🧠 Medical Device Use Case

Make Neural Interface AI Explainable

Every neural signal. Every stimulation decision. Every safety check. Observable, queryable, and FDA-auditable. Finally answer the question patients and regulators both ask: "Why did the implant send that signal?"

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FDA 21 CFR Part 11 Compliance

Medical AI devices require explainable decision-making. EventIDs provide the audit trail regulators demand — every neural decode, every stimulation pulse, every safety threshold crossed.

"The device decided to stimulate. We don't know why."

Brain-computer interfaces make thousands of decisions per second. When a patient reports discomfort or an adverse event occurs, "the AI decided" isn't an acceptable answer for the FDA — or for the patient.

FDA Approval Blocked

Regulatory approval requires demonstrating that every decision is traceable, auditable, and explainable. Black-box neural networks won't pass review for implantable devices.

Adverse Event Investigation

When a patient reports discomfort or unusual sensations, clinicians need to reconstruct exactly what the device did in the preceding seconds — and why.

Malpractice Liability

If something goes wrong, the decision chain must be traceable. "We're investigating" doesn't work in court when a device is implanted in someone's brain.

The Event Model Solution

Every neural interface decision becomes a structured, queryable event. The same domain.entity.action:version pattern that works for software telemetry works for medical device telemetry — with FDA-grade auditability.

LIVE Neural Interface Event Stream
Patient N-0847 • Motor Cortex Array
M1 Motor Cortex 1024 channels @ 30kHz Latency: 4.2ms
NORMAL OPERATION Intent decoding active • Cursor control mode
neural.signal.spike_detected:1 14:32:01.003
neural.decode.motor_intent_classified:2 14:32:01.007
neural.decode.cursor_movement_predicted:1 14:32:01.011
implant.safety.parameters_nominal:1 14:32:01.015
neural.signal.spike_detected:1 14:32:01.047
neural.decode.click_intent_detected:1 14:32:01.051
implant.calibration.decoder_confidence_logged:1 14:32:01.089
implant.safety.impedance_check_passed:1 14:32:02.001

Query Anything. FDA-Ready.

Adverse Event Investigation
WHERE patient_id = 'N-0847'
AND timestamp BETWEEN '14:30:00' AND '14:32:00'
AND event_id LIKE 'implant.%'
Every implant decision in the 2 minutes before the patient reported discomfort. Exact sequence, exact timing.
Safety Threshold Analysis
WHERE event_id LIKE 'implant.safety.%'
AND (current_uA > threshold OR impedance_ohms > limit)
ORDER BY timestamp
Find all instances where safety parameters approached or exceeded limits across all patients.
Decoder Performance Audit
SELECT AVG(confidence), patient_id
FROM events
WHERE event_id = 'neural.decode.motor_intent_classified:2'
GROUP BY patient_id
Which patients have lower decoder confidence? Target calibration sessions where they're needed most.
Stimulation Audit Trail
WHERE event_id LIKE 'implant.stimulation.%'
AND patient_id = 'N-0847'
ORDER BY timestamp DESC LIMIT 1000
Complete stimulation history for FDA audit. Every pulse, every parameter, every safety check.

Case Study: Adverse Event Investigation

14:31:58.234
Normal motor intent decoding
neural.decode.motor_intent_classified:2 {confidence: 0.94, direction: "right"}
14:31:59.891
Impedance increase detected on electrode 47
implant.monitoring.impedance_change:1 {electrode: 47, ohms: 1250, threshold: 1000}
14:32:00.123
Safety system reduces stimulation current
implant.safety.current_reduced:1 {electrode: 47, from_uA: 80, to_uA: 40, reason: "impedance_elevated"}
14:32:00.456
Patient reports mild tingling sensation
patient.feedback.sensation_reported:1 {type: "tingling", intensity: 2, location: "left_hand"}

Total time to generate FDA incident report with EventIDs: 12 minutes
Previous method (manual log review): 3-5 days

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FDA Approval Path

Demonstrate explainability from day one. Every decision is logged, queryable, and auditable — exactly what regulators need to see.

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Patient Safety

Real-time safety monitoring with queryable thresholds. Know immediately when parameters approach limits, and prove the device responded correctly.

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Legal Protection

Complete audit trail for every stimulation pulse. In litigation, show exactly what the device did, when, and why — with millisecond precision.

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Clinical Improvement

Query patterns across all patients to improve decoder algorithms. "Which electrode configurations produce highest confidence?" becomes a SQL query.

"The long-term goal is to achieve symbiosis with AI. But first, we need to trust it."

Trust requires transparency. Transparency requires observability.
The Event Model makes every neural interface decision visible, queryable, and accountable.

Make Medical AI Auditable

The same Event Model that makes AI code observable can make any medical device explainable. Neural interfaces. Insulin pumps. Cardiac monitors. If it makes decisions, it should emit events.

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