Reading a scientific paper in full takes hours. But most of the time the real question is simpler: does this study say what I need, and can I trust it? Answering that takes five minutes, if you know where to look. This is the method I use, with the tools in the lab I built.
Minute 1: find the paper (legally)
Many studies are freely available in open-access archives. With the PDF search I look directly on arXiv (physics, computer science, mathematics and more) and Europe PMC (biomedicine and life sciences), without going through sites of dubious origin. One important note: on arXiv many articles are preprints, meaning they haven't been peer reviewed yet.
Minute 2: read the three right parts
Before any summary, I open the preview and read, in this order:
- The abstract: the study's promise.
- The conclusions: what the authors claim to have shown.
- The main figures and their captions: they often say more than the text.
If after these three parts the study is clearly off-topic, I stop there. I've saved everything else.
Minute 3: the AI summary
If the paper looks relevant, I send it to the summary through the workspace, without downloading it. The mode I pick depends on the question:
| Mode | When to use it |
|---|---|
| Short | Understand in two lines what it's about |
| Bullet points | Get method, results and conclusions separately |
| Insights | Look for practical implications and weak points |
| Detailed | Prepare for a full read |
Minute 4: the questions the summary won't ask by itself
This is the difference between using AI and being used by it. A summary confidently repeats what the study claims, but doesn't judge it. I always check by hand:
- The sample: how many people, how many cases, which conditions? A result on twelve participants isn't worth the same as one on twelve thousand.
- The type of study: observational or experimental? Correlation isn't causation.
- The publication status: preprint or peer-reviewed journal?
- The stated limitations: almost every paper has a section where the authors admit what they haven't shown. It's the most honest part of the document.
- The numbers quoted in the summary: I find them in the original text before reusing them.
Minute 5: decide
At this point I have three options: discard the paper, cite it with the right caveats, or read it in full because it's genuinely central. In all three cases the decision is informed, and it cost me five minutes instead of an afternoon.
The limits to know
- The summary works on the text: charts and tables saved as images aren't interpreted.
- Very long papers are truncated beyond a certain threshold, and the tool tells you: appendices and supplementary material may be left out.
- AI can get a number wrong or attribute the wrong conclusion: that's why minute 4 is never skipped.
- Not everything is open access: if a study is paywalled, there is often a preprint version, or the author will send it on request.
In short
Five minutes are enough to decide whether a paper deserves a full read: find it on an open source, read the abstract, conclusions and figures, ask for a targeted summary and check sample, method, limitations and numbers by hand. AI speeds up the first filter; the judgement stays yours. I wrote more about using summaries well in this guide.
