I grew up listening to bands like Black Flag, Minor Threat, Bad Brains, and the Ramones as a teenager. I remember the first time I heard Damaged by Black Flag when I was 16 years old. The music was energetic, raw, and perfect for someone with a short attention span. The only problem was that this music was recorded more than a decade before I was even born. Punk was not in vogue during the time of the mid 2000’s when I was a teenager. It was rappers with lyrical talent like 50 Cent, Eminem, and Jay Z who were in the mainstream. And don’t get me wrong — there is still really great contemporary punk rock music like Code Orange, Turnstile, and Knocked Loose. But these bands are only popular among people who listen to punk rock. It’s not on the radio. It’s not mainstream. But something interesting is happening in today’s music landscape. Punk is in the spotlight again. This time, however, it’s coming from hip hop artists like Lil Pump, XXXTentacion, and 6ix9ine. For the first time in my life I feel invested and personally connected to mainstream music because it reminds me so much of old school hardcore. Here are some of the reasons why SoundCloud Rap is the new punk.

Gucci Gang Goes Viral

Last year hip hop took the throne as the most popular music genre in the US for the first time. With hip hop becoming increasingly commercialized a group of hip hop misfits have created a very DIY “I don’t give a fuck” sound that is more similar in spirit to the Sex Pistols than Kanye West. The Sex Pistols are probably the most famous embodiment of what “punk” rock means: be self reliant and do your own thing. Who cares if people say you can’t sing or play your instrument. Just get on the microphone and say something. Eventually you’ll get better. Today we have rappers like Lil Pump whose lyrics are so simple that they sound like he spent less than 5 minutes working on them. I mean come on — have you heard the lyrics to D Rose? He literally says “100 on my wrist 80 on my wrist” over and over again. Consequently he’s gotten a lot of criticism for not being able to rap. But Lil Pump doesn’t give a shit that he’s not lyrical. He wants his songs to sound mad “itnorant”. Lil pump started rapping when he was 16 and became famous after uploading his early music to SoundCloud. In this day and age young rappers have bypassed the traditional music establishment. They don’t need a record label to be heard. All they need is a phone and a wifi connection. And just like back in the day when punk rock tock over popular music, the same thing is happening in rap now. Lil Pump is one of the hottest artists in the entire world. His comically simple Gucci Gang has been streamed on YouTube more than 800 million times. Now hip hop’s mainstream players are embracing the new wave of punk rap. Kanye West did a track with Lil Pump called “I love It”. Nicki Minaj did a song called FEFE with the one and only “King of New York/Europe” Tekashi 6ix9ine.

Fashion and Face Tats

The ethos of punk rock fashion back in the day was stand out as much as possible. Musicians wore mohawks, ripped t shirts, and drew attention for dying their hair bright colors. Well those colors are back with a vengeance. Soundcloud rappers are covered in face tats which scream “don’t hire me”. The biggest SoundCloud rappers of our time including Lil Pump, 6ix9ine, and XXXTentacion all have ridiculous face tats. It’s almost as if there’s some unspoken competition of who can have the most ridiculous forehead tattoo (the winner is probably 6ix9ine). It’s not uncommon for these artists to wear chokers, makeup, and paint their nails — in other words they’re not afraid to connect with their feminine side. All of these rappers also have crazy dyed hair with 6ix9ine having his hair dyed every color of the damn rainbow.

Disregard for the Music Establishment

Lil pump beefed with J Cole. 6ix9ine beefed with practically everyone in the music industry including Ebro of Hot 97. On his new song Stoopid, 6ix9ine publicly disses the famous NY radio personality. While the old-heads may disregard these young rappers making moves (lil pump is only 18 years old of this writing), the youth freaking love them. Their primary audience is young people just like punk rock back in the day was intended for the young and disenfranchised. In an age where music on the radio was a formulaic 4 minutes, punk rock artists fought this by playing faster and recording songs with shorter length. These new SoundCloud rappers have songs that are shorter, full of energy, and aggressive. XXXtenteacion’s Look at Me is only 2:07 while Gucci Gang by Lil Pump is only 2 minutes as well. At their shows mosh pits and crowd surfing are the norm. Almost akin to the era of the Cro-Mags where the audience was encouraged to get on stage and crowd surf. In fact it’s interesting that the SoundCloud rappers stage dive and crowd surf during their own sets. It’s less about the musical or lyrical dexterity and more about the energy as frequently during these shows the rappers rap along with their music playing on the speakers.

DIY

The man who has given a huge voice to emerging rap artists and SoundCloud savants is the one and only Cole Bennett. This man is only 21 years old and started making videos after taking a class in high school. He started a whole music production and events company while still in high school. He started making music videos and got a shit ton of clout for making music videos for not only Lil Pump, Lil Xan, and 6ix9ine but blowing up a bunch of other people’s careers. You don’t need a college degree or years of training to make shit. Just go out and do it.

SoundCloud Rap Ain’t Here for Forever

People are constantly saying punk is dead. But that couldn’t be farther from the truth. Punk is thriving and enjoying mainstream airplay. Punk is covered in face tats and head-banging with colorful hair. Punk is not knowing how to rap or sing but just yelling into a microphone. Punk is not doing what people tell you to do. Punk is Lil Pump, 6ix9ine, XXXTentacion and rappers like them. I know this trend of SoundCloud punk rap won’t be popular forever, but for now I am enjoying the hell out of it.

The NoiseBridge

FREE. For teams working on technical and creative projects. Located in the Mission. Frequently hosts meetups, classes, events, and hackathons. They survive of donations so if you use the space be generous and help them out. Open from 11:00 AM -10:00 PM everyday.

The Laundry

Space for creatives and artists. Looks great for brainstorming sessions. FREE. Good for startups that are still in the idea phase and research phase.

AWS Loft

525 Market Street, 2nd Floor. Free. M-F. 9:30 A.M. — 5:30P.M. FREE. Great for early technical startups doing a lot of software. Get answers to AWS related questions for free and attend Amazon events.

Anchor

Located in the CBD. Dedicated desks start at $250/month. Lots of different stuff included including space for hosting meetups. 24/7

PARISOMA

Located on 11th street next to Uber HQ. Has free Philz coffee, a bunch of free events, and unlimited printing. Flexible seating starts at $325/month. Dedicated desks start at $495/month. Open 20/7.

ConnectionsSF

Located in the financial district. 24/7 access to a shared desk starts at $400. Private desks start at $600. Comes with free coffee and snacks. Also has space for larger teams.

Runaway

Located on Market St. next to Uber HQ. Shared desk space starts at $400/month. Dedicated desks start at $650/month. Dedicated desk membership allows access to mentors, VC’s, and industry experts. Open 24/7

Bespoke

Located at the Westfield Shopping Centre (near Yerba Gardens). Shared desk starts at $404/month. Private desk cost $625/month. Comes with library with sleep nooks and a bunch of other cool stuff. 24/7 access.

Covo

Hourly rate $4/hr. Open seating at $419/month. Dedicated desks cost $569/month. 24/7

WeWork

Bunch of different locations in SF and the bay area. Check the website for full details. 24/7.

CANOPY

They have three campuses located in Jackson Square, Pacific Heights, and the Financial District. Offices look super swanky on the website. 24/7 access to a community table starts at $595/month.

RocketSpace

Located in the Financial District. Hot Desk starts at $300 but only available from 8:30 AM — 5:30PM. Private Desk starts at $600 and has 24/7 access.

Workshop Café

For $3/h, you can book a seat and work as long as you want without the guilt of taking someone’s space. They offer the first 3 hours free. Use their app (mobile site) to reserve space when you get there. I really love their space and have come here a few different times.

Startupbasecamp

$20/day or $175/month. Also offers co-living spaces. Located relatively close to the Civic Center. Open M-F from 9:30 AM-6:30 PM.

