Clear-eyed research on AI in healthcare

Practical, well-researched insights for clinicians, hospital leaders, and health IT teams across the US and Canada. No hype. Just evidence, real cases, and honest tradeoffs.

What is AI in healthcare?

AI in healthcare refers to software that assists clinicians, hospital administrators, and care teams by finding patterns in data faster than a person can. In practice this covers a wide range of tools: artificial intelligence in healthcare shows up as diagnostic imaging software that flags a suspicious scan, ambient scribes that draft clinical notes during a patient visit, scheduling systems that reduce no-shows, and revenue cycle tools that catch billing errors before they become denied claims. Some of it works well and is already saving clinicians real time. Some of it is still unproven, or comes with tradeoffs vendors don't advertise.

The honest answer to how much this technology helps depends entirely on which tool, which task, and which hospital. A triage model that works well at an academic medical center with a large, representative training dataset may perform very differently at a rural clinic with a smaller, less diverse patient population. That's the gap this site is built to cover: not vendor pitches, but what independent evidence, FDA filings, and named documented risks actually show.

We organize our research into three areas: Foundations covers the core concepts, benefits, and documented risks; Operations covers the workflow tools hospitals are actually adopting, like scribes and scheduling; and Events tracks the conferences where this field moves fastest.

Latest research

Foundations

7 Real Risks of AI in Healthcare (2026): Bias, Errors & Fixes

From algorithmic bias to the 192.7-million-record Change Healthcare breach — a grounded look at what can go wrong with clinical AI, backed by named, dated, verifiable cases.

12 min read · Updated July 2026

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