Generative AI is coming for healthcare, and not everyone’s thrilled
Generative AI, which can create and analyze images, text, audio, videos and more, is increasingly making its way into healthcare, pushed by both Big Tech firms and startups alike.
Google Cloud, Google’s cloud services and products division, is collaborating with Highmark Health, a Pittsburgh-based nonprofit healthcare company, on generative AI tools designed to personalize the patient intake experience. Amazon’s AWS division says it’s working with unnamed customers on a way to use generative AI to analyze medical databases for “social determinants of health.” And Microsoft Azure is helping to build a generative AI system for Providence, the not-for-profit healthcare network, to automatically triage messages to care providers sent from patients.
Prominent generative AI startups in healthcare include Ambience Healthcare, which is developing a generative AI app for clinicians; Nabla, an ambient AI assistant for practitioners; and Abridge, which creates analytics tools for medical documentation.
The broad enthusiasm for generative AI is reflected in the investments in generative AI efforts targeting healthcare. Collectively, generative AI in healthcare startups have raised tens of millions of dollars in venture capital to date, and the vast majority of health investors say that generative AI has significantly influenced their investment strategies.
But both professionals and patients are mixed as to whether healthcare-focused generative AI is ready for prime time.
Generative AI might not be what people want
In a recent Deloitte survey, only about half (53%) of U.S. consumers said that they thought generative AI could improve healthcare — for example, by making it more accessible or shortening appointment wait times. Fewer than half said they expected generative AI to make medical care more affordable.
Andrew Borkowski, chief AI officer at the VA Sunshine Healthcare Network, the U.S. Department of Veterans Affairs’ largest health system, doesn’t think that the cynicism is unwarranted. Borkowski warned that generative AI’s deployment could be premature due to its “significant” limitations — and the concerns around its efficacy.
Google Cloud, Google’s cloud services and products division, is collaborating with Highmark Health, a Pittsburgh-based nonprofit healthcare company, on generative AI tools designed to personalize the patient intake experience. Amazon’s AWS division says it’s working with unnamed customers on a way to use generative AI to analyze medical databases for “social determinants of health.” And Microsoft Azure is helping to build a generative AI system for Providence, the not-for-profit healthcare network, to automatically triage messages to care providers sent from patients.
Prominent generative AI startups in healthcare include Ambience Healthcare, which is developing a generative AI app for clinicians; Nabla, an ambient AI assistant for practitioners; and Abridge, which creates analytics tools for medical documentation.
The broad enthusiasm for generative AI is reflected in the investments in generative AI efforts targeting healthcare. Collectively, generative AI in healthcare startups have raised tens of millions of dollars in venture capital to date, and the vast majority of health investors say that generative AI has significantly influenced their investment strategies.
But both professionals and patients are mixed as to whether healthcare-focused generative AI is ready for prime time.
Generative AI might not be what people want
In a recent Deloitte survey, only about half (53%) of U.S. consumers said that they thought generative AI could improve healthcare — for example, by making it more accessible or shortening appointment wait times. Fewer than half said they expected generative AI to make medical care more affordable.
Andrew Borkowski, chief AI officer at the VA Sunshine Healthcare Network, the U.S. Department of Veterans Affairs’ largest health system, doesn’t think that the cynicism is unwarranted. Borkowski warned that generative AI’s deployment could be premature due to its “significant” limitations — and the concerns around its efficacy.
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