Bridging the chasm between AI and clinical implementation


Many advances in artificial intelligence (AI) for health care using deep neural networks have been commercialised. But few AI tools have been implemented in health systems. Why has this chasm occurred? Transparency, suitability, and adaptability are key reasons.

The deployment of any new technology is usually managed centrally in hospitals and health systems. For the information technology (IT) teams, there is the concern that input data are drawn from outside the health setting and the algorithm performance, source code, and input data are unavailable to review. Many commercial AI applications are in radiology, but few are supported by evidence from published studies. And there are concerns that the algorithms were tested and validated using retrospective, in-silico data that may not reflect real-world clinical practice. Regulators reviewing a company’s AI data are privy to considerable data, but these data are usually unavailable to health system IT teams or clinicians.

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