B

Public Hospital AI Diagnosis Pre-Visit Guide

3.55

Derivation Chain

Step 1 Seoul Medical Center as a leading public healthcare AI hospital
→
Step 2 Lack of patient understanding when public hospitals adopt AI
→
Step 3 Pre-visit guide for AI diagnosis experience + question generation service

Problem

When patients in their 50s and 60s with chronic conditions receive AI-assisted diagnosis at public hospitals like Seoul Medical Center, even if the doctor explains the AI-read test results, anxiety such as 'Can I trust what AI did?' or 'Where does my data go?' remains unresolved. There is no material to understand the AI diagnosis process before the visit, and within the limited consultation time (average 5-7 minutes), they leave without asking all their questions.

Solution

Users select the hospital and department they plan to visit, and the service explains how the AI diagnostic tools used by that hospital work in layman's terms, and automatically generates '5 questions to ask during the visit'. It provides a Korean-language explanation of key clauses in the data usage consent form and a comparison table of what changes if they refuse.

Target: Chronic disease patients (hypertension, diabetes, lung disease) aged 50-65 who regularly visit public hospitals and are experiencing AI-assisted diagnosis for the first time.
Revenue Model: Free web service. 2,000 KRW per download of hospital-specific AI diagnosis detailed report PDF (~$1.50). Can supply patient pre-education solutions to hospitals as B2B.
Ecosystem Role: Education
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
4.0/5
U Urgency
3.0/5
M Market
3.0/5
R Realizability
4.0/5
V Validation
3.0/5
NUMR-V Scoring System
N Novelty1-5How uncommon the service is in market context.
U Urgency1-5How urgently users need this problem solved now.
M Market1-5Market size and growth potential from proxy indicators.
R Realizability1-5Buildability for a small team with realistic constraints.
V Validation1-5Validation signal quality from competition and demand data.
Each dimension is scored 1-5 and weighted into grade S/A/B/C/D.
N=.15 U=.20 M=.15 R=.30 V=.20

Feasibility (72%)

Tech Complexity
32.0/40
Data Availability
20.4/25
MVP Timeline
20.0/20
API Bonus
0.0/15
Feasibility Breakdown
Tech Complexity/ 40Difficulty of core implementation stack.
Data Availability/ 25Practical availability and cost of required data.
MVP Timeline/ 20Expected time to ship a usable MVP.
API Bonus/ 15Bonus for viable public API leverage.
Total 100 points; below 50% is filtered out.

Market Validation (53/100)

Competition
8.0/20
Market Demand
9.4/20
Timing
14.0/20
Revenue Signals
7.5/15
Pick-Axe Fit
7.5/15
Solo Buildability
7.0/10
Validation Breakdown
Competition/ 20Signal quality from competitor landscape.
Market Demand/ 20Demand proxies from search and mention patterns.
Timing/ 20Fit with current shifts in tech, behavior, and regulation.
Revenue Signals/ 15Reference evidence for monetization viability.
Pick-Axe Fit/ 15How well the concept serves participants in a trend.
Solo Buildability/ 10Practicality for lean-team implementation.
Total 100 points; below 50 is filtered out.

Technical Requirements

Frontend [low] Backend [medium]
Dashboard