AI and Healthcare: What is it Good For?
As with all technological advances, it is unnecessary for CXOs to understand the detailed inner workings of a piece of AI software, just as it is unnecessary for them to understand the detailed engineering of their mobile phones’ operating systems. But what they do need to know about AI is the answer to one question: What is it good for?
Right now, most senior executives don’t have a clear answer to this question, and thus many companies are undertaking pilots addressing the wrong business problems, measuring the wrong short-term metrics, and trying to build platforms and roadmaps which — at this stage of AI’s maturity — will often do more harm than good.
Nowhere has this been truer than in the attempt to bring AI to the life sciences industry. Capital has been pouring into the task. Healthcare AI start-ups raised almost $1 billion of venture capital1 just in the second quarter of 2019. Large incumbents are also making enormous investments in internal AI capabilities. Novartis, as an example, has roughly 250 full-time data scientists2, has built a global AI hub, and has created major AI partnerships with IBM Watson, MIT, Intel and Quantum Black.
Unsurprisingly, much of this investment has been focused on trying to unlock huge, previously unsolved problems. There are more than 150 significant venture-backed AI companies3 just focused on the topic of drug development. In fact, there are so many now that they appear to be running out of name ideas — for instance, there are three separate AI-driven companies called, respectively, Helix, HelixAI and Healx.
But senior executives with P&L responsibility in publicly-traded corporations cannot exist forever on press releases, long term projections and big dreams. Eventually they have to produce results. And these leaders are now starting to say out loud what has been whispered all along by life sciences veterans: there is a huge disconnect between rhetoric and reality.
In one three-month period in mid-2019:
WHAT AI IS GOOD FOR
AI on television is robots playing Jeopardy. But in our experience, AI that makes money for large corporations almost always follows a specific pattern: it is software that uses data + math to create statistical improvement in a repetitive business decision process.
- Operational processes like the details of executing clinical trials.
- Back-office processes like finance or procurement.
- Precisely stratfying groups of patients by risk + advanced characteristics, like their likelihood to respond to interventions and preventative measures.
- Go-to-market processes like targeting detailing efforts, DTC advertising, and digital outreach.
- Sub-processes within drug discovery where there have been significant successes, such as synthesis prediction9, automated compound synthesis10, bioactivity modeling11 or using image recognition12 to analyze phenotypic screening data.
In fact, once you know what to look for, profitable AI opportunities are hiding in plain sight everywhere inside of a large life sciences company. “Isn’t this just process automation,” one might ask? Yes, in a sense, but it is automation (and to be more accurate, often semi-automation) of a specific kind of process: cognitive business processes that require decision-making under conditions of uncertainty.
CONCLUSION
AI visionaries have spent years making ambitious claims about the potential transformative impact of AI on life sciences, but most of the real successes have resulted from using AI to improve less glamorous operational processes. We have seen executives who follow the guidelines in this paper successfully capture this opportunity, creating significant value within surprisingly short timelines. We hope these concepts help you in this important domain and more broadly as you consider how to drive focused, practical, and profitable AI initiatives within your organization.
~ the Curia team