History of Garlic

Garlic is one of nature’s oldest medicines. The early Egyptians used garlic extract to treat diarrhea all the way back in 1500 BC. Ancient greek physicians Hippocrates and Galen both prescribed it for intestinal ailments. Traditional Chinese and Japanese healers used it to treat sore throat, fever, and the flu. Indian healers gave it to patients suffering from respiratory, digestive, and parasitic ailments. Practically every civilization has been using garlic as a medicine since antiquity. However, with the rise of modern medicine practitioners have stopped focusing on dietary recommendations and instead shifted their focus towards expensive pharmaceuticals. The downside is that these drugs frequently come with harmful side effects. And let’s not forget that doctors don’t get paid to prescribe vegetables. Over the last few decades, scientists have conducted research to prove why garlic is so healthy for us. It turns out garlic has beneficial effects on blood pressure, cardiovascular health, the immune system, and women’s health. In addition scientific evidence suggests garlic has anti-cancer, anti-bacterial, as well as anti-diabetic properties. We will go through some of the most well established health benefits of this ancient medicinal plant.

The Active Ingredients

Allicin is the principal and most well studied active ingredient in raw garlic. When garlic is crushed or chopped the allicin is activated. There are also many different sulfur based compounds such as diallyl sulfide and diallyl trisulfide that are healthy for us in garlic. These sulfur based compounds give garlic its pungent smell. To get the maximum health benefits it’s important to eat garlic raw. High heat can destroy the active ingredients.

Cardiovascular Health

There have been several randomized placebo-controlled clinical studies that demonstrate the ability of garlic to lower blood pressure. In one significant clinical trial, 47 hypertensive patients who were given garlic had an average decrease in systolic BP of 12 mmHg and an average decrease in diastolic BP of 9mmHg compared to the placebo group. Although the exact mechanism of how garlic reduces BP is unknown, some scientists think the sulfur compounds have prostaglandin-like effects which decrease peripheral vascular resistance. Some studies suggest that the therapeutic dosage needed is >480 mg of garlic which works out to roughly 1.5–2 cloves of raw garlic.

There is also some evidence that garlic can decrease total cholesterol and LDL (the bad type of cholesterol) in patients with high cholesterol (i.e. >200 mg/dL). One meta-analysis including 39 primary trials showed that consuming garlic decreased total serum cholesterol by 17±6 mg/dL and low-density lipoprotein cholesterol by 9 ± 6 mg/dL. This data suggests that patients with high cholesterol may benefit from eating raw garlic in addition to taking medication prescribed by their doctor.

Although garlic has blood pressure and cholesterol lowering effects, there is still debate in the medical community as to whether garlic actually saves lives from adverse cardiovascular outcomes. There is at least some clinical evidence that the answer is yes. A study conducted in India of 432 coronary artery patients were randomized into an experimental group of garlic juice in milk vs a control group of just milk and NO garlic juice. The results showed that within 3 years, twice as many people in the control group died than in the garlic juice group. Of course there could be confounding variables not taken into account and more studies are needed.

Cancer

In 1990, the U.S. National Cancer Institute created the Designer Food Program to determine which foods had anti-cancer potential. They concluded that garlic was one of the most potent foods that could possibly prevent cancer in humans. Since then there have been a number of papers published by the scientific community that agree with this statement.

Researchers at the Medical University of South Carolina demonstrated that certain organo-sulfur compound found in garlic demonstrated some efficacy in killing brain cancer cells in vitro. They published their results in the prestigious journal Cancer. They looked at several different compounds including DAS, DADS, and DATS. DATS had the most effect in helping destroy glioblastoma brain cancer cells.

Chinese scientists have also conducted observational studies that seem to indicate eating garlic has a protective effect in other types of cancer. Those studies suggested that eating garlic was associated with a lower risk of developing prostate and GI cancers. In one study involving more than 5,000 Chinese patients who were deemed high risk for stomach cancer, researchers compared how a combination of allitridum (a garlic extract) and selenium would impact tumor development. The experimental group consumed garlic extract and selenium every other day for 5 years while the placebo group consumed an ineffective pill. After 5 years the experimental group had a 33% percent decreased risk for all tumors and a 52% decrease in stomach tumors compared to the control group.

It’s not just Chinese studies, however, that have illustrated garlic having a protective effect on developing cancer. A study done in San Francisco found that pancreatic cancer risk was 54% lower in people who ate large amounts of garlic vs those that didn’t. The Iowa Women’s Study, a large prospective study looking at various cancer risk factors, also showed that women who ate very high amounts of garlic had a 50% lower risk of distal colon cancer compared to women who ate very little garlic.

Immune System

A double blind randomized study conducted with 146 healthy volunteers who were given either a garlic supplement or placebo showed that garlic can decrease the risk of getting a cold and the total number of sick days. The garlic group had a 63% lower chance of getting a cold and if they did catch a cold they recovered 70% faster than the control group. A similar study showed that a group who ate ~2.50 grams of garlic extract/day suffered colds that were 61% shorter than the control group. Also the colds were less severe in the garlic group. On average since children have six to eight colds per year and adults have two to four it might not be a bad idea to add garlic to your diet to minimize sick days. Garlic has also been suggested to have anti-viral, anti-fungal, and antibacterial properties. Diallyl sulfide in garlic was shown to be 100x more effective at killing campylobacter bacteria than traditional antibiotics according to a study published in the Journal of Antimicrobial Chemotherapy.

Women’s Health

There is some evidence to suggest that eating garlic can help lower the risk of hip osteoarthritis in women. The study was conducted by the King’s College London and the University of East Anglia and published in the journal BMC Musculoskeletal Disorders. The long-term study used more than 1000 female twins and found that those women who had a diet more rich in garlic had fewer signs of hip OA. There is also some evidence that garlic can help pregnant women avoid having spontaneous preterm delivery. That study was conducted by the scientists at the Division of Epidemiology, Norwegian Institute of Public Health and published in the Journal of Nutrition.

Diabetes

Unclear evidence. Some studies suggest that it’s good for humans and can decrease fasting blood glucose levels while other studies say it has no effect. Some studies say it can also decrease insulin resistance. We need more studies, because current evidence in humans is promising yet largely inconclusive.

Alcohol Induced liver disease

May have protective effects. One randomized control study showed that patients with alcohol induced liver damage who ate 2 small raw garlic cloves for 45 days had significantly lowered liver marker enzyme activities, decreased levels of lipid peroxidation and enhanced antioxidant status to near normal. This was, however, only a small trial with 20 alcoholic patients and 20 healthy individuals. A recent 2018 meta-review seems to also support that garlic can help patients who have acute and chronic alcohol induced liver damage. That being said there is still a need for better controlled studies in the future to support this.

Conclusion

Over the past few decades scientific studies have backed some of what ancient doctors and healers believed. Garlic has a lot of medicinal power. Numerous experimental and observational studies in humans have shown that garlic has the ability to decrease blood pressure, decrease total cholesterol levels, protect against cancer, increase the activity of the immune system, and prevent hip OA and spontaneous preterm delivery in women. There is also some evidence that suggests garlic can benefit patients with type 2 diabetes and alcohol induced liver damage. Because of all these potential benefits the World Health Organization (WHO) suggests adults consume a daily dose of 2 to 5 g of fresh garlic (about one clove), 0.4 to 1.2 g of dried garlic powder, 2 to 5 mg of garlic oil, or 300 to 1,000 mg of garlic extract. Given all the health benefits of eating garlic and the relative lack of adverse side effects (besides bad smelling breath and possible food allergies), there’s a lot of scientific evidence that we should be eating more garlic for our health.

