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Updates from the intersection of AI and healthcare

Systemic racism in healthcare: role of artificial intelligence in safeguarding equity

“If we are not careful, AI will perpetuate the bias in this world. Computers learn how to be racist, sexist, and prejudiced in a similar way that a child does. The computers learn from their creator-us.” 

Aylin Caliskan, computer scientist

 

The COVID-19 pandemic and the protests for racial equality rage on unrelentingly as dual forces that are forcing change. There is an underlying irony in that even in the pandemic that can be indiscriminately lethal for any human on this planet, there is a very obvious and disheartening racial disparity in morbidity and mortality with Blacks and Latinos disproportionately affected.

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The second pandemic: artificial intelligence and its role in racism and inequity

“Algorithms are still made by human beings, and those algorithms are still pegged to basic human assumptions. They’re just automated assumptions. And if you don’t fix the bias, then you are just automating the bias.” 

-Representative Alexandria Ocasio-Cortez (D-NY)

 

We are in the midst of a second pandemic. No, not the “second wave” of this horrid coronavirus pandemic. The second pandemic is the one that is erupting after it has been in our cultures for centuries and is even more destructive than COVID-19: the pandemic of systemic racism in regions around in the world. The unrelenting protests seen around the world is the final clarion call to rid of this supremely unjust human-inflicted scourge of our societies.

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SpaceX launch and the Covid-19 pandemic: complicated vs complex

“Simple and complicated contexts assume an ordered universe, where cause-and-effect relationships are perceptible, and right answers can be determined based on the facts. Complex and chaotic contexts are unordered, and there is no immediately apparent relationship between cause and effect, and the way forward is determined based on emerging patterns.“ 

-David Snowden, in Harvard Business Review’s A Leader’s Framework for Decision Making (in describing the Cynefin Framework)

 

This past Saturday, amidst the COVID-19 pandemic and widespread protests for the unjust death of George Floyd, the U.S. launched two astronauts into space. The vehicle was the SpaceX Falcon rocket with its Crew Dragon capsule and this event opened a new era of space travel. This journey, with Elon Musk as the privileged vanguard, is symbolic of the future with a partnership between SpaceX and NASA and implementation of AI as an essential part of its sciences. A close colleague posited about artificial intelligence: even if we launched SpaceX with an incredulous return of its booster back to Earth (including practice returns on a drone ship at sea), maybe AI is still not as good as we think if we cannot seem to make even reasonable predictions about the course of the COVID-19 pandemic.

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Intelligence in the Covid-19 pandemic: Decision analysis and its clarity of action

“The only thing you can do that will change your future life are the decisions that you make. Otherwise you are not the wind, but the leaf in the wind.” 

-Ronald Howard, retired Stanford professor of Electrical Engineering

 

There is a supreme imbroglio now with the COVID-19 pandemic as it runs its apocalyptic course around the world. With very few exceptions (Taiwan, Singapore, New Zealand, South Korea, etc), it is obvious that leaders and experts have made and continue to make decisions that lack clarity and foresight. So it is timely to consider: how does one make a good or even great decision?

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The Top Ten Mistakes Healthcare Executives Can Make and How to Avoid These

“Some executives think that artificial intelligence is a sentient being, but it is simply a better way to turn data into actionable insights.“ 

-Ryan Detert, CEO, Influential

 

Artificial intelligence (AI), with its machine and deep learning, natural language processing, and robotic process automation tools, has become an integral part of many sectors in society and now more than ever in healthcare as well. More than 90% of healthcare executives agree that artificial intelligence improves healthcare, and healthcare AI startups raised close to $1 billion in Q4’2019 alone (Gil Press of Forbes, February 21, 2020). With a coalition of enlightened leaders within a healthcare organization, artificial intelligence enables interdisciplinary collaboration and yields valuable dividends. Here is a list of ten common mistakes that healthcare executives can make and how to avoid them:

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Artificial Intelligence and COVID-19: How Pandemics Will Be Eradicated in the Future

“What’s natural is the microbe. All the rest-health, integrity, purity (if you like)-is a product of the human will, of a vigilance that must never falter."  

The Plague by Albert Camus, French author

 

SARS. MERS. Ebola. These are familiar names of recent pandemics that strike fear even amongst seasoned global healthcare workers even though the combined mortality (774, 38, and 11325 deaths respectively for a total of 12,137) was less than the number of people who have already succumbed to the current coronavirus pandemic (18,605 worldwide including over 700 in the US of as March 24th).

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Can we approach AI in medicine the same way as we do in other industries?

“What we have done is use natural language processing and machine learning techniques to train this engine to recognize whether this is an outbreak of anthrax in Mongolia versus a reunion of the heavy metal band Anthrax.” 

-Kamran Kahn, CEO, BlueDot

 

Coronavirus, or COVID-19 (2019-nCov), started in Wuhan, China in December of 2019, was manifested by a cluster of people with pneumonias that may have originated from live animals at the Hunan Seafood Market. As of middle of February, it is estimated that close to 100,000 people have been infected with close to 2,000 deaths worldwide.

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Remaining Predictions for the Year Revealed

“Outwork your potential, don’t fear failure, and create something timeless.” 

-Kobe Bryant, NBA basketball player

 

The world mourned the loss of star basketball player, Kobe Bryant, whose helicopter crashed near Los Angeles along with 8 others, including his daughter Gianna. He is most famous for his extreme work ethic (his “Mamba Mentality”) as well as his relentless curiosity about everything. We can all aspire to his supreme dedication in our work in AI in medicine and healthcare.

