SPECIAL REPORT: Tech Optimization: Getting the most out of AI
Six healthcare artificial intelligence experts offer best practice suggestions for CIOs and other health IT leaders seeking to fine-tune their AI systems.
Artificial intelligence is a highly complex technology that, once implemented, requires ongoing oversight to make sure it is doing what is expected of it and ensure it is operating at optimal levels.
Healthcare provider organizations using AI technologies also need to make sure they’re getting the biggest bang for their buck. In other words, they need to optimize the AI so that the technologies are meeting the specific needs of their organizations.
We spoke with six artificial intelligence experts, each with extensive experience in healthcare deployments, who offered comprehensive advice on how CIOs and other health IT workers can optimize their AI systems and approaches to best work for their provider organizations.
Optimizing AI depends on the understanding of what AI is capable of and applying it to the right problem, said Joe Petro, chief technology officer at Nuance Communications, a vendor of AI technology for medical image interpretations.
“There is a lot of hype out there, and, unfortunately, the claims are somewhat ridiculous,” he said. “To optimize AI, we all need to understand: the problem we are trying to solve; how AI can solve the problem; can an existing capability be augmented with AI; and, when AI is not helpful.”
For example, is “traceability” important? AI has a well-known “black box limitation” — every fact or piece of evidence that contributed to a decision or conclusion made by the neural net is not always known.
“It is sometimes impossible to trace back through the bread crumb trail leading to the conclusion made by the neural net,” Petro explained. “Therefore, if traceability is a requirement of the solution, you may need to retreat to a more traditional computational methodology, which is not always a bad thing.”
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