Medicine's Machine Learning Problem
I&#x27;m an eye surgeon and self-taught machine learning practitioner, I started to learn Python in 2016 when the deep learning hype was at his highest.<p>After 3 years of research, playing with datasets, extracting and cleaning data from EMR and from different machines, I not sure that the biggest problem with the so-called &quot;AI&quot; is the inequalities that it can induce ; it is rather, is it useful at all ? This is a little bit provocative so let me explain.<p>First, it took me a very long time to really, fully get that AI is not fundamentally different from a simple linear regression (infering a rule from data). More powerful, but definitely, no intelligence added. Most of my fellow doctor colleagues still think that AI is special, different, like magic; I also thought like this before learning to code.<p>Scores inferred from data were used in medicine from decades and fundamentally, nothing changed with the AI wave. I&#x27;m extremely impressed with the performances of GAN for image generation, and with what allows deep RL in controlled environments (which the human body is not): however, I can&#x27;t see any applications of those technologies in medicine.<p>Ok, Deep Learning allows to analyze images with a great level of performances. However, at the end of the day, nobody (informed) wants an AI diagnosis and the doctor will read the images. He will maybe have a pre-completed report : wow, incredible. We are very far from the disappearance of radiologists that Geoffrey Hinton took for granted a few years ago.<p>At this time, a team published in Nature a paper about a DL algorithm which could diagnose melanomas better than dermatologists, using a picture. Unfortunately, no real-life application. Why? Because when you suspect a melanoma, if you have any doubt, you won&#x27;t take a chance: you will take a biopsy. What is the interest of guessing the result of the biopsy that you will do anyway, because if you guessed wrong, the patient dies? No interest.<p>I also realized that it is extremely difficult, if not impossible, to use data from EMR out of the box. Medical data is dirty, intrinsically, because humans are complex things that do not fit easily in little boxes. Hence, if you want quality data, you have to think your data collection in advance, and motivate all your fellow doctors to check the little boxes correctly. For many years (we are talking about big data, no ?) Of course, there is some exceptions, but most of the time the data cleaning process is extremely hard to perform (however, if a dedicated team of people with medical knowledge is concentrated on this work, things could be different. I had to clean the data myself).<p>I&#x27;ll finish with the most ironic part : I dedicated a few years of my life to a topic where both optics and prediction from data are involved (intraocular lens calculation in cataract surgery). I tried a great deal of ML approaches, only to find recently that by better accounting for the optical specificities of the problem I was trying to solve, I obtained excellent results, better than with ML, even with a dumb multiple regression. Ouch. The lesson is : physics beats AI.<p>I would be happy to be challenged on this topic.