When we asked dozens of venture capitalists where they see the most potential for applied artificial intelligence, they unanimously agreed on healthcare. Technology has already been used to incrementally improve patient medical records, care delivery, diagnostic accuracy, and drug development, but with A.I. we could achieve exponential breakthroughs.
Deep learning first caught the media’s attention when a team from the lab of Geoffrey Hinton at the University of Toronto won a Merck drug discovery competition despite having no experience with molecular biology and pharmaceutical development. Recently, a multidisciplinary research team at Stanford’s School of Medicine comprised of pathologists, biomedical engineers, geneticists, and computer scientists developed deep learning algorithms that diagnose lung cancer more accurately than human pathologists.
The ultimate dream in healthcare is to eradicate disease entirely. This dream might be possible one day with the assistance of AI, but we have a very very long way to go.
INNOVATION IS CHALLENGED BY RISK-AVERSION
“Healthcare as a system advocates ‘do no harm’ first and foremost. Not ‘do good’, but ‘do no harm’. Every application of A.I. in healthcare is regulated by that fundamental philosophy,” cautions Kapila Ratnam, PhD, a scientist turned partner at NewSpring Capital. Additionally, Lisa Suennen, Managing Director at GE Ventures highlights that “the single biggest contribution to excess cost and error in healthcare is inertia.” The attitude of “this is how it’s always been done” is literally killing people.
Other investors agree that the ultra conservatism in the healthcare system, while intended to protect patients, also harms them by restricting innovation. Gavin Teo, Partner at B Capital Group and a specialist in digital health, cites “provider conservatism and unwillingness to risk new technology that does not provide immediate fee-for-service (FFS) revenue” as a major challenge for startups tackling healthcare. Teo also points out that the industry feels burned from recent experiences, such as “ electronic medical records (EMR) digitization regulations, which were overhyped and resisted.”
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