B

Medical AI Pre-Sales Kit

2.85

Derivation Chain

Step 1 47.7% of doctors using medical AI
→
Step 2 Hospital medical AI adoption decision support demand
→
Step 3 Medical AI vendor hospital pre-sales automation tool

Problem

Domestic medical AI startups (20-50 companies) must individually research each hospital's PACS/EMR environment, national health insurance reimbursement status, and regulatory approval status during hospital sales. A single sales rep spends an average of 2-3 days creating a proposal for one hospital, and without customized ROI simulations, it's difficult to persuade decision-makers (radiology department heads, hospital directors). As a result, sales cycles extend to 6-12 months.

Solution

Provides a proposal builder that automatically simulates medical AI adoption ROI by hospital type (general hospital/specialty hospital/clinic) and department. Integrates with the national health insurance reimbursement database to instantly calculate insured/non-insured revenue models, and auto-matches PACS compatibility checklists and regulatory approval status to generate customized proposals within 30 minutes.

Target: Sales/business development team members at medical AI startups (10-50 employees)
Revenue Model: SaaS monthly subscription at $142/month (~190,000 KRW) per account, including 50 proposal generations per month. Overage at $2.25 (~3,000 KRW) per additional proposal.
Ecosystem Role: Supplier
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
3.0/5
U Urgency
3.0/5
M Market
2.0/5
R Realizability
3.0/5
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.
SaaS N=.15 U=.20 M=.15 R=.30 V=.20 Senior N=.25 U=.25 M=.05 R=.30 V=.15

Feasibility (70%)

Tech Complexity
29.3/40
Data Availability
20.8/25
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 (54/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
14.0/20
Revenue Signals
9.0/15
Pick-Axe Fit
12.0/15
Solo Buildability
5.0/10
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

Backend [medium] Frontend [medium] Data Pipeline [low]
Dashboard