GoodMind: AI-Powered Early Detection of Brain Disorders
Transforming neurological diagnostics through advanced AI decision support
The Diagnostic Crisis in Neurology
A fundamental paradox exists in modern neurology: while therapeutic innovation has delivered advanced treatments, the diagnostic pathway remains archaic, fragmented, and unacceptably slow. This disconnect significantly reduces treatment value due to delays in initiation.
$85.1B
Stroke Market by 2034
Global stroke management market projected growth from $41.7B in 2024
$21.7B
Alzheimer's Market by 2030
Therapeutics and diagnostics market forecast
$4.94B
Brain Tumor Market by 2030
Diagnosis and therapeutics market growth
The current patient journey spans months with significant diagnostic delays. Patients are passed between specialists, undergoing disconnected tests that create a data-rich but insight-poor environment where critical patterns can be overlooked.
The Devastating Consequences of Delay
Time-Critical Conditions
In stroke, every minute of delay leads to the death of millions of neurons. The mantra "time is brain" is a clinical reality with devastating consequences.
For progressive diseases like Alzheimer's, the window for effective intervention occurs long before severe symptoms manifest. By the time diagnosis is certain, significant and irreparable cognitive decline has already occurred.
Economic Burden
Diagnostic delays impose a staggering economic burden on healthcare systems through:
Increased long-term care costs
Higher hospitalization rates
Lost productivity
Preventable neurological progression
The existing diagnostic infrastructure is fundamentally ill-equipped to handle the impending wave of aging patients.
The GoodMind Solution
GoodMind introduces a secure, cloud-based diagnostic intelligence engine that empowers clinicians with enhanced insights. Our platform unifies fragmented patient data and transforms it into a coherent, actionable clinical picture.
V1: Neuroimaging-First
CT/MRI decision support for acute stroke and dementia detection with rapid turnaround in minutes
V2+: Multimodal Fusion
Planned integration of EHR, biomarkers, genomics, and cognitive signals as partnerships mature
Seamless Integration
Designed to integrate with existing EMR and PACS systems within clinical workflows
Clinical Workflow Integration
GoodMind integrates seamlessly into existing acute stroke workflows, supporting clinicians at critical decision points without disrupting established protocols.
Scan Acquisition
Patient presents with suspected stroke; emergency CT or MRI scan performed following standard protocols
Platform generates triage flag, confidence score, and visual attention maps highlighting regions of interest
Clinician Review
Qualified professional reviews scan alongside GoodMind outputs; clinician makes final diagnostic determination
Escalation/Routing
Case routed appropriately based on clinical judgment: immediate consultation, standard workflow, or additional assessment
Key Principle: Clinician-in-the-Loop — GoodMind outputs are always advisory. The system supports and accelerates clinical decision-making, never replaces it. All diagnostic and treatment decisions remain under the control of qualified healthcare professionals.
Multimodal AI Synthesis: Our Core Innovation
The fundamental flaw in current diagnostics is its reliance on siloed, single-modality analysis. GoodMind shatters these data silos with Multimodal AI Synthesis, creating a comprehensive biological narrative.
01
Current Focus (V1): Neuroimaging Data
Non-contrast CT and structural MRI analyzed for subtle imaging patterns in tissue intensity and texture. This is our core initial product offering, delivering proven value from neuroimaging alone.
02
Planned V2+: Advanced Imaging
Roadmap includes incorporating fMRI and PET to further refine diagnostic signals, once partner data rights and governance are established.
03
Planned V2+: Genomic & Biomarker Data
Integration of genetic risk scores, specific gene variants (e.g., APOE4 for Alzheimer's), and fluid biomarker levels, contingent on partner data access and ethical approvals.
04
Planned V2+: Cognitive & Functional Data
Incorporation of digital cognitive assessments and passive data from smart devices to capture subtle declines long before they become clinically obvious.
Enhanced Cost-Effectiveness — Improved bottom line while elevating standard of care
Enhancing Clinical Outcomes
Improved Diagnostic Accuracy — Designed to reduce diagnostic errors and prevent adverse events
Less Invasive Procedures — Clearer diagnostic picture reduces need for exploratory procedures
Mitigation of Liability — More accurate, data-driven, auditable diagnostic process reduces malpractice risk
Technical Architecture: Built for Scale
GoodMind is built on Google Cloud Platform (GCP), providing the three pillars essential for mission-critical healthcare solutions.
Scalability
GCP's elastic infrastructure dynamically scales computational resources. Whether processing ten scans or ten thousand, our platform maintains optimal performance without bottlenecks.
Reliability
Leveraging Google's global network and redundant systems guarantees high availability and uninterrupted service for clinical tools practitioners depend on.
Security & Compliance
Healthcare-grade security controls with encryption, role-based access, comprehensive audit logs. Supports HIPAA, GDPR, PDPA compliance. Working toward ISO 13485 and ISO 27001 certification.
1
V1: Secure Upload
Secure upload application for fastest path to pilot
2
V2: PACS Routing
PACS routing integration for automated DICOM ingestion
3
V3: Workflow Integration
Deeper workflow integration including viewer integration and structured reporting
Proven Model Performance
GoodMind's neuroimaging-first platform has produced multiple validated model families for dementia and stroke imaging tasks, demonstrating strong within-dataset performance and critical cross-dataset generalization.
99.97%
MRI Dementia Detection
Within-dataset validation accuracy on OASIS-derived dataset
~98%
Multi-Dataset Average
Average accuracy across dataset holdout scenarios for dementia classification
98.19%
CT Stroke Classification
Accuracy on combined CT stroke dataset (normal, bleeding, ischemia)
Important: These results represent R&D baselines on public datasets and are not clinical claims. Patient-level grouping and prospective validation are required before clinical deployment. Clinical validation through prospective studies with independent expert review is underway.
Competitive Advantage: An Unassailable Moat
Technical Moat
Paradigm Shift to Multimodal Analysis
While competitors focus on single-modality diagnostics, GoodMind's platform is designed to evolve toward models trained on complementary signals including cognitive performance, imaging, and clinical records. This holistic approach enables significantly earlier and more precise diagnoses.
Data Moat
Proprietary Data Flywheel
Actively building high-dimensional datasets by partnering with healthcare providers in emerging markets. More diverse data leads to more robust models, which attract more partners, creating a self-perpetuating network effect competitors cannot replicate.
Visionary Moat
Foundational Model for Brain Health
Building the world's first foundational model for brain health—a large-scale, pre-trained model serving as the core intelligence layer for a vast ecosystem of neurological and psychiatric applications, from screening to treatment planning to drug discovery.
Go-To-Market Strategy & Business Model
Beachhead: GoodMind Stroke (V1+)
Cloud-first product supporting emergency and stroke workflows using non-contrast CT:
Stroke triage and segmentation
Ischemic stroke and hemorrhage detection
Target turnaround: <2 minutes from upload to results
Target Markets
Initial focus on emerging markets (GCC/Middle East, Pakistan, Indonesia, Philippines) with:
Strong demand for improved stroke pathways
Limited specialist availability
Faster procurement cycles
Flexible Pricing Models
SaaS (per site/network): Annual subscription by sites + volume tiers + modules. Predictable budgets and SLAs for enterprise buyers.
Per-scan (usage-based): $ per processed scan with volume discounts. Low friction adoption for imaging networks.
Hybrid: SaaS base + per-scan overage for mixed networks.