Quite often, the best way to gauge the potential success of a new technology is to follow the money.
For example, a recent commentary by financial giant Morgan Stanley points to what many healthcare stakeholders already know: “Pressed to reduce costs and boost productivity, medical equipment manufacturers and technology companies are increasingly investing in AI.”
More specifically, Michael Jungling, head of Morgan Stanley Research’s Medical Tech and Services team, explained, “Based on our analysis of AI capabilities, as well as discussions with executives and industry experts, we’re seeing a number of applications across the entire healthcare spectrum, from prevention to diagnosis to follow up.”
In a recent report, Jungling and his colleagues found that, while hurdles to the development and deployment of MedTech AI lay ahead—including questions around regulations and privacy of patient data—the successful implementation of AI in the field could boost productivity, lower treatment costs and drive growth across the healthcare value chain. Indeed, the company estimates that the global market for AI in healthcare could surge from $1.3 billion today to $10 billion by 2024, growing at an annual compound rate of 40%.
As the financial analysts see it, medical AI has great potential, from managing dialysis to optimizing patient-dosing to early disease detection. Indeed, even relatively modest deployments of AI, such as assistive intelligence, which helps reduce manual processes and simple but repetitive tasks, such as appointment scheduling, would help give skilled medical staff more time for specialized and revenue-generating work.
According to Jungling, “The timelines for adoption of AI-enabled MedTech will likely be determined by the tangible economic benefits produced by the product and the ease of usability and integration into existing workflows.”
At the same time, more advanced forms of AI could help medical professionals with their decision-making, by evaluating diagnostic images and creating treatment plans. This form of AI, known as unsupervised machine learning, can assess raw unstructured data and search for patterns.
“Such functionality could lead to dramatic improvements in productivity, especially in clinical settings where the supply of highly skilled professionals is limited," said Jungling.
Of course, even the financial guys know AI won’t be adaptable to all of healthcare’s needs. Specialties, such as corrective lenses, chronic care or orthopedics, are more difficult fits, while other fields may not offer sufficient profitability or cost savings. And “other potential barriers include the plausibility of the technology and regulatory approval for use in medical settings, as well as anonymizing patient data to ensure individual privacy within large data pools.”