How AudioScholar is made
AudioScholar turns the week’s peer-reviewed medical literature into short audio briefings — a free ~10-minute podcast for every specialty, and tools for papers you choose yourself. The scripts are written by AI, and we say so plainly, in every episode and on this page. This page explains what stands between the source literature and your ears.
Who runs it
AudioScholar is built and run by Dr. Nikhil Shah, a practicing academic nephrologist in Edmonton, Canada — who is also one of the service’s heaviest listeners. There is no content team and no sponsor: one physician, an engine he supervises, and the checks described below.
Where the papers come from
Every Specialty Weekly starts from PubMed, the U.S. National Library of Medicine’s index of the biomedical literature, filtered through a hand-curated catalogue of over 300 journals selected per specialty. Only papers published in the current window are eligible, and a paper without a usable abstract is excluded outright — a summary written from a title alone would be guesswork, so it is not allowed to happen.
What the AI does — and what checks it
A model selects the week’s most significant papers and writes the narration. What makes the output trustworthy is not the model; it is the checks around it:
Citation verification. Every paper cited in a script is verified against the week’s actual PubMed results. A script whose citations cannot be verified is regenerated — and if it still fails, the episode is not published. We would rather skip a week than misattribute a paper.
Numbers from the source only. Scripts are constrained to the figures present in each paper’s abstract, and a negative or non-significant result must be reported as exactly that. Because every episode publishes its full transcript with linked references, any claim can be checked against its source in one click.
Honest quiet weeks. When a week’s literature is thin, the episode says so and stays short. Nothing is padded to fill ten minutes, and a week with nothing worth your time tells you that in under two.
A disclaimer you will actually hear. Every episode ends by stating that it is an automated summary that can make mistakes, and that the original sources are the reference — in the audio itself, not fine print.
When something is wrong
AI systems make mistakes, and some will reach publication despite the checks. Two commitments. First, reported errors are investigated against the stored record of how that episode was generated. Second, every confirmed defect becomes a new permanent, automated check — each safeguard above exists because a physician caught a real problem in a real episode, and the fix was built so that class of error cannot recur silently. If you hear something wrong, please say so: nikhil.shah@audioscholar.cc.
Listen or read
Every episode has its own page with the full transcript and the reference list, each paper linked to PubMed. If audio is not your medium — or not your colleague’s — the same briefing can simply be read. Papers you bring yourself (a PDF or a link) can be turned into audio in any of 31 languages.
What AudioScholar is not
It is not medical advice, and it is not a systematic review — it is a current-awareness briefing, a fast and honest way to know what was published and why it might matter, before you read the papers that matter to you. It also never touches patient data: the service is for published literature only, and uploading patient-identifiable information is prohibited.
Cost
The weekly specialty shows are free, carry no advertising, and require no account — pick a specialty and follow it in your podcast app.