The Opportunity
Radiology is a $30B+ market running out of radiologists. Every AI tool to date has failed to produce output that's actually clinically usable. VoxelMD changes that.
The Crisis in Radiology
Demand for imaging is exploding while the workforce cannot keep up. This is a systemic, structural problem - and it's getting worse.
The global radiology workforce faces a structural deficit that medical education cannot close fast enough.
An aging workforce accelerates the crisis. The pipeline of new radiologists cannot replace departing physicians at this rate.
High-volume, standardized exams represent the immediate automation opportunity - before expanding to complex modalities.
The Solution
Two complementary product lines that cover the full spectrum of radiology, from standardized exams to complex volumetric imaging.
Production-Ready AI
Purpose-built AI that generates preliminary radiology reports from medical images. Rapid deployment with no costly training cycles. Working prototypes for thyroid ultrasound, CT perfusion, cardiac nuclear medicine (SPECT MPI), and DEXA bone densitometry.
Expanding Coverage
Advanced model training on radiologist-verified data for modalities requiring volumetric spatial awareness. Purpose-built for complex imaging via Google Cloud and 3D medical image processing.
Market Opportunity
Sources: Grand View Research, MarketsandMarkets, Precedence Research (2025-2026 reports)
Revenue Model
Two revenue tracks with compounding unit economics.
B2B SaaS Licensing
Per-radiologist subscription model for private practices and hospital departments. Tiered pricing based on volume and modality coverage.
Scales with modality coverage and practice size
CPT Code Billing
Each AI-generated interpretation maps to a billable CPT code, transforming AI from practice overhead into an independent revenue center. Near-zero marginal cost per study.
Varies by modality and CPT category (requires FDA clearance)
Competitive Advantage
Four structural differentiators that compound over time.
Multi-Modality from Day One
Architecture designed for any imaging modality, not locked to a single exam type. Thyroid ultrasound, CT perfusion, and spine MRI all share the same clinical AI framework.
Zero-PHI Edge Platform
De-identification happens on the customer's own infrastructure - PHI and the re-identification crosswalk never leave their network. This collapses the security-review cycle that kills most health-AI sales, and results are pre-computed so radiologists never wait on an API call.
Physician-Founded
Built by a practicing Neuroradiologist who reads the same studies the AI targets. No translation layer between clinical need and technical execution.
Self-Improving Moat
A proprietary learning architecture that compounds accuracy over time. The longer a practice uses VoxelMD, the more tailored and accurate it becomes — creating a moat competitors cannot replicate.
Traction
Self-funded to date. Building with conviction.
Working prototypes for thyroid ultrasound, CT perfusion, cardiac SPECT MPI, and DEXA bone densitometry
Lumbar and cervical spine MRI in active research
Member of NVIDIA Inception Program
Google for Startups Cloud Program member
AWS credits active - GPU compute and storage
Google Cloud AI infrastructure for production deployment
NSF SBIR application submitted
Mo Fakhri, MD
Practicing Neuroradiologist and sole founder. Trained at Harvard Medical School (research fellow), UCSF (neuroradiology fellowship), and Mallinckrodt Institute of Radiology (residency). NIH T32 and RSNA Research Grant recipient. Writes every line of code.
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We are seeking pre-seed investment to accelerate product development, expand our pipeline, and prepare for FDA pre-submission.
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