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Startups Applying AI To Transform Healthcare

Machine learning and healthcare are in many respects uniquely well-suited for one another. At its core, much of healthcare is pattern recognition. A healthy human body and its various subsystems function in consistent, quantifiable ways. When a human organism is suffering from some affliction, it deviates from this homeostasis in ways that tend to be predictable across time and populations.

Using computer vision to identify health conditions in medical images has become perhaps the most widely referenced use case for AI in healthcare. It is easy to understand why: examining a medical scan to determine whether a tumor, a skin lesion, a retinal disease, or some other indication is present is a clear-cut example of object classification, exactly what deep learning excels at

Recent advances in natural language processing have made possible AI-based conversational interfaces that can automate patient screening and care navigation. For example, patients can share symptoms and questions via text message and receive automated clinical guidance in response. Similarly, AIs can be developed that communicate with patients on an ongoing basis to ensure that they remain engaged and compliant with their care regimen.

COVID-19 has greatly accelerated the adoption of remote health: the delivery of clinical services to patients over distance rather than in-person, using digital tools.

While the pandemic has served as a near-term catalyst, many experts believe that remote health (also called telehealth) is on its way to becoming a permanently important pillar of healthcare delivery. McKinsey estimates that up to $250 billion of healthcare spend will be virtualized in the coming years in the United States alone.

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