AI in health and medtech has been around and in the field for ages. However, two persistent challenges make roll out slow-- and they’re not going anywhere because of the stakes at hand.
The first is just straight regulatory. Regulators don’t have a very good or very consistent working framework to apply to to these technologies, but that’s in part due to how vast the field is in terms of application. The second is somewhat related to the first but really is also very market driven, and that is the issue of explainability of outputs. Regulators generally want it of course, but also customers (i.e., doctors) don’t just want predictions/detections, but want and need to understand why a model “thinks” what it does. Doing that in a way that does not itself require significant training in the data and computer science underlying the particular model and architecture is often pretty damned hard.
I think it’s an enormous oversimplification to say modern AI is just “fancy signal processing” unless all inference, including that done by humans, is also just signal processing. Modern AI applies rules it is given, explicitly or by virtue of complex pattern identification, to inputs to produce outputs according to those “given” rules. Now, what no current AI can really do is synthesize new rules uncoupled from the act of pattern matching. Effectively, a priori reasoning is still out of scope for the most part, but the reality is that that simply is not necessary for an enormous portion of the value proposition of “AI” to be realized.
AI in health and medtech has been around and in the field for ages. However, two persistent challenges make roll out slow-- and they’re not going anywhere because of the stakes at hand.
The first is just straight regulatory. Regulators don’t have a very good or very consistent working framework to apply to to these technologies, but that’s in part due to how vast the field is in terms of application. The second is somewhat related to the first but really is also very market driven, and that is the issue of explainability of outputs. Regulators generally want it of course, but also customers (i.e., doctors) don’t just want predictions/detections, but want and need to understand why a model “thinks” what it does. Doing that in a way that does not itself require significant training in the data and computer science underlying the particular model and architecture is often pretty damned hard.
I think it’s an enormous oversimplification to say modern AI is just “fancy signal processing” unless all inference, including that done by humans, is also just signal processing. Modern AI applies rules it is given, explicitly or by virtue of complex pattern identification, to inputs to produce outputs according to those “given” rules. Now, what no current AI can really do is synthesize new rules uncoupled from the act of pattern matching. Effectively, a priori reasoning is still out of scope for the most part, but the reality is that that simply is not necessary for an enormous portion of the value proposition of “AI” to be realized.