So you’re thinking about moving to San Francisco but you’ve only got a few bands in your bank account and a dream brewing in your brain. Well I am here to tell you to ignore all the negativity, take the leap of faith and move here! SF is a beautiful place full of opportunities for those who want to work in tech, finance, or grow their own business. It’s the best place in the world to link up with talented and ambitious people. Yes it’s really expensive and yes the city is not perfect, but from my own experience it’s been absolutely worth the move. The amount of talented people here is unparalleled. People from all over the world come here to develop their careers. On a random evening I was having drinks with an Oxford molecular biology PhD, an Oxford MBA who was pursuing a VC career, a serial entrepreneur enrolled in a prominent SF accelerator, and a med school dropout turned biotech entrepreneur. I met these people just a few hours ago at a meetup in the city and despite their impressive credentials, they were all chill AF. If you want to be surrounded by smart ambitious people who aren’t dickheads you will like SF. Moving to any new city can be scary and expensive as hell, but hopefully this short guide will make the move easier, less expensive, and maybe even a little less painful.

Renting an Apartment

In a 2018 study conducted by Walletwyse, out of 540 cities San Francisco was ranked as having the most expensive rent in the world averaging $3,500 per month for a one bedroom apartment.

Now the key word in that last sentence is AVERAGE. There are apartments that are a lot more expensive than that but there are also apartments that are a lot cheaper. To give you an idea of what’s possible where I currently live I pay less than $500 per month for rent (including utilities and access to a gym). It’s definitely possible to get a great price! Here’s how to do it

1. Find roommates. This means less privacy and less space but you also will be able to save $$$. There are a lot of websites that can help with this, but the most commonly used in SF is still craigslist. It can be pretty competitive but if you send out a dozen emails you should get a response from at least one or two people.
2. You want to live in a non-trendy part of SF. A lot of outsiders might not know that SF is actually pretty small. In fact it’s only 49 square miles. Here’s a map that shows the relative trend in prices across the city. The more southwest you go the rent prices will be cheaper. If you want the average prices for each neighborhood check out Priceonomics.com

Where I live — close to SF state — it’s not uncommon to find rooms (yes even private rooms) for less than $1k. Most of these choices will have you staying in a house with anywhere between 3–6 people. When I first moved here I was staying on Fuente Street (right next to SF state) for around $750 per month. I was paying that while also having a roommate. I found the room on craigslist while driving to San Francisco. Now I live right down the street on Font Boulevard. I pay about $500 per month (including utilities) which is dirt cheap for SF. I found the room using Sulekha.com which is a site that allows Indian men and women to find roommates. Now — technically I am an American and was raised in the South, but both of my parents are from India so I figured I could use my cultural heritage to my advantage. If you are of Indian descent or are coming to SF from India I would recommend you use this site because it’s less competitive than craigslist. Although you’ll be paying cheaper rent by staying in an uncool spot — you will have more space, feel safer, and encounter less of the homeless epidemic. The downside of course is that to get to where the action is (SOMA, Mission, Financial District) you’re gonna have to take the MUNI or Uber. But since SF is not that big, its pretty easy to get there.

Transportation

When I moved to SF I was pleasantly surprised at how good the public transportation system is. In fact it ranks just below NYC in terms of ease/convenience. Just like how most NY residents don’t need to drive and can just use the subway, most SF residents can get around really well just by walking and using public transport. According to Business Insider 99% of SF residents live less than .5 miles from public transit stops. A one-way muni ticket in the city only costs $2.50 if you use the MUNI app (or $2.75 if you use the paper ticket). It typically takes me about less than 30 minutes to get downtown using the M train so it’s also fast. If you need to get there faster you can also use Uber. If you use Uber Pool or Uber Pool Express it only costs between $5–10 a trip from where I live to downtown. If you do decide to drive then be warned that finding parking is a nightmare and traffic can get really bad during peak time. It’s just easier to use public transport and Uber in the city rather than having your own car.

Now let’s say you’re dirt broke — I am talking down to your last few dollars, but you need to make a trip across the city. Well you can actually finesse a trip on the MUNI without having to pay really really easily. In fact paying for the MUNI relies on the honor system. Every now and then there will be police officers that come by to check that you have payed, however this happens extremely rarely and you are more likely to get caught if you are on a train near the Civic Center. If you’re caught hopping on the train without paying you may get a fine for more than $100. There are some people in the city who literally never pay and haven’t gotten caught in months. So if you really really need to get somewhere you can probably get there for free. Also if you have a bike SF is great because there are plenty of bike lanes. Be warned, however, that some neighborhoods downtown will have really steep hills making it difficult to ride.

Everyone’s Gotta Eat Sometime

If you want to save money and are too lazy to cook — hit up the dollar menu on McDonalds or Taco Bell. Otherwise if you are planning on eating out regularly in SF you are going to find out that it’s really really expensive. A basic meal in the business district will cost you upwards of $16 according to Expatisan.com. Now McDonalds and Taco Bell are freaking terrible for you so if you want to avoid feeling like shit without breaking the bank the best way to thrive is to buy groceries and cook at home. In terms of grocery stores there’s a lot of different options in SF, butTrader Joe’s is fairly cheap and has really good quality. I’ve been able to eat very healthily off of $20-$25 a week. I can make another post on what groceries I buy but you can also check out the Brothers Green Youtube videos to get a sense of how far $20 will take you.

Free Food and Alcohol

One of the great things about living in SF is that there are events literally everyday. Just check out meetup.com or eventbrite.com and you will see that on most days there are free events that provide free food and alcohol. It’s a great way to network and meet people in the city. In fact several entrepreneurs have told me that they have recruited early team members at meetups so if you’re looking for a job it also might not be a bad idea to check it out. So basically you get free food, free beer/wine, possibly a job, and a chance to meet some really cool people pretty much everyday. All you have to do is step out of your apartment.

TLDR/Summary

1. Get some roommates using Craigslist
2. Live in a non-trendy part of SF. AKA not the Mission, Financial District, or neat the civic center, bur rather in the Sunset/Park Merced area
3. Use public transportation — it’s surprisingly good!
4. Buy your groceries from Trader Joe’s
5. Go to meetups as much as possible to get free food, drink, and meet new people.

Hope this helps you in your move! If you have any questions feel free to email me at s.velaga@uky.edu

Surviving in SF on less than $1k/month. Totally possible! Here’s How. was originally published in Future Travel on Medium, where people are continuing the conversation by highlighting and responding to this story.

Introduction

“My AI will seek to collaborate with people for the greater good, rather than usurp the human role and supplant them” — from the Hippocratic oath on artificial intelligence by Oren Etzioni [1]

Artificial Intelligence (AI) is currently one of the most hotly debated topics in technology with seemingly every business leader and computer scientist voicing an extreme opinion on the topic. Elon Musk, Bill Gates, and Stephen Hawking are all pessimists who have posited that AI poses an existential threat to humanity. Musk even publicly states that, “AI is far more dangerous than nukes” [2]. Famed futurist and technologist Ray Kurzweil, who studied under the inventor of the AI field, has a more optimistic outlook, “My view is not that AI is going to displace us. It’s going to enhance us. It does already” [3]. Billionaire Mark Cuban has said that AI will produce the world’s first trillionaire.

All these brilliant minds have caused an intense public interest in the field, but what many people may not realize is that the field of AI is more than 60 years old and that there have been a couple of hype cycles throughout its relatively short history. Periods marked by intense investment and euphoric speculation followed by disappointment, disillusionment, and precipitous declines in funding.

Although it’s right to be cautious about any life altering technology, AI in its current state is already changing every industry including even the ones most resistant to change such as healthcare. Already AI can diagnose diseases such as diabetic retinopathy with just as good or better accuracy than board certified ophthalmologists [4]. The fields of radiology, dermatology, and pathology which require doctors to eyeball images and make decisions based on what they see can all be very easily replaced by algorithms that do the work faster and more accurately. Startup companies as well as big international tech conglomerates (think Google, Apple, and IBM) are spending huge amounts of money to collect enormous healthcare data sets which can be used to train disease detection algorithms. This paper will take a closer look into the intersection between healthcare diagnostics and AI. We will discuss the most commonly used AI techniques in medical diagnostics, the history of AI, the current AI platforms that have received FDA approval, how the FDA is preparing for a tsunami of AI software products, future AI projects in the pipeline, and the obstacles that lay ahead of the AI healthcare revolution.

