Strategic Partnership Proposal

Bringing Universal Medical AI to Canada at Scale

M31.Bio × IBM Canada — deploying foundation models for medical image analysis across the Canadian health system.

M31.Bio
IBM Canada
April 2026
97.3%
Average Dice Score across modalities
5.2×
Faster than manual segmentation
1.57M
Annotated training image-mask pairs
85%
Reduction in annotation time (MedSAM2)
01 / Market Need

Medical imaging is at a breaking point

Demand is growing; the workforce is not.

Radiology shortage
Canada faces a critical shortage of radiologists. Wait times for diagnostic imaging stretch weeks to months in many provinces, delaying diagnosis and treatment.
Manual segmentation bottleneck
Organ and tumor segmentation — required for treatment planning, surgery prep, and cancer monitoring — is done by hand. Each CT scan can take 30–90 minutes per specialist.
Inconsistency at scale
Inter-rater variability in radiological reads is well-documented. AI standardizes analysis quality across institutions, geographies, and case volumes.
$20B
by 2030
Medical Imaging AI Market
30%
of imaging scans
Diagnostic discrepancies

"75% of the world's population lacks access to a medical imaging expert. AI is not optional — it's how we close the gap."

Ontario Health System Aging Population Provincial Budget Pressure
02 / Technology

One platform. Every modality.

Foundation models trained on unprecedented scale — not fine-tuned niche tools.

Core
MedSAM & MedSAM2
Universal segmentation foundation model. 1.57M image-mask pairs, 10 imaging modalities, 30+ cancer types. MedSAM2 extends to 3D volumes and video — reduces annotation time by 85%.
CT MRI Pathology Ultrasound X-Ray
Agent
MedRAX
Agentic AI system for chest X-ray reasoning. Integrates specialized CXR tools with multimodal LLMs in a unified reasoning loop. Benchmark adopted by xAI (Grok 4). 500K+ social views on demo launch.
Reasoning Report Generation Diagnosis
Research
MorphoDiff
Generative AI for cellular morphology prediction under chemical and genetic perturbations. Enables drug discovery pipelines to predict cell phenotype changes without running experiments.
Drug Discovery Cell Biology ICLR 2025 Spotlight
Architecture: Transformer-based encoder + task-adaptive decoder — one shared backbone, all tasks
Inference: Runs on consumer hardware (laptop) — no hospital GPU cluster required
Integration: REST API + DICOM-compatible pipeline
03 / Proof Points

Peer-reviewed, clinically validated, widely cited

Not a prototype — a published platform with real-world deployment.

MedSAM — Nature Communications 2024
3,300+ citations
Foundation model for universal medical image segmentation. 86 internal + 60 external validation tasks. Top 50 papers in AI/ML in Nature Communications.
Cell Segmentation Benchmark — Nature Methods 2024
1,500+ labeled images, 50+ biological experiments. Sets the universal evaluation standard for cell analysis across the field.
MedRAX — ICML 2025
27% accept rate
Chest X-ray reasoning agent. Benchmark used by xAI team in Grok 4 development. Demonstrates real-world AI applicability.
MorphoDiff — ICLR 2025 Spotlight
Top 3.26%
Cell morphology prediction. Accepted as Spotlight — top 3.26% of submissions worldwide.
Clinical Partners
  • Sunnybrook Health Sciences Centre — Toronto, ON
  • SickKids Hospital — Toronto, ON (pediatric imaging)
  • UTHealth Houston — oncology imaging workflows
  • PocketHealth — patient-facing imaging platform
Leadership
  • Jun Ma, PhD — Lead researcher, MICCAI 2024 Young Scientist Award
  • Bo Wang, PhD — Co-PI, co-creator of scGPT (Nature Methods 2024), Xaira Therapeutics; Vector Institute
MedSAM2 User Study (n=5,000 CT scans)
85%
reduction in annotation time vs. manual baseline
04 / The Case for Partnership

Better together — M31's models, IBM's reach

M31 has the AI. IBM has the infrastructure, trust, and hospital relationships. Neither wins the Canadian market alone.

What M31.Bio brings
  • State-of-the-art foundation models — Nature/ICML/ICLR validated, clinically tested
  • Universal coverage — CT, MRI, pathology, ultrasound, X-ray in one platform
  • Proven efficiency gains — 85% faster annotation, 97.3% Dice accuracy
  • Existing Canadian clinical relationships — Sunnybrook, SickKids
  • Deep research pipeline — agentic AI (MedRAX), perturbation biology (MorphoDiff), report generation
What IBM Canada brings
  • watsonx platform — enterprise-grade AI deployment, compliance, and observability
  • IBM Research Canada — joint R&D capacity, Montreal AI ecosystem proximity to Mila & Vector
  • Health system relationships — provincial and federal health authority contracts across Canada
  • Regulatory and compliance trust — ISO, HIPAA, PIPEDA — the enterprise floor that hospitals require
  • Hybrid cloud infrastructure — data residency, sovereignty guarantees for Canadian health data
The combined offer:
M31's foundation models deployed on IBM watsonx → packaged for enterprise procurement → distributed through IBM's Canadian hospital relationships → compliant with Canadian health data sovereignty requirements.
05 / Partnership Opportunities

Five ways to collaborate

Structured for quick starts and long-term expansion.

01
Model Deployment on watsonx Health
Integrate MedSAM2 and MedRAX as first-class models on IBM's watsonx Health platform. IBM distributes to enterprise health system customers; M31 provides model updates and clinical validation.
Near-termRevenue
02
Joint R&D — IBM Research Canada
Co-develop next-generation agentic imaging AI with IBM Research Canada. Focus areas: real-time surgical guidance, federated learning across provincial health networks, 3D report generation.
12–24 monthsIP Sharing
03
Go-to-Market: Canadian Health Systems
IBM's procurement relationships with Ontario Health, Alberta Health Services, BC Health Authority unlock M31's models for provincial-scale deployment. Co-branded clinical implementation program.
Near-termScale
04
Federal Health AI Initiatives
Jointly pursue CIHR, NSERC, and federal digital health grant opportunities. Pan-Canadian imaging AI network — standardized segmentation across research hospitals, enabling national datasets.
12 monthsGovernment
05
Health Data Partnership — Expanding Training Sets
IBM's health data relationships (de-identified, consent-governed) expand M31's training corpus. Larger, more diverse Canadian datasets improve model performance on Canadian population genetics, disease prevalence, and imaging protocols — creating a defensible, Canada-first moat.
StrategicDataLong-term differentiation
06 / Next Steps

From conversation to collaboration

A proposed 90-day path to a working partnership.

Now
Technical introduction
M31 shares API access to MedSAM2 + MedRAX. IBM technical team evaluates integration with watsonx. Aligned on data privacy / sovereignty requirements.
30 days
Partnership framework
Draft MOU covering model licensing, revenue share, IP ownership, and co-marketing rights. Identify 1–2 Canadian health system pilots.
60 days
Pilot launch
Deploy M31 models at a partner hospital via IBM infrastructure. Measure annotation time reduction, clinician satisfaction, and diagnostic accuracy.
90 days
Announce & expand
Joint press release + case study. Expand to additional provinces. Submit joint federal funding application.
What success looks like
  • MedSAM2 deployed at 3+ Canadian health systems within 12 months
  • IBM watsonx Health lists M31 as a certified AI partner
  • Joint CIHR/NSERC application submitted by Q3 2026
  • Co-published real-world outcomes data by end of 2026
M31.Bio
general@m31.bio
Research Lead
Jun Ma / Bo Wang
LinkedIn
m31-ai
Website
m31.bio
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