In many AI/ML papers, classifiers are scored by well they do compared to human doctors. For example, a (made up) title could be “MyNextGenClassifier does 0.6% better at finding 2 mm brain bleeds than human radiologists at Memorial Sloan Kettering“. Let’s unpack that. The title implies that the goal is to do “better” than a human, where better is defined as higher classification accuracy. This is the kind of thinking that got IBM into trouble with their attempt to “revolutionize” cancer care. In their original take on cancer care, the notion was that their technology would serve as an adjunct to 12 world class human cancer doctors, and make sure that e.g. new therapies or drug combinations would not be missed.
Let’s think about this from a different angle. For code running on a silicon-based computer, there are dozens of potential optimization functions. Beyond accuracy relative to a human, there are also cost-per-diagnosis, energy efficiency, reliability, accessibility, stability over time, scalability, privacy, and ease of use. Unfortunately, considering the set of medical AI papers (of which there are somewhere between 25,000 to 75,000, depending on how you count), we have somehow navigated ourselves into a corner – the vast majority of these papers focus on accuracy, which is not really where Healthcare AI shines.
For something different, we could for example look at the energy-efficiency and climate impact of a hospital with 200 human doctors vs. a hospital with zero human doctors (and only nurses). This type of hospital would presumably also be more cost effective and better able to grow and shrink with real-time patient demand, such as during a pandemic. Similarly, we could ask about the hidden cost of untreated/undiagnosed conditions. Especially in communities of color, the US healthcare system struggles to provide suitable levels of care – patients might not have insurance, they may not trust their local providers, or there may be any one of many barriers that can make it hard to access care. Digital health classifiers and recommendation engines can offer convenience, 24/7 ease of access, and privacy guarantees that are hard to realize in a traditional medical setting.
The real power of digital health is not to be 0.6% better than a typical human doctor, but to provide entirely new capabilities that health systems built around human doctors fundamentally cannot. Most obviously, once a classifier has been trained, deployed, and is used to help 1 million people, it costs almost nothing to use the same classifier to help all 7.9 billion people on earth. Why not make the entire diagnosis step of healthcare all-digital (and free)? With a relatively modest investment, it is entirely conceivable to build (and open-source) classifiers for the top 10 human health conditions and make those available globally. The world’s computers run on open-source software – the world’s health diagnostics system should, too.
Biology, Data, and Networks 