By Katharine Miller
Of all the industries romanticizing AI, healthcare organizations may be the most smitten. Hospital executives hope AI will one day perform healthcare administrative tasks such as scheduling appointments, entering disease severity codes, managing patients’ lab tests and referrals, and remotely monitoring and responding to the needs of entire cohorts of patients as they go about their daily lives.
By improving efficiency, safety, and access, AI may be of enormous benefit to the healthcare industry, says Nigam Shah, professor of medicine (biomedical informatics) and of biomedical data science at Stanford University and an affiliated faculty member of the Stanford Institute for Human-Centered Artificial Intelligence (HAI).
But caveat emptor, Shah says. Buyers of healthcare AI need to consider not only whether an AI model will reliably provide the correct output — which has been the primary focus of AI researchers — but also whether it is the right model for the task at hand. “We need to be thinking beyond the model,” he says.
This means executives should consider the complex interplay between an AI system, the actions that it will guide, and the net benefit of using AI compared with not using it. And, before executives bring any AI system on board, Shah says, they should have a clear data strategy, a means of testing the AI system before buying it, and a clear set of metrics for evaluating whether the AI system will achieve the goals the organization has set for it.