How Do Researchers Use AI? ELI5.

“Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.” — Dan Ariely [5]

Actually using AI techniques requires a basic understanding of calculus, probability, and linear algebra. We will skip the math and instead describe the tools of machine learning (ML) and natural language processing (NLP) using plain English. Both ML and NLP are subsets of AI and are currently the most commonly used tools [6]. ML in medicine has become extremely popular in the past few years because of the exponentially increasing amounts of data that is being generated and recorded in digital format aka “big data”. All this data needs to be analyzed and that’s where ML comes in, because it can be used to learn about patterns in the data and make predictions. Broadly speaking ML can be separated into unsupervised and supervised learning.

Unsupervised learning uses unlabeled data and can be a good way to find and group data based on similarities. For example if you have scans of chest x rays, you could then use unsupervised learning to divide the data into x rays that resemble pneumonia vs those that resemble a collapsed lung (pneumothorax), vs those that resemble congestive heart failure. You would NOT provide labeled chest x rays with the model, however in supervised learning you WOULD provide labels.

In terms of healthcare, supervised learning is more commonly used. Supervised learning comes in multiple different flavors including linear regression, logistic regression, naïve Bayes, decision tree, nearest neighbor, random forest, discriminant analysis, support vector machine (SVM) and neural networks (NNs). By far the most commonly used supervised learning techniques in medicine are SVM and NNs [7]. SVM is very simply used to separate subjects into 2 different categories given a set of traits. For example, for the disease pneumonia there could be a number of disease traits including cough, fever, and difficulty breathing. Each of these traits would be weighted differently depending on how much each of these individual traits is statistically associated with the disease. If the weighted average of these 3 traits is above a critical level the patient is diagnosed with pneumonia. If below the critical level then they do NOT have pneumonia.

NNs are the second most commonly used form of supervised learning in medicine and were originally inspired by the structure of neurons. NN’s use an input layer, a middle hidden layer(s), and an output layer. The input layer is the equivalent of the dendrites and the output layer is the equivalent of the axon which carries the signal. When a NN is first being trained all the weights and the threshold values are set to random values. The weights and thresholds in the hidden layer are randomly adjusted a bunch of different times until the input layer data consistently produces the labeled output layer data. Put very crudely it’s a way to get from input to output by trying a lot of different things in the middle to see what works best.

If you have a model that has a LOT of separate hidden layers (layers between the input and output) then this is called deep learning, which has also become very popular recently. The number of papers in medical research that mentioned deep learning doubled just from 2015 to 2016 [6]. Deep learning is especially popular with diagnostic imaging analysis. While ML and deep learning are excellent tools for these types of imaging studies, the use of NLP is also often necessary given that so much clinical information is stored as text. NLP can be used to process text and then classify keywords associated with diseases [8]. With a basic understanding of the most commonly used AI tools in medicine, we will now look back at the history of AI.

A History of Booms and Busts

“The excitement of making something, that’s the spark of God” — Childish Gambino [9]

Like any good origin story, the history of AI can perhaps be traced back to the world’s most infamous hoax. In 1769 a humble thirty-five-year-old civil servant named Wolfgang von Kempelen created the Mechanical Turk — a chess playing machine designed to amuse Austrian empress Maria Theresa [9]. The machine was created to look like a life sized human head and torso wearing Ottoman robes and a turban that could play a mean game of chess. The Mechanical Turk received significant fame during its time and even managed to beat Napoleon Bonaparte and Benjamin Franklin during its tour. The European and American public were fascinated by how the machine could play chess so well on its own, yet little did they realize that underneath the cabinet of the machine there was an actual human chess champion making masterful moves. Fast forward to the early 20th century and two visionary computer scientists, who had never met, were simultaneously dreaming of intelligent machines: the British Alan Turing and the German Konrad Zuse.

In 1936 Turing proposed an idealized computational device called a Turing machine and in 1950 he went several steps further and proposed the famous Turing Test, which simplistically posits that the test will be satisfied when a human cannot tell the difference between talking to a computer vs another human [10, 11]. In essence talking to a computer is indistinguishable from talking to a human. Tragically after Turing created his famous Turing Test he was persecuted by the British Government for being gay and he committed suicide a few years later. The other man Konrad Zuse was equally ahead of his time if not more so. In 1941 Zuse created the first functionally programmable computer called the Z3. As early as 1943 he was daydreaming of whether intelligent machines could be created to play chess. In 1945 he created the world’s first higher level programming language called Plankalkuel which he used to design the world’s first chess program. If that wasn’t enough the year after that he created the world’s first computer startup company and even managed to convince investors to back his venture [12]. Talk about a man with conviction. These two men were perhaps the most famous progenitors of computing science preceding the official birth of AI in the 1950’s.

In the winter of 1955 a group of scientists including Allen Newell, Cliff Shaw, and Herbert Simon proposed the Logic Theorist, which entailed using machines to prove mathematical theorems from the Principia Mathematica [13]. The proposal received funding from the RAND corporation. As a major foreshadowing event even so early on in AI history, one of the theorems was proven more elegantly using the machines than the one produced by the actual authors, which astounded Bertrand Russell, one of the key authors of the Principia Mathematica. [14] The discoveries were presented at a key conference called the Dartmouth Summer Research Project on Artificial Intelligence (DRPAI) organized by John McCarthy and Marvin Minsky in the summer of 1956. The idea behind the conference was to collect the 10 foremost experts on the topic at hand and have them elucidate “every aspect of learning or any other feature of intelligence [that] can in principle be so precisely described that a machine can be made to simulate it.” [15] These are the exact words that they submitted to the Rockefeller Foundation, which led to them securing $7,500. The field of AI was born at a fun little summer conference bankrolled by one of the richest families in America. And thus it began.

Since that faithful conference about 60 years ago we have had at least 3 AI booms, marked by increased public attention and funding, of which we are now in the third. In between these booms are what historians refer to as AI winters, in which the overly optimistic predictions made by scientists fell short and investors tightened their purse strings. To give you an example of the way these cycles work consider Herbert Simon (aforementioned creator of the Logic Theorist) who publicly predicted in 1957 that in within a decade a computer would be able to beat the world chess champion [16]. He was off by 30 years when IBM’s Deep Blue famously defeated Garry Kasparov. Still such optimistic predictions made by well-respected public scientists fueled investment dollars, but when the promises/predictions were not seen to come to fruition the public and investors grew disheartened.

During the first boom, which took place roughly between 1957 to 1974, machine learning methods improved significantly as well as the computational ability of computers while costs dropped. In the early 1950’s only the most prestigious universities had access to computers and they were super expensive costing upwards of $200,000/month [17]. But as the years progressed computers became more powerful and cheaper thanks to Moore’s Law. This trend opened up the doors for more people to get involved. The first boom brought such innovations as the General Problem Solver, Arthur Samuel’s Checkers Program, the OG general purpose robot SHAKEY, and the primitive chatbot ELIZA which ran a program called DOCTOR that used NLP to simulate a psychotherapist. (18) These initial successes were so inspirational that the Defense Advanced Research Projects Agency (DARPA) agreed to plow large amounts of capital into AI research at several US universities. Along with the piles of money and euphoric optimism also came fear that AI would lead to mass unemployment. President Kennedy remarked that unemployment caused by technology was one of the major challenges of the 1960’s. (19) From 1953 to 1964 more than 800 publications discussed the role of technology in eliminating jobs [20]. In 1964 President Johnson created a special committee to discuss the growing public fear of AI and technology displacing jobs [21]. Perhaps at the peak of boom 1 mania in 1970 Marvin Minsky told Life Magazine, “[in] three to eight years we will have a machine with the general intelligence of an average human being.” He was off the mark considering that today we are still working on fulfilling that goal. After being promised so much by celebrity scientists and having drunk so much of the initial AI Kool-Aid, DARPA finally sobered up and cut funding leading to the first AI winter (1974–1980). But the 2nd boom showed signs of life as early as 1980 [23].

