AI-Based Cardiac Monitoring
Identifying algorithm pipelines and security architectures for an FDA- and CE-cleared cloud ECG analytics service that is scalable, secure, and equitable.
Client
Diagnostics-as-a-Service Startup
Objective
Deliver Secure & Equitable AI Analytics
Timeline
10-Week Program
Key Focus
Bias Mitigation & Liability
The Challenge: Three Hurdles to Trustworthy AI in Cardiology
Cloud-hosted AI promises faster, cheaper arrhythmia detection. But its adoption is constrained by critical issues of algorithmic bias, legal liability, and data security.
Training-Data Bias
Public ECG corpora skew toward middle-aged white males, leading to algorithms that "under-see" arrhythmias in women and minority patients, jeopardizing equity and regulatory clearance.
Liability for False Negatives
A missed critical event can have fatal consequences. Responsibility between the AI vendor, physician, and device maker remains a murky legal area in many jurisdictions.
End-to-End Security & Privacy
Streaming raw ECG data to the cloud invokes strict HIPAA and GDPR rules. Encryption, zero-trust access, and full audit trails are mandatory but add cost and latency.
Key Outcome: A Bias-Audited, Secure, & Performant Solution
Our analysis identified a top-performing solution that met every target for clinical performance, equity, security, and cost, providing a clear path to market.
Top-Ranked Solution: Transformer-CNN Hybrid + Re-weighted Focal Loss
96.8%
AFib Sensitivity
98.5%
AFib Specificity
3.1%
Bias Gap Across Demographics
210 ms
Latency via Edge-GPU
Strategic Impact: A Clear Three-Pillar Go-to-Market Plan
The findings provided the client with a comprehensive framework for a successful, compliant launch of the first bias-audited, GDPR-secure AI ECG cloud service.
Pilot Plan
A 300-patient prospective study across the UK & Germany, with edge-GPU nodes hosted in NHS-approved data centres.
Regulatory Path
A clear strategy for an FDA 510(k) de novo and CE MDR Annex IX dossier, with identified predicate devices.
Liability Framework
An ISO 14971 risk file complemented by a £2M per-claim cyber-malpractice rider shared between the vendor and hospital.