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Deep Learning and Radiology, False Dichotomy, Tools and a Paradigm Shift

From MIT Tech Review article called “Google shows how AI might detect lung cancer faster and more reliably” we have the following information:

Early warning: Danial Tse, a researcher at Google, developed an algorithm that beat a number of trained radiologists in testing. Tse and colleagues trained a deep-learning algorithm to detect malignant lung nodules in more than 42,000 CT scans. The resulting algorithms turned up 11% fewer false positives and 5% fewer false negatives than their human counterparts. The work is described in a paper published in the journal Nature today.

That reminds me of a lot of haterism, defensiveness, confirmation bias and especially a lack of understanding of technology and their potentials to help people worldwide. I’ll not cite most of this here but you can check in my Twitter @flavioclesio.

Some people from academic circles, especially from Statistics and Epidemiology, started in several different ways bashing the automation of statistical methods (Machine Learning) using a lot of questionable methods to assess ML even using one of the worst systematic reviews in history to create a false dichotomy between the Stats and ML researchers.

Most of the time that kind of criticism without a consistent argumentation around the central point sounds more like pedantism where these people say to us in a subliminal way: “- Hey look those nerds, they do not know what they are doing. Trust use <<Classical Methods Professors>>, We have <<Number of Papers>> in that field and those folks are only coders that don’t have all the training that we have.

This situation’s so common that In April I needed to enter in a thread with Frank Harrell to discuss that an awful/pointless Systematic Review should not be used to create that kind of point less dichotomy in that thread:

My point it’s: Statistics, Machine Learning, Artificial Intelligence, Python, R, and so on are tools and should be and should be treated as such.

Closing thoughts

I invite all my 5 readers to exercise the following paradigm shift: Instead to think

This AI in Health will take Doctors out of their jobs?

let’s change the question to

Hey, you’re telling me that using this very easy to implement free software with commodity CPU power can we democratize health exams for the less favored people together with the Doctors?

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  1. Deep Learning, Nature and Data Leakage, Reproducibility and Academic Engineering – Flávio Clésio

    […] I tackled one example of this some months ago related with a systematic review from Christodoulou Et al. called “A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models” that the authors want to start a confirmatory study without a clear understanding of the methodology behind of Machine Learning and Deep Learning papers (you can read the full post here). […]