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AI in Medicine in 2020: Predictions

“Our intelligence is what makes us human, and AI is an extension of that quality.“  

-Dr. Yann Lecun, Professor, New York University

 

In the spirit of the new year, here are a few of my predictions for this year for AI in medicine and healthcare (five predictions here and another five predictions will be included with the next newsletter):

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Educating Medical Students and Clinicians on Artificial Intelligence

“We came all this way to explore the Moon, and the most important thing is that we discovered the Earth.” 

-William Anders, Apollo 8 astronaut and photographer of Earthrise


It is time for all of us to contrive an artificial intelligence educational agenda for all students and clinicians to have the background in data science and artificial intelligence (along with other advanced technologies) that we will need for the next few decades. It is of vital importance to review statistics, data and databases, and biomedical informatics as well as principles and applications of artificial intelligence in medicine.

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Continuing our mission of transforming healthcare

“These are not just numbers. I want to tell the stories of each person“ 

-Marie Colvin, Deceased War Correspondent

 

Pulmonary edema, heart failure, ventricular tachycardia, pulmonary hypertension, acute respiratory distress syndrome, and pneumothorax are medical terms to the data scientists (more accurately “strings”) working on AI in medicine projects. These are, however, medical issues that I suffered this past week as I learned that I had developed acute mitral regurgitation from a chordal rupture of my mitral valve and needed urgent open heart surgery. While these are medical terms, people suffer to yield these terms. It is daunting to not be able to breathe and become air hungry, for instance, with pulmonary edema (you literally feel like you are drowning). In a special way, I feel even closer to my own patients with congenital heart disease now as I have had the same journey as they have. So for my data scientist and artificial intelligence colleagues, please remember that these medical terms all signify human suffering and pain.

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Need for a Sense of Urgency

“You must unite behind the science. You must take action. You must do the impossible. Because giving up can never ever be an option.“ 

-Greta Thunberg, teenage climate activist

 

The past few weeks we heard these impassioned words spoken with bravura from Greta Thunberg, the young teenager and climate activist who assiduously stressed the lack of a sense of urgency of potential interventions to mitigate the effects of climate change. At a recent panel discussion on AI in medicine and healthcare, I mentioned that perhaps we can all learn from her understandable exasperation when we face the myriad of challenges in artificial intelligence in medicine and healthcare.

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Artificial Intelligence: The Apollo Program of the Present Era

“Artificial intelligence is the Apollo program for the 21st century.”  

-Demis Hassabis, Founder of DeepMind

 

One of the most renowned AI efforts is Google’s DeepMind, founded by neuroscientist Demis Hassabis. His philosophy is to “solve intelligence then use that to solve everything else”, and his DeepMind enterprise has soundly defeated human champions in the games. Go (AlphaGo) as well as other games (AlphaZero).

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Accuracy, Outcome, and Behaviour

“Accuracy and clarity of statement are mutually exclusive.”  

-Neils Bohr, Danish physicist and Nobel laureate

 

I recently returned from a few meetings on machine learning in medicine and healthcare, and many abstracts and presentations were focused on the use of Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) as a performance measure of classification problems (also known as Area Under the Receiver Operating Characteristic, or AUROC). The higher the AUC, as we know (or lead to believe), the better the model is at distinguishing between classes (such as disease vs no disease). A few thoughts:

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APOLLO 11’s almost-aborted lunar landing: lessons learned

“It’s a 1202… What is that? Give us a reading on the 1202 Program Diagram.”  

-Neil Armstrong, Apollo 11 Commander, minutes before lunar landing 

 

We have just celebrated the 50th anniversary of the lunar landing, arguably the most significant news event of this century. One relatively small but significant moment during this monumental event was Apollo 11’s 1202 Alarm that occurred at about 3,000 feet up from the lunar surface that almost lead to an abort of the historic landing.

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Which Musical Ere Are We In?

“Mozart’s music always sounds unburdened, effortless, and light. This is why it unburdens, releases, and liberates us.” 

-Karl Barth, Swiss theologian

 

I am a lifelong devotee to great music, from classical music to Lady Gaga, and even an occasional reggae and rap. I was pondering: If we put in parallel the history of AI in medicine with the history of Western music (Medieval, Renaissance, Baroque, Classical, Romantic, and 20th/21st Century), which period would AI in medicine be?

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Social Good & AI

“The choice for mankind lies between freedom and happiness and for the bulk of mankind, happiness is better.” 

-George Orwell, English novelist of Animal Farm and 1984

 

A friend and colleague and I had a spirited debate about the present advantages and concerns about AI in general. The discussion was particularly focused on China’s Social Credit System, which is an AI-enabled surveillance system (with close to 500 million cameras) that leverages facial recognition and big data analytics to monitor every citizen’s social behavior, good and suboptimal (the latter including failure to pay taxes, jaywalking, and spreading false information).

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Seeing the Unseeable

“We have seen what was unseeable.” 

-National Science Foundation announcement on April 10, 2019

 

The first ever picture of the most fascinating structure in the universe, the black hole, was essentially a data science feat coupled with an international network of 8 radio telescopes functioning as a single planetary receiver. The supermassive black hole at the center of a giant galaxy 55 million light-years away called Messier 87 was successfully visualized after astronomers and data scientists analyzed the petabytes of raw data derived from this virtual consortium of telescopes. A key architect of the algorithm that eventually lead to this historic picture is an inspiring 29 year-old Katie Bouman, who will soon be an assistant professor at California Institute of Technology.

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About the Author

Anthony Chang 500px copy

Anthony Chang

Founder

Dr. Chang is the Chief Intelligence and Innovation Officer (CIIO) and Medical Director of the Heart Failure Program at Children’s Hospital of Orange County. He has also been named a Physician of Excellence by the Orange County Medical Association and Top Cardiologist, Top Innovators in Healthcare.