Clinical case reports hold decades of medical experience, but they're locked behind keyword search. If you don't use the exact right term, you miss the case that matters. Asclevor changes that. Describe a patient in plain language -presentation, history, findings - and Asclevor returns the nearest cases on record, ranked by semantic similarity rather than exact keyword matches.
Hey Product Hunt š
I built Asclevor after watching how much clinical knowledge is effectively invisible. Case reports exist for almost every presentation you can imagine ā but finding the one that matches your patient means guessing the exact words the authors used.
Asclevor flips that: you describe the case in plain language, and it surfaces the nearest matches ranked by similarity. No keywords, no filters, no query-building.
What's different from generic AI search:
⢠Results are actual case records, ranked by case-to-case similarity ā not an LLM summary that might hallucinate
⢠You see the reasoning: your request side-by-side with each match
⢠Fast, focused, and purpose-built for clinical text
It's early, and I'd genuinely love your feedback ā especially on the ranking quality and what you'd want in a v2 (filters by specialty? full-text view? export?).
Ask me anything about how it works under the hood. Thanks for stopping by! š