Boom 2 was remarkable in the amount of tenacity/ingenuity it took key researchers such as Edward Feigenbaum, who studied under Herbert Simon, to wipe the frost off the face of AI. Feigenbaum’s scientific contribution to the field was a radical new paradigm called expert systems in which AI was trained to be a narrow problem solver in a specific domain of interest such as medicine [24]. Feigenbaum was brilliant and shrewd. Early on he realized that if he was going to bring AI out of the winter he would need to get boatloads of funding into the field, but that he wasn’t going to do it by making outlandishly optimistic prognostications. Instead he would need to use fear as a motivator. During the late 1970’s Japan’s economy was booming and there was a fear amongst the American public that Japan would surpass the US as an economic superpower. Japan was also working on a project called the Fifth Generation Computer System (FGCS) which the Japanese Ministry of International Trade had dumped half a billion dollars into. The FGCS was designed to be a massively parallel supercomputer which could be used to develop expert system artificial intelligence [25]. At the Japanese premier of FGCS in 1981, Feigenbaum was invited to give a keynote address. In the speech he praised the project and Japan’s ingenuity. As soon as he got back to America he visited computer science departments across the country and spearheaded a campaign to raise fear about Japan beating the US in AI development [26]. The US government took notice. Months later DARPA funded a billion-dollar proposal called the Strategic Computing Initiative (SCI) to crush Japan in AI development. Feigenbaum’s plan worked, but it went further than he imagined. The SCI inspired the development of the $1.5 billion European Strategic Programme on Research in Information Technology, and the $500 million Alvey project in the UK [27]. In an effort to bring AI out of the winter Feigenbaum and his contemporaries had sparked an international arms race leading to the 2nd AI boom.

The expert systems domain characterizing the 2nd AI boom was especially important in catalyzing innovation in healthcare. In a Stanford paper Feigenbaum described some of the exciting and pioneering applications including MYCIN, VM, PUFF, INTERNIST, and CADUCEUS [28]. MYCIN used AI to diagnose bacteremia and to then recommend the appropriate antibiotic with the correct dosage based on body weight. MYCIN (named after the suffix of common antibiotics i.e. — erythroMYCIN) was never actually used in hospitals due to the lack of computer infrastructure in clinical settings back then. It was however put to test theoretically at Stanford and ended up performing superior to physicians [29]. VM provided real time advice to clinicians about ventilation management settings for critically ill patients who were intubated in the ICU. For example, if a patient showed improved breathing function and could be weaned off the ventilator slowly VM could suggest that course of action to the attending doc. PUFF was created to interpret pulmonary function parameters such as lung volume, flow rates, and diffusion capacity. The program used these measurements in concert with a diagnosis from the referring doctor and knowledge of common obstructive/restrictive lung pathology to diagnose the patient. The system was also automated to create a report that doctors could read and sign. Even as early as 1980, the reports generated at the Pacific Medical Center by PUFF were signed by 85% of doctors without any necessary editing. INTERNIST was a very ambitious program that even by today’s standards was way ahead of its time. INTERNIST, as one could guess from the name provided differential diagnoses in the field of internal medicine. It had knowledge of close to 500 different pathologies. Writing about INTERNIST in 1980 Feigenbaum wrote,

“Because INTERNIST is intended to serve a consulting role in medical diagnosis, it has been challenged with a wide variety of difficult clinical problems: cases published in the medical journals, clinical conferences and other interesting and unusual problems arising in the local teaching hospitals. In the great majority of these test cases, the problem formation strategy of INTERNIST has proved to be effective in sorting out the pieces of the puzzle and coming to a correct diagnosis, involving in some cases as many as a dozen disease entities” [30]

Although INTERNIST was incredibly promising it was far from perfect. Subsequent papers put out by the team who originally created INTERNIST (computer scientist Harry Pople and internist Dr. Jack Myers) showed that the system was not robust enough for regular clinical decision making due to a host of frequent errors so they addressed these errors on the next iteration [31]. CADUCEUS was basically version 2 of INTERNIST-1. It’s estimated that CADUCEUS had working knowledge of more than 500 different disease processes and 3,400 specific disease characteristics [32]. CADUCEUS also improved upon INTERNIST-1 by giving explanations on how it came to its conclusions. At the height of AI boom 2 in 1986 CADUCEUS was described as the “most knowledge-intensive expert system in existence” [33]. Certainly, these were exciting times for the expert systems AI enthusiasts, however good times don’t last forever.

These large expert systems of knowledge were expensive, difficult to keep updated, and cumbersome to use by everyday medical professionals. Disappointed by the progress that had been made in the actual use of AI, the SCI and DARPA cut funding to AI projects aggressively in the late 1980’s [34]. The Japanese economy started to collapse in the early 1990’s leading to a global AI bust in which all the major AI funding from international government organizations froze. These were hard times for AI as VC funding dwindled and academics were even embarrassed to be associated with the field. The 2nd AI winter which historians date from 1987 to 1993 may have not been well funded or respected but important research activity still took place and by the early 1990’s it looked like the frost was again thawing. Optimism was rekindled by developments in powerful new AI techniques such as multiple layer neural networks and genetic algorithms in concert with more powerful computers.

The pace of AI started to increase in 1993 but really took off in earnest when IBM Deep Blue made history on May 11th 1997. In a highly publicized multiple day televised chess match between IBM Deep Blue and the reigning chess champion Garry Kasparov, Deep Blue triumphed. Finally the great promises made by the earliest AI pioneers were coming true. It’s estimated that the 1997 Deep Blue was able to calculate 200,000,000 possible chess positions per second [35]. From that year to the present we have seen an exponential increase in the rate of breakthroughs and breathtaking AI discoveries. In February 2011, Watson played against the reigning kings of Jeopardy, Ken Jennings and Brad Rutter. In a televised spectacle Watson clearly beat them both, which stunned audience members as well as computer scientists [36]. In March 2016, AlphaGo (created by Google AI subsidiary DeepMind) played the 18-time world champion of Go, South Korean Lee Se-dol. Lee Se-dol has been called the Roger Federer of Go and is a legend in South Korea.

Go is an ancient incredibly complex game that has more possible move combinations that there are number of atoms in the universe. It is considered the hardest board game invented in history. Because Go is so complex experts believe it requires human intuition [37]. The researchers trained AlphaGo using deep neural nets on a data set of 100,000 examples of humans playing the game. After that they went one step further and programmed the machine to play millions of games against itself. Through reinforcement learning AlphaGo learned the best possible moves to make by playing itself. It’s estimated that 80 million people worldwide watched the first game between Lee Se-dol and AlphaGo [38]. At the beginning of the 5 game tournament Lee Se-dol remarked that he expected to beat the machine all 5 times. During game 1 Lee Se-dol was caught off guard and was easily defeated. Game 2 was much closer. It was a slower more deliberate game. On move 37 something interesting happened. AlphaGo made a move that baffled experts who were commentating on the game. The machine made a move no human would make. In fact, when the programmers looked at the probabilities there was only a 1/10,000 chance that a human would make that move and yet AlphaGo decided to go for it anyway. Move 37 has been called “beautiful” by professional players. Eventually Lee Se-dol resigned as he realized there was no way to win. He was crushed. AlphaGo beat him on the third game as well and ended up winning the championship. It was the first time in history that the best Go player in the world was a computer.

