Diagnosica

TrustMRR

Diagnosica is an AI clinical simulator for medical students and junior doctors. Learners take a history from a video AI patient (voice, video and text), order labs, view real radiology imaging, work through diagnosis and management, then defend their reasoning to an AI attending in a narrative debrief. No hardware — just a phone or laptop. Each five-step encounter ends in a competency scorecard and suggested next cases.

Visit product View on TrustMRRAug 13, 2023Health & Fitness Software stripe

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Actual performance

Did not reach leaderboard

Traction signals

Domain Rating1

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: video · Missing: mobile apps, ios, personal

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

40%40% predicted probability of success on BetaList, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

28%28% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Incorrect prediction on native model

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