NeuraLog AI : AI Research Infrastructure for Clinical Trials

Hi Product Hunt! I'm Dr. Rameen Rao, a physician and dementia researcher. I built NeuraLog AI because I kept watching the same thing happen in research labs: someone opens a patient chart, reads a note, finds an NIHSS or MMSE score, types it into a spreadsheet, closes the chart, opens the next one. Fifteen to forty minutes per visit. For a 200-patient study across three timepoints, that's hundreds of hours before anyone gets to do actual science. The obvious answer is "just use AI." I tried. General models will confidently hand you a number that isn't in the note. They'll conflate HAM-D-17 and HAM-D-21, which have different denominators. They'll give BDI-II the wrong severity bands. And there's no way to check - no citation, no audit trail, nothing. In clinical research, a wrong number that enters a published dataset is far worse than a missing one. So I built this around a principle I ended up calling "null over wrong": if the documentation doesn't support a score, the system returns nothing and flags it. Click any extracted value and it highlights the exact sentence it came from. If it can't verify the source, it says so rather than guessing. The part that surprised me most: building refusal is harder than building extraction. I audited my own product and found places where it was inferring scores from narrative descriptions instead of refusing, exactly the failure mode I built it to prevent. Fixing those became the roadmap. I also ran every clinical scale citation against PubMed and found a 16% error rate, including two citations that didn't exist. All corrected, and nothing goes in now without independent verification. Happy to talk about any of it: the clinical logic, the refusal architecture, the de-identification layer, or what it's like building software as a physician. If you do clinical research, what's your current abstraction workflow? I'd genuinely like to know what I'm missing.