But that’s not the end of the story. There were after all still 2 more games to be played. During the 4th game Lee Se-dol was much more relaxed. Everyone in the audience wanted him to win so badly because they had seen the despair on his face and felt it the days before. This is a man who had dedicated his entire life to the game. On move 78 Lee Se-dol made a move that stunned professional players. The computer calculated that there was a 1/10,000 chance that a human could have made that move. Later Lee Se-dol remarked that it was the “only” move he could make. The computer tried it’s best but lost. AlphaGo resigned after it realized there was no way it could win. Move 78 is now known as the “hand of god” in the professional Go community. The crowd was happy that humanity had a chance against the machines. But wait there’s still more. In the 5th and final game AlphaGo invented a new way of playing the game. In a documentary called AlphaGo on the match, it shows the DeepMind crew backstage thinking that there was a bug in the software that was causing it make all these weird moves. Even the professional onlookers thought that there was a possible malfunction with the software. As the game progressed, however, the professionals realized this was a new way to play the game and Lee Se-dol was forced into resigning after realizing he had lost [39]. The machine had taught the master a new way to play the game. Google is now using DeepMind’s technology to not just play board games but also to advance the field of healthcare and save lives [40].

AI is Disrupting Every Industry Including Healthcare

“AI is the new electricity” — Andrew Ng [41]

Interwoven in the AI booms is an interest in not just theoretically making machine intelligence approach that of humans but to use the technology to improve society. One industry that is in desperate need of help is healthcare. These efforts started as early as using INTERNIST-1 and CADUCEUS to make medical diagnosis during the 2nd AI boom. AI applications in the healthcare field are essentially limitless, however, much of the key work requires computer scientists to closely collaborate with physicians, hospitals, and perhaps most crucially the FDA. To date there have been only a handful of AIM (AI in medicine) companies in the US that have finished FDA trials to actually use their platforms in hospitals. We will discuss the 3 that have received the most attention as of now: Arterys, Viz.AI, and IDx.

In January 2017 the FDA gave San Francisco based startup Arterys the first approval for using AI to diagnose patients in a clinical setting. The company has created a medical imaging service that uses deep learning to analyze MRI scans of the heart. The model was trained using a data set of only 1000 MRI imaging studies which means they will likely need to collect way more studies to increase the robustness of their platform. The company’s 4D Flow cloud-based platform can receive cardiac MRIs and then analyze them with algorithms to determine the ejection fraction — i.e. the volume of blood that the ventricles eject from the heart with each contraction. In February 2017 Arterys partnered with GE healthcare to launch its flagship product at a private practice radiology clinic in Virginia [42]. Arterys’s algorithm is based on 10 million rules and can also produce contours of the heart’s four chambers in just 15 seconds, while it would take a human cardiologist between 30 to 60 minutes [43]. This year Arterys announced it had also won FDA approval for an AI based cancer detection platform. The software employs deep learning to assist radiologists in diagnosing lung cancer lesions on CT images and liver lesions on both CT and MRI [44].

In February 2018 the FDA approved early stroke detection software platform Viz.AI. The platform analyzes CT scans of the brain and if it detects the presence of a stroke will send a text message to a neurovascular radiologist alerting them to look at the imaging study ASAP [45]. The idea is that by alerting providers there will be more timely care decreasing the time to clinical intervention. In the United States stroke is the 5th leading cause of death with an estimated 795,000 people dying per year [46]. It’s estimated that by using the Viz.AI platform the time from imaging to notification of a specialist has been reduced from 66 minutes to 6 minutes [47]. This time difference is critical since the standard treatment of care for ischemic stroke requires tPA to be given within a time window of 4.5 hours (48). Viz.ai received approval based on a retrospective study of 300 CT images in which they compared the effectiveness of the platform vs two human neuro-radiologists [49].

For the first time in history on April 11th 2018 the FDA approved an AI platform made by Iowa startup IDx that can make a diagnosis WITHOUT a doctor. Their IDx-DR platform diagnoses diabetic retinopathy by analyzing pictures of a patient’s retina which are uploaded to the system by a nurse or physician [50]. Diabetic retinopathy is the most common complication of diabetes and there are an estimated 200,000 cases per year. If left untreated the disease can progress to blindness so it is important for early detection and management [51]. IDx-DR is the first of its kind and represents a giant step for the future of medicine especially given the extreme shortage of doctors the US is facing. It’s estimated that a decade from now there will be a shortage of more than 40,000 doctors [52]. This platform allows family practice physicians who are able to obtain images of a patient’s retina to make a diagnosis without the need of an ophthalmologist. A clinical trial with 900 participants showed that IDx-DR correctly diagnosed diabetic retinopathy 87% of the time while it correctly identified patients that did NOT have the disease 90% of the time [53].

A new study took funduscopic images using a smartphone on 296 patients in India. The results were also extremely promising. The IDx-DR platform even with this smartphone-based approach had a specificity of 80.2% and a sensitivity of 95.8%. This is on par or better than ophthalmologists and retina specialists according to the study [54] As evident from the above examples FDA approval for these type of AI powered diagnostic tools is a very recent development. Given the FDA’s very new committee for these types of digital health and AI powered platforms it is very likely we will see many more approval announcements in the months to come.

The Old FDA and the Digital FDA

“If you’re trying to develop a new drug, that costs you a billion dollars to get through the FDA. If you want to start a software company, you can get started with maybe $100,000” — Peter Thiel [55]

The FDA is the oldest consumer protection agency in the country and as most old things it can be incredibly slow. Established in 1848 as a small little division of the US Patent Office, over time the FDA has ballooned in size to become one of the largest and most stringent consumer protection groups in the entire world. The insidious growth in bureaucracy has had negative repercussions on innovation. New medical device approval can take upwards of 7 years and cost tens of millions of dollars. In fact, the average cost to get a medical device (hardware) approved from start to finish is currently pegged at $94 million [56]. But wait…it gets worse. New drug approval takes an average of 12 years and the average cost is more than $2.5 billion, a 145% increase in cost from 2003 [57]. This trend has been remarkably consistent over time leading to what experts call Eroom’s Law, which states that the cost of approving a new drug doubles roughly every 9 years.

Realizing the rapid pace of innovation that is taking place in software and AI, the FDA’s associate director for digital health Bakul Patel decided to streamline the approval process. He’s heading a digital division of the FDA consisting of 13 software engineers and AI experts. Patel aims to create a pre-cert process in which companies who have passed inspection can continue to add software products to the market without regulators having to inspect every new platform or update [58]. Already several companies have signed up for the pre-cert process including Apple, Samsung, Verily (subsidiary of Google), Roche, and Johnson and Johnson. Apple and Google especially have huge amounts of cash that they have been piling into various digital healthcare initiatives such as diagnosing Parkinson’s, diabetes, and diabetic retinopathy [59].

It seems that ever since the 21st Century Cures Act, which was passed in December of 2016, the FDA has been racing to keep up with the times. This landmark act changed the definition of what the FDA considers as a medical device to exclude certain types of software making it less difficult to obtain approval for certain types of digital health companies [60]. The newly appointed FDA commissioner, Dr. Scott Gottlieb also announced that the FDA would be creating a data science incubator cleverly called INFORMED (or the Information Exchange and Data Transformation) that would study how data analysis using AI/ML plays into the FDA approval process. Dr. Gottlieb states that “The initial focus of this new tech incubator will be, among other things, the conduct of regulatory science research in areas related to health technology and advanced analytics related to cancer. Our goal will be to help modernize our framework for advancing promising digital health tools.” [61] Clearly the new digital FDA has its eyes on the future. Hopefully with these new internal incubators and small teams the approval for innovative new AI technologies can come to providers faster.

