Method
Every entry here reached a human, was measured against something, and names the date it happened — not the date it was published.
Readers of this site have been burned by AI hype. So the registry earns credibility by what it leaves out, by publishing what did not work, and by dating every benefit by when it happened rather than when the paper came out.
The four tests
An entry starts "Because of AI, …" only if all four hold:
- It reached people. A benefit arrived for actual humans, not a benchmark score or a demo.
- It was measured against something. A control arm, a prior period, a human benchmark, another model. The comparison is named on every entry.
- The AI did something that would not otherwise have happened. The attribution grade says how cleanly we can tell.
- We know when the benefit occurred. Not when the result was published.
A result that passes the first three but produced no benefit goes to Not because of AI, under the same grades. A candidate that has not yet passed them goes to Not yet, with a note on what would change that.
Three grades, never one score
Every entry carries three separate stamps. They are a vocabulary, not a formula. They are never added up, and no entry has a single score. The same sentences below appear when you hover or focus a stamp anywhere on the site.
Evidence
- A · randomized. Randomized or cluster-randomized, with a preregistered primary endpoint. Example: Because of AI-assisted decision support, 10 fewer stroke patients per 1,000 had another stroke, heart attack, or vascular death within three months.
- B · observational. Large observational or population-level data with a credible benchmark. Example: Because of AI, the Hurricane Center called a Category 5 landfall in Jamaica about three days out, while Melissa was still a Category 1.
- C · operational report. A company or government operational report with no independent comparison. Example: Because of AI, there were an estimated 841 fewer injury-causing crashes across 270 million driverless miles.
- D · single case. A single case or anecdote. Example: Because of AI, a baby girl was born to a couple who had been told there was no sperm to work with.
Attribution
- isolated. The comparison isolates the AI's contribution: the arms differ only by the AI. Example: Because of AI, the Hurricane Center called a Category 5 landfall in Jamaica about three days out, while Melissa was still a Category 1.
- only path. No human or conventional alternative existed in practice. Example: Because of AI, a baby girl was born to a couple who had been told there was no sperm to work with.
- bundled. The AI shipped inside a package (tablets, protocols, crews) and the package was tested. Example: Because of AI-assisted decision support, 10 fewer stroke patients per 1,000 had another stroke, heart attack, or vascular death within three months.
- AI vs AI. The control also used AI. Example: Because of AI, 30 hearing-aid users understood far more words in heavy noise — according to the company that makes the device.
The cleanest counterfactual is where no human alternative existed: tuberculosis X-rays with no radiologist to read them, sperm no technician could find. Bundled and AI-versus-AI results are real results that say less about AI than they appear to. We label them rather than hide them.
Window
- in window. The benefit occurred inside the last twelve months. Example: Because of AI, the Hurricane Center called a Category 5 landfall in Jamaica about three days out, while Melissa was still a Category 1.
- ongoing. It began earlier, is still delivering, and has new in-window data. Example: Because of AI, there were an estimated 841 fewer injury-causing crashes across 270 million driverless miles.
- pre-window. The benefit predates the window. Only the publication is new. Example: Because of AI, TB case notifications rose 80% in a tribal district of Chhattisgarh, India, including 20 confirmed cases in people with no symptoms.
The window is the twelve months ending on the registry's reference date, currently 2026-10-22. Window grades are editorial and change only through a reviewed edit, so an entry can sit at the edge of the window for a short while before it is re-graded. Pre-window entries stay on the main list with a hatched stamp: the result is real, it is just not new.
Achieved is not published
This is the registry's most common mistake, and the one we work hardest to avoid. Every row carries a lag bar: the span the benefit was achieved as a solid bar, the publication as an orange tick, and the gap between them hatched and labelled when it is six months or more. The dates themselves are on the entry page.
The worked example is GOLDEN BRIDGE II, a cluster-randomized trial across 77 Chinese hospitals published in the BMJ in March 2026. The trial is superb. Its patients were enrolled between January 2021 and June 2023. So the benefit happened years before the paper, and the entry is graded pre-window, not in-window. We check methods sections and trial registries before trusting a headline date.
How entries get here, and what is and is not automated
An automated research run scans the literature every morning and a deeper one runs weekly. They read primary sources, apply the four tests, and may draft an entry; a drafted entry is published the same day once the data checks pass, and carries an "unreviewed" note in its history until the editor has read the source and confirmed or changed the grades. What is never automated: the grade vocabulary itself, the decision to keep or remove an entry after review, and the corrections readers send in.
The cautionary case is an automated index that collected thousands of entries in a couple of months by having a language model read and score papers. Unrelated studies in it carry identical sub-scores, line for line, because the scorer produces the same shape of answer whatever it is shown. An index like that looks dense and reads as evidence. It is neither.
Our rules, for the run and for the editor alike: a primary source for anything we put a number on, an absolute effect next to every relative one, company dashboards admitted only with the conflict stated and the comparison understood, press releases treated as leads rather than evidence, and no entry without a date the benefit actually occurred.
Submit or dispute an entry
Both go through the public repository. Open an issue to propose a new entry (bring the primary source and the achieved date) or to dispute an existing one (name the entry and the claim you disagree with). Every change to an entry is recorded in its history and in the changelog.