What can the history of cloud computing teach us about the future of AI?

Photo by Steve Johnson on Unsplash

*Reposted* — originally published on 4/21/2021 on byte2bedside.com

There is much excitement about AI in healthcare, but we still don’t quite understand how it can produce impact at a meaningful scale.

Discussions about AI often jump to examples of machine learning (ML) models that perform eye catching tasks such as interpreting X-rays and diagnosing disease. However, although there is no shortage of published papers describing highly accurate ML models that predict all sorts of clinical outcomes, there is a dearth of actual AI products that are widely adopted and deliver value.

Common explanations for this gap between research and real world application include low risk tolerance in healthcare, complexity and messiness of healthcare data that limit the ability to train ML models, lack of IT infrastructure for deploying ML, and unclear ways to integrate ML models into clinical workflows. In essence, the problem, as described, centers around limitations in being able to train and deploy ML models and understanding what to do with them.

These explanations tell part of the story, but I think there is a deeper gulf of understanding that precludes the use of AI in healthcare in a way that delivers meaningful impact. To explain further, I find it useful to draw from the history of cloud computing.

The concept of the cloud was born out of a technology known as virtualization, which for a non-IT professional like myself, can be simplified as the ability to duplicate the functionality of a physical computer into multiple entities beyond the original hardware. So if I have a computer with 500GB of hard drive space, 2.3GHz CPU, and 32GB RAM that contains a set of files and software, virtualization would allow me to distribute the storage space, processing speed, and memory across multiple other “guest” computers via virtual machines that can run the same software and access the same files located on the original host physical computer. This functionality, which was first developed decades ago and iterated over time, is foundational to the ability to flexibly distribute and scale IT resources and construct what is commonly referred to as the “cloud.”

Cloud computing has now made its way well beyond concept into being a core component of the modern economy. To appreciate its full impact, we need to look beyond just the traditional cloud service providers (eg. AWS, Azure, GCP) to the vast array of businesses and products that are made possible by cloud architectures such as infrastructure as a service (IAAS), platform as a service (PAAS), and software as a service (SAAS). While a company like Netflix may incorporate cloud computing into its tech stack to build its products, its business value is generated from what is enabled by the cloud, rather than directly from the cloud itself. Netflix did not start its business sitting on a bunch of virtual machines and wondering what to do with them; rather, they identified a set of unaddressed needs in the market and realized that cloud computing would enable a suite a products that fulfilled those needs.

I find it useful to compare AI to cloud computing in that both ML and virtualization are significant methodological advances that can deliver new functionalities and enable products and business models that previously were not possible. But just as the early developers of virtualization software probably did not envision the creation of Netflix, it is also difficult for the AI community to fully appreciate what future products and business models can be made possible by AI. We need to look beyond the immediate applications of ML models to the higher order derivatives that may emerge from functionalities that AI enables. By re-framing the question from “what can we do with a more accurate ML model” to “what do we need to change in healthcare, and what future state products, care processes, and business models needed to deliver these changes can be made possible by AI,” we can greatly expand the opportunity space for AI in healthcare.