3 Major Bottlenecks of AI in Healthcare

In 2016, neural network pioneer and Turing Award winner Geoff Hinton made a bold prediction: “We should stop training radiologists now,” he said. “It is just completely obvious deep learning is going to do better than radiologists.”

Fast forward nearly a decade, and you’ll notice that while AI and machine learning (ML) models have made strides in image-based diagnosis and other medical tasks, radiologists haven’t yet gone anywhere.?

It’s a similar situation across the healthcare industry, where AI hasn’t had the paradigm-busting impact initially predicted. At least, not yet.

Just have a look at the share of U.S. job postings that require AI-related skills in the chart (that’s healthcare close to the bottom).

But even though companies such as Pera Labs, HyberAspect, NeuraLight, Protai, and others have made noise in the healthcare AI space, a series of bottlenecks have made full-on implementation by large hospitals and medical systems extremely difficult.?

Indeed, implementing?human-centric AI?is critical to its widespread adoption in healthcare, which can?improve patient satisfaction?and hospital efficiency. But several significant roadblocks stand in its way. Here are the most important.

Data Issues and AI Bias

AI models are useless without being fed high-quality data, which doesn’t happen nearly enough in healthcare. Despite the massive amounts of data produced by the healthcare space – around?30 percent?of all the world’s data – data quality issues have plagued the sector for years and have harmed the clinical implementation of AI.

Part of this is due to the?massive data surface area?that must be probed for relevant information:

• Medical databases containing peer-reviewed literature and studies such as PubMed, EMBASE, Cochrane Library, and MEDLINE
• Insurance databases
• Medical imaging databases
• Electronic health records (EHR) and electronic medical records (EMR)
• Postmarket surveillance

Much of this information is siloed in different repositories, often making healthcare data difficult to access and collect. Busy medical professionals often view data collection?as an inconvenience. Collected clinical data can be incomplete or contain errors. And EHR/EMR systems are often incompatible across various providers, resulting in localized data that are difficult to integrate.

Additionally,?data privacy considerations?around the presence of personally identifying information (PII) and protected health information (PHI) adds another challenge. Companies and healthcare systems need to be sure they’re on the right side of the Health Insurance Portability and Accountability Act (HIPAA) and other regulations before using healthcare data.

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