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Author: Anthony Chang

Articles by Anthony Chang

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Anthony Chang


After his cardiology training at the Children’s Hospital of Philadelphia with his research interest in mathematics and chaos theory in biomedicine, Dr. Anthony Chang was an attending cardiologist in the cardiovascular intensive care unit of Boston Children’s Hospital and an assistant professor at Harvard Medical School. Throughout his career as a pediatric cardiac intensive care physician, he has been interested in applications of biomedical data to decision-making. He completed his Masters of Science (MS) in Data Science with a sub-specialization in artificial intelligence from Stanford School of Medicine. He is also a computer scientist-in-residence at Chapman University. He is currently the Chief Intelligence and Innovation Officer and Medical Director of the Heart Failure Program at Children’s Hospital of Orange County.

He is the founder and medical director of the Medical Intelligence and Innovation Institute (MI3) that is supported by the Sharon Disney Lund Foundation. The institute, founded in 2015, is dedicated to the introduction and implementation of artificial intelligence in medicine and was the first institute of its kind in a hospital. He intends to build a clinician-computer scientist interface with a nascent society (the Medical Intelligence Society) and is the editor-in-chief of Intelligence-based Medicine, the accompanying journal for his book, Intelligence-Based Medicine: Artificial Intelligence and Human Cognition in Clinical Medicine and Healthcare. He is the organizing chair for Artificial Intelligence in Medicine (AIMed) meetings around the world, the largest and most comprehensive clinician-led meetings that focus on applications of artificial intelligence in medicine and the dean of the nascent American Board of Artificial Intelligence in Medicine (ABAIM).

The art of feature engineering: Machine and human synergy

“…some machine learning projects succeed and some fail. What makes the difference? Easily the most important factor is the features used.”
 -Pedros Domingo, author of The Master Algorithm

The COVID-19 pandemic rages on unrelentingly as we head into the winter months of 2020. The glimmer of hope is the group of innovative vaccines that have demonstrated high efficacy and hopefully high effectiveness as well. Innovation is so direly needed during this health crisis, and this mindset is exemplified by DeepMind of Google, where a team devised an impressive machine learning methodology called AlphaFold that is capable of translating a genomic sequence into a three-dimensional protein structure in a matter of minutes and hours (instead of years). This breakthrough by DeepMind to engender an in-depth understanding of protein structure will result in drug design improvements and improved patient outcomes.

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Ethics of artificial intelligence in medicine and healthcare: a suggested framework

“The prospect of AI with superhuman intelligence and superhuman abilities presents us with the extraordinary challenge of stating an algorithm that outputs super ethical behavior.”

-Nick Bostrom, professor in philosophy

This month, I am invited to elucidate my perspective on a relatively convoluted topic: ethics of artificial intelligence in healthcare. What is especially disconcerting is that the recent trajectory of advances in artificial intelligence is exponential while our discussions of ethics (and other issues on regulation and law) are lagging behind.

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Parameters and hyperparameters: a conundrum in machine and deep learning

"“Be confused. It is where you begin to learn new things.”

-S.C. Lourie, British writer

As we plan for the upcoming inaugural comprehensive review course for the nascent American Board of Artificial Intelligence in Medicine and its certification assessment, several topics appear to be conundra for especially those who are not from the data science domain and are therefore worth delineating here at AIMed.

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AI + XR: convergence of artificial intelligence and extended reality in healthcare

“At that time, we were not yet in a position to implement a comprehensive digital twin. I had been in the IT industry for over 30 years at the time and was firmly convinced that computers would be powerful enough someday to bring my ideas to life.” 

-Michael Grieves, American digital pioneer and progenitor of the digital twin concept

Extended reality (XR) includes its myriad of modalities: augmented (AR), virtual (VR), and mixed (MR) reality. Artificial intelligence and extended reality together can create a special synergy to help manage complicated operations and complex systems.

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Intelligence-based medicine: lessons learned after seventy manuscripts

“Maybe that’s enlightenment enough: to know that there is no final resting place of the mind; no moment of smug clarity. Perhaps wisdom… is realizing how small I am, and unwise, and how far I have yet to go.” 

-Anthony Bourdaine American cook/author and global traveler

We have remained in a viral apocalypse now for almost six months, with no obvious denouement to this virtual lockdown. It is not the “new normal” (as some of you may recall, I do not like this term, along with “social distancing”) but a “better normal” that we work adjusting towards.

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Screening test and interpretation for covid-19: sensitivity/specificity and positive/negative predictive values and the role of Bayes’ theorem (Part II)

“Humans, it turns out, are not very good at Bayesian inference, at least when verbal reasoning is involved. The problem is that we tend to neglect the cause’s prior probability.” 

Pedro Domingos, in The Master Algorithm


We remain in the most challenging global health crisis of our generation, with the real-time (or not so real-time) reverse transcriptase polymerase chain reaction (rRT-PCR) screening tests still not widely available. Even when the tests are available, the results are not in a timely manner so it renders these screening tests irrelevant. Last time, we delineated why a high sensitivity as well as high negative predictive value are preferred given that a false negative test has much higher level of significance than a false positive. Again, the general observation is that we humans tend to over-rely on the screening test itself (especially if it is in print, like the electronic health record that is often full of erroneous information) and concomitantly underestimate the importance of a pre-test assessment (patient symptoms, local prevalence of disease, exposure risk, etc). In one common scenario of a patient with suspicion for COVID-19, how would we interpret a negative screening test?

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