Dermatology, Radiology, and Pathology

“Radiologists and pathologists need not fear artificial intelligence but rather must adapt incrementally to artificial intelligence, retaining their own services for cognitively challenging tasks” — Dr. Eric Topol and Saurabh Jha [62]

The FDA’s digital health and AI approval bodies are going to be very very busy for the next couple of years as a ton of startups, large tech companies, and universities are working on automating diagnosis in dermatology, radiology, and pathology.

Researchers at Stanford have used convolutional neural nets (a form of deep learning) to diagnose skin cancer and melanoma. The algorithms were trained on a data set of 130,000 skin lesion images and 2,000 different skin diseases [63]. This application is incredibly important considering that there are more than 5 million new skin cancer diagnoses each year. Early detection is critical as there is an associated 97% survival rate, however this drops with late detection and is as low as 15% if caught at stage IV [64]. Stanford’s deep learning algorithm was tested against 21 dermatologists and performed just as well as them in making clinical assessments [65].

In terms of radiology there are way too many AI startups too count, not to mention the large conglomerates such as GE and Siemens that are working on building a presence in the space. The field is more crowded than a Cro-Mags most pit, but with all these players there will certainly be more platforms receiving approval and hitting hospitals soon. Perhaps the most impressive player in the field is a pioneering Israeli startup named Zebra Medical Vision that offers a cloud-based platform that charges just $1 per imaging study. They have 5 FDA cleared algorithms and have performed more than a million scans [66]. They will likely receive approval for the other products in their platform such as breast imaging study analysis automation.

In 2016 a team at Harvard and Beth Israel Deacon won an international imaging competition by detecting metastatic breast cancer that had spread to lymph nodes on pathology slides by using neural nets [67]. The success lead some of the researchers to create a startup named PathAI which as you probably guessed uses AI to automate diagnoses based on slide images.

Although 80% of patient care is associated with the examination of pathology slides, there had been no digital disruption of pathology until April 2017 when the FDA granted permission to make diagnoses based on digital pathology images for the first time [68]. It’s not just these pioneering startups though, IBM’s Watson has collected more than 30 billion digital images in the fields of radiology, dermatology, and pathology. Dr. Eric Topol told VentureBeat that, “Most doctors in these fields are overtrained to do things like screen images for lung and breast cancers. Those tasks are ideal for delegation to artificial intelligence.” [69]. Although it is inevitable that AI will disrupt the traditional role of doctors in these three specialties, the transformation will not take place overnight. There are of course sizeable obstacles that will need to be overcome.

The Challenges That Lay Ahead

“There are no big problems, there are just a lot of little problems” — Henry Ford [70]

The path towards full blown AI in healthcare seems inevitable but first there are some serious hurdles such as data acquisition, not enough people working on the problems, hype with limited follow through, not understanding how the algorithms make decisions, integrating into physician workflow, and systemic malfunctions.

In a recent survey of 16,700 data scientists on what were the biggest challenges in the field of machine learning the number one answer was data quality followed by lack of talent [71]. Acquiring large data sets especially of patient sensitive information is perhaps the single greatest challenge. Not to mention that the data sets need to be properly labeled and annotated. If researchers use poor data they will be subject to the GIGO phenomena — garbage in equals garbage out. The founder of data science startup Kaggle recently summarized it well by saying,

“There’s the joke that 80 percent of data science is cleaning the data and 20 percent is complaining about cleaning the data. In reality, it really varies. But data cleaning is a much higher proportion of data science than an outsider would expect. Actually training models is typically a relatively small proportion (less than 10 percent) of what a machine learner or data scientist does.” [72].

What can make data collection so difficult in this setting is that a lot of the data contained in the EHR systems is in free-text form and can be difficult to sort through not to mention time consuming. In addition, EHR systems usually are not compatible with one another making data acquisitions across multiple institutions more difficult. Thankfully there are open access databases of medical information such as x-rays on the internet that can be used to train models provided by reputable organizations such as the NIH. In addition to actually acquiring the data, there are problems associated with acquiring humans. Human talent in AI that is. According to LinkedIn it’s estimated that there are only approximately 35,000 Americans that have data science skills and only a fraction of them can actually apply and develop machine learning models [73]. Meaning that in the US there is a shortage of these specialists. Because there is a huge shortage the financial incentives for these people are insane and big tech companies such as Google are willing to pay >$300,000k/year salaries for their skills. Hopefully some of these people will be motivated to work on medicine vs. trying to tell the difference between a hot dog vs. not a hot dog.

Which brings us to the third problem which is hype with limited results which results in disillusionment. Nowhere can this be illustrated better than with IBM’s Watson. Several upper level executives have spoken out that IBM used deceptive marketing tactics to sell its AI services to hospitals and providers. StatNews also reported last year that despite relentless marketing and advertising, IBM’s Watson for cancer clinical decision support had only been adopted by 12 hospitals and was still struggling to learn the differences between even the most basic types of cancer. This difficulty lead MD Anderson to cancel their partnership with IBM after 3 years and spending millions trying use the machine [74]. And IBM is surely not the only one to fault here, there are of course much smaller startups that are high on hype and short on product.

In a recent stunningly well written NYT article Stanford professor Vijay Pande described the “black box” problem of AI. Basically the problem is that these neural nets are fed input data and then produce output data while the steps in between are opaque and researchers sometimes don’t know how the algorithms came to the conclusions that they did. This will pose a problem to doctors who insist that they have reasons behind their diagnosis. Simply having an algorithm that tells you the answer without knowing why will probably not convince doctors to use it. Pande writes, “Doctors will no longer “drive” the primary diagnosis; instead, they’ll ensure that the diagnosis is relevant and meaningful for a patient and oversee when and how to offer more clarification and more narrative explanation” [75]. He is right but first doctors are also going to need to be trained on how AI and machine learning work which will also slow down the adoption. Given the current abysmal state of medical training/education this may take longer than most non-medical professionals think. Doctors are very busy people and they often do not have the time to learn new things or try out new technologies. Convincing them to use AI platforms even if they are better and safer is going to take a lot of work, time, and persistence. Legacy platforms and technology in hospitals have a tendency to stick around even decades after they have become obsolete in other industries. Two well-known examples that even in the year 2018 still exist are fax machines and pagers.

Fax machines are still routinely used to transmit medical charts from hospital to hospital. And don’t even get me started on pagers (yup those are also still the industry standard to communicate). In order for AI to go mainstream at most hospitals it will likely take government mandates. To give you an example the first EHR systems were used in the early 1960’s. In 1991 the Institute of Medicine declared that all physicians needed to use EHR’s by 2001 to improve healthcare, but there was no actual law/mandate passed. By 2001 only 18% of doctors were using an EHR system. Meaningful adoption of EHR did not become significant until after the federal government mandated the adoption by passing the HITECH act and paying out $21 billion to acute care hospitals to adopt the technology [76].

The final problem that needs to be sorted out is potential software errors leading to adverse outcomes. Case in point the recent NHS mammography scandal in which a faulty algorithm caused 450,000 women to miss their breast cancer screening [77]. To prevent debacles like this it is necessary for software companies to continually check their code so as to avoid bugs. If these companies want faster adoption they are going to have to safeguard against these kinds of errors. Despite all these possible obstacles, it is certain that AI will find its way into healthcare. It may take longer than other more rational industries but slowly it will.

The Future

“Future medicine will be the medicine of frequencies” — Albert Einstein [78]

In the future AI will make every single diagnosis. Most algorithms will be just as good or better than the very best board-certified MDs. Instead of physicians relying on their “intuition”, what they learned in medical school, or what they read on UpToDate right before they saw the patient they will rely on an automated AI generated report. We are still far away from this future. Even in 2017 nearly 100% of US healthcare was delivered with 0% AI involvement [79] But in the ensuing decades this will change. Today there are hundreds of venture-backed startups working on these types of solutions not to mention the multibillion dollar tech conglomerates.

