Tag Archives: continuous monitoring

Navigating AI Decisions: Going Beyond “Buy vs Build”

Conceptual illustration of an AI buy-versus-build crossroads with governance and monitoring decisions

“Should we buy or build?” is usually the first question organizations ask about radiology AI. It is useful, but it is no longer the whole question.

Today, you can choose from cleared clinical algorithms, enterprise AI platforms, generative-AI tools, and internally developed workflows. The real challenge begins after a contract is signed or a prototype works. AI becomes another clinical system: one that can help, distract, confuse, or quietly change behavior.

Anyone who has lived through an enterprise IT rollout, PACS replacement, or hospital merger (let alone lead an aspect of it) knows the pattern. Installation is only one part of deployment. The hard work is making a tool fit the people, data, incentives, exceptions, and failure modes of the place where it will be used.

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FDA AI Guidance and the Hard Part of Transparency

The least glamorous part of AI in radiology may turn out to be the most important: telling people what changed.

That sounds simple. It is not. A diagnostic AI tool may be trained on one dataset, validated on another, deployed inside a PACS or reporting workflow, monitored after release, and then updated when the model, threshold, input, interface, or intended environment changes. Somewhere in that chain, a radiologist is expected to decide whether to trust a box, a score, a contour, a triage flag, or a sentence.

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The FDA has an Idea, or 10, on Good Machine Learning Practice

Good machine learning practices goes beyond the chip

The U.S. FDA, Health Canada, and the UK’s MHRA have unveiled 10 guiding principles for Good Machine Learning Practice (GMLP) in developing AI/ML medical devices. These principles aim to ensure safety, efficacy, and quality in healthcare innovation. Key focuses include leveraging multi-disciplinary expertise, implementing good software and security practices, ensuring representative clinical study participants and data sets, maintaining independence between training and test data sets, and emphasizing the performance of the human-AI team. These guidelines also highlight the importance of clear user information, robust testing, and ongoing monitoring of deployed models to manage re-training risks and maintain performance.

Read the full GMLP draft on the FDA website.

1-Minute Summary

Here are the ten principles of GMLP.

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