I work for a company that does machine learning on clinical notes. The challenges the author introduces are real, but he misses the mark on the last point "Only

I work for a company that does machine learning on clinical notes. The challenges the author introduces are real, but he misses the mark on the last point "Only Partially a Problem: Regulation and Fear." Actually, regulation and fear are the main reasons that machine learning hasn't taken off in clinical medicine. More precisely, the provider's fear of getting sued and the regulations that require a licensed practitioner to "have the final say." There is one more problem as well --> machine learning doesn't solve a problem that providers think they have. It's lesson #1 from The Lean Startup or The Startup Owner's Manual. You may have the best EKG-reading software in the world (I have no doubt computers could surpass providers on this task), but if the providers don't feel they need it, it simply won't be adopted. This is the Watson situation at heart. Conversely, here are some areas in medicine where machine learning has been adopted: 1. Medical billing code generation: Several companies have systems for reading notes using natural language processing and predicting billing codes using market-basket analysis. 2. Identifying bacterial cultures: Inpatient bacterial cultures are placed in a big incubator and constantly scanned for growth. When growth is suspected, there are emerging algorithms to automatically classify the bacteria. Similar work is being applied to other areas of pathology (see: http://www.nature.com/articles/ncomms12474 http://www.nature.com/articles/ncomms12474) 3. Image-analysis in radiology: There are a few radiology companies that are demonstrating superior results by applying novel algorithms. While not "machine learning" per se, the existence of such algorithms is encouraging for future advancements in radiology, since it's a step beyond just viewing the image. Here's one such company that has gained FDA approval for their blood flow mapping technology: http://www.ischemaview.com/ http://www.ischemaview.com/