Although we mostly covered radiology, dermatology, and pathology it is important to note that this is just the tip of the iceberg. There are also currently platforms being built that diagnose congenital disorders using machine vision, mental health conditions such as depression and mania, neurologic diseases such as parkinson’s, epilepsy, and alzheimer’s, respiratory diseases such as asthma, and not to mention cardiovascular disease based on ECG and heart rate data. The AI revolution in medicine will not happen overnight. It will take some time for these pioneering companies to receive FDA approval and even longer for doctors to become comfortable using these technologies on a daily basis. Likely after much consideration, a federal mandate will be passed that requires doctors to use the technology because it is better than the traditional old school method of relying on the human brain.

The history of AI is one that is based on booms and busts so it is likely that there will be another bust in the future which will slow some progress in the short term, but will lead to much stronger business strategies and discoveries in the long term. It’s true that as a field AI has been around for close to 60 years, but only in this past decade has there been any real significant progress in applying the technology to healthcare. And even more recently only in the last 5 years have we seen these application becoming robust enough to receive FDA approval for clinical use. There is still a ton of work to do and we are still obviously in the very early stages, but now more than ever we need better tools to help our doctors.

Healthcare in America is going through a crisis in which both the doctors and patients are suffering. Doctors have become so overburdened by paperwork and bureaucracy that they can no longer give their patients the attention they need. The system has resulted in our physicians dealing with large amounts of stress which can lead to depression, drug use, marital problems, burnout, and unfortunately suicide (which has become too common). These things result in patients getting bad care. According to a John Hopkins study, medical errors cause more than 250,000 deaths/year making it the third highest cause of death (after heart disease and cancer) [80]. Surely, we can do better than this.

Using AI is not going to solve all these problems, but it can help. It’s just impossible for doctors to always perform at the top of their game because they are human — and like all of us they can occasionally screw up. Having an AI diagnostic assistant to objectively analyze the data and come to a conclusion based on the numbers and facts will lead to less misdiagnosis especially with respect to rare diseases. Algorithms don’t get divorces and have to pay child support. They are objective and have no emotion. They crunch numbers and sort through facts. Doctors can however do what machine learning algorithms can’t which is empathize.

AI at its best will restore humanity and caring to medicine. If we ignore this technology we risk hurting our patients. For physicians that fear losing their job to AI remember that at the end of the day medicine is not just a science but also an art. The Hippocratic oath that all doctors swear on their first day of medical school states “I will remember that there is art to medicine as well as science, and that warmth, sympathy, and understanding may outweigh the surgeon’s knife or the chemist’s drug”. AI alone is no good. We still need the humans. And if we let it, maybe AI can help us bring the humanity back to medicine.

Full List of Sources

AI in Medicine: A Beginner’s Guide was originally published in HackerNoon.com on Medium, where people are continuing the conversation by highlighting and responding to this story.

Who do you listen to the most regarding your health? An interesting study done in Los Angeles published in the NEJM looked at blood pressure reduction in two groups of African American patients in barbershops. Yes, barbers — those people we trust to wield scissors and razors on our heads. In the study, one group was assigned to receive recommendations by the barbers to see pharmacists in the barbershops to get their blood pressures checked. On the other hand, the control group just had the barbers talk about improving their health and lifestyle. The results were impressive. The group that met the pharmacists saw a whopping drop of 27mm on average in their systolic pressure, while the control group saw a drop of 5mm. Identifying and tackling social determinants of health to improve medication compliance and lifestyle changes can take a whole different approach if we could work with the community influencers. The approach must be two-pronged; providers must build rapport to guide patients in the right direction, but utilizing the influencers in the community will be key. The cutting-edge move is perhaps to bring the care to where the people are, like barbershops.

Tree Oil and Man Boobs

If you were looking for something to blame for your Man Boobs, essential oils might be something to consider. Essential oils are ubiquitous. Grooming products like lotions, soaps, shampoos, aftershave, and hair products often contain oils derived from plants. Fueled by increased reports by consumers regarding male breast growth, or gynecomastia, after continued use of these products, a new study in North Carolina looked at eight important chemicals. The results showed that all eight of the chemicals either promoted estrogen or inhibited testosterone properties on cancer cells. And to nobody’s surprise, they recommended further large studies to demonstrate the relationships between these essential oils and gynecomastia. If you have stocks in lavender or tea tree oil, maybe you can also look into investing in pharma companies that make the anti-anti-male hormones. But first, perhaps consider scrutinizing your diet and exercise regimen for your Man Boobs.

Risks of Secondhand Marijuana Smoking

It is not uncommon to smell a whiff of marijuana secondhand on the streets of major U.S. cities nowadays. While smoking has been gaining social stigma and getting banned in many public places, marijuana has enjoyed more social acceptance. But regarding its effects of secondhand smoking, there has not been enough research to date. I mean, is smoke, smoke? Recently, science may have made some headway determining whether all smoke is the same, and the answer might be more intuitive than we think. Springer and his lab at UCSF have shown that secondhand marijuana smoke inhibits the rats’ arteries from expanding, and that is not good for anyone’s lungs. And according to some authorities, those who are in support of the widespread use of cannabis are following in the footsteps of tobacco; the old excuse of ‘No research, no rule’ perhaps deserves more scrutiny. We’re not too far from talking about potential occupational risk hazards and using tax revenue for marijuana research, and maybe even a new graphic designer for a No Smoking sign.

BCBS working with Lyft to reach patients

Uber and Lyft recently made splash about providing transportation for patients to and from clinics and hospitals. It looks like Lyft is in the lead with a new $600million funding and a new partnership with Blue Cross Blue Shield (BCBS), a major health insurance provider that insures over 100 million total members. BCBS made big claims that it really wants its plan holders to make it to their doctor’s appointments, and that they will pay for those Lyft rides. Although it is only offered for plan holders through their employers for now, we will see whether the new benefit will expand for people with insurance through Medicaid or Medicare. We are living in an era where technology is redefining care. And this is just one example how a common goal of removing barriers to receiving care can bring a pink smartphone app and a giant insurance company together. The caveat is that we are not sure how these Lyft rides will affect the premium. Will patrons be charged for every minute the driver waits, like the standard app does?

Cardiologists are #TeamiPhone

Imagine wearing a watch that could literally tell you if you were sick or not. With Apple’s Smart Watch, this is theoretically possible. The current model of the watch can test heart rate and even alarm users if they have atrial fibrillation. This early prevention can prevent stroke. However, after being tested for effectiveness, it was found that the current model was only 8% effective at sensing AFIB. With this news, Apple set out to improve their appliance. The company announced they were teaming up with researchers from Stanford University to perfect the smart watch to double as a mobile heart monitor. As the device is perfected, doctors can better rely on the watch’s detection. This will help save time and money in healthcare.

Mississippi’s Abortion Law Halted

No, the debate is not going to go away anytime soon. Before we get into what went down in Mississippi, remember that those who are pro-life and want to ban abortion want to get the minimum age of the fetus as low as possible. Mississippi and several other states banned abortion after 20 weeks amidst a push for federal law, while Planned Parenthood and others find this unconstitutional. President Trump supported the bill to ban abortions after 20 weeks, but was halted by the U.S. Senate two months ago. With this in mind, Mississippi’s Governor Phil Bryant this week signed the even stricter abortion law, to ban abortions after 15 weeks. Swiftly, Judge Carlton Reeves halted the bill for 10 days to hear more about it. Whether the Governor expected this to get through the judge we are not sure, but it is obvious that this debate will continue for a while. The medical community will serve an important role here to define what makes a fetus viable and what point cells become life. And even after more scientific definitions, our country will probably still fight about pro-life or pro-choice.

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