AliveCor* iPhone case to take an ECG every day for less than $1/test. This device and others like it would capture a lot more information than the typical heart patient's semiannual ECG check at the doctor's office (it would also cost a lot less). What if you could send 500 "auto-diagnosed" ECGs to your doctor for less than it costs to get one ECG done in the hospital? Today, most heart disease is identified only after patients have heart attacks. But imagine having preventative cardiac care, enabled by machine-learning software that identifies abnormalities and?predicts?episodes. We could discover most heart disease before a heart attack or stroke and address it at a fraction of the cost of care that would be needed following such a trauma. But we need a decades-worth of data to be really good at it.

????Dermatology appointments could be handled by CellScope*, which produces low-cost iPhone attachments for imaging skin moles, rashes, ear infections, and (in the future) your retina or throat. Those images could be processed by algorithms to detect patterns that warrant closer inspection. A device like the Eyenetra* could give you an eye test and fit you for glasses at little cost or hassle. Adamant* is attempting to produce a chip that can identify hundreds of gases in your breath, which could be used to detect and even?identify?different types of lung cancer, all for far less than a big CT scanner that'll just tell you that you have a nodule. Ginger.io* monitors your rate of emailing, tweeting, texting, and calling to gauge your social activity. By watching for changes in your behavior, it can tell how you're doing far better than a psychiatrist.

????These point innovations will seem immaterial at first, but, when there are enough of them, they will integrate and start to feel like a revolution. The technologies of 2020 will be as different from today's systems as the car floor-mounted, multi-pound cell phones with bulky handset cords of 1986 are from today's iPhones!

The human element will survive

????Some critics of more automated healthcare argue that medicine isn't just about inputting symptoms and receiving a diagnosis; it's about building relationships between providers and patients. Providing good bedside manner and answering certain questions can often be handled better by a person than a machine, but you generally don't need a medical degree to do that. Nurses, nurse practitioners, social workers, and other less expensive, non-MD caregivers could do this just as well as doctors (if not better) and spend more time providing personal, compassionate care. I'm not advocating the removal of the human front-end here. I'm arguing that we should build robust back-end sensor technology and diagnostics through sophisticated machine learning and artificial intelligence operating on data in greater volumes than humans can handle.