IntuitionLabs
AI integration for Calyx EDC, IRT, and Medical Imaging clinical trial platforms

Calyx AI Integration — eClinical AI Agents for EDC, IRT & Imaging

Connect Claude, GPT, and Gemini to Calyx via REST API. Validated AI workflows for MedDRA coding, anomaly detection, supply forecasting, SAE narratives, and adjudicator support — all with full audit trail and human-in-the-loop sign-off.

Calyx AI Use Cases We Build

High-value AI workflows that sit on top of Calyx data, fully audit-trailed, and aligned to FDA's January 2025 AI in drug development guidance and ICH E6(R3) GCP.

Medical Coding
AI MedDRA & WHODrug Coding
Auto-suggest MedDRA LLTs and WHODrug codes for adverse events and concomitant medications. 3–5x coder throughput with consistent quality and full audit trail back into Calyx.
Discuss coding AI
Data Quality
Central Monitoring & Anomaly Detection
AI-augmented detection of implausible values, digit-preference patterns, and site-level fraud signals on top of Calyx EDC. Integrated with the TransCelerate RBQM framework and ICH E6(R3) central monitoring expectations.
Discuss monitoring AI
Drug Safety
SAE Narrative Drafting
Auto-draft CIOMS-style SAE narratives from Calyx EDC and concomitant data. Safety physicians edit and approve before E2B(R3) submission to Argus or LifeSphere Safety.
Discuss narrative AI

Why Calyx + AI Now

Frontier reasoning models — Anthropic Claude, OpenAI GPT-class, Google Gemini — are now capable enough to handle the structured-but-noisy data that flows through Calyx EDC, IRT, and Imaging. The FDA January 2025 draft guidance made the regulatory expectations explicit: AI is acceptable in regulated decisions provided context-of-use, validation evidence, and human oversight are documented. Sponsors who deploy AI inside that framework get measurable efficiency gains in coding, monitoring, narratives, and supply — without compromising inspection readiness.

AI integration framework connecting Anthropic Claude and OpenAI to Calyx EDC for medical coding

Built for GxP and 21 CFR Part 11

Every AI invocation against Calyx data is logged with model version, prompt template version, input hash, output, confidence score, human reviewer ID, and final decision — satisfying 21 CFR Part 11 audit trail expectations and EU Annex 11. Prompt templates are version-controlled and validated as configuration artifacts under GAMP 5 Category 4. Periodic review, model drift monitoring, and prompt regression testing complete the lifecycle.

GxP audit trail and 21 CFR Part 11 controls applied to AI workflows over Calyx

Aligned With ICH E6(R3) GCP

The revised ICH E6(R3) Good Clinical Practice guideline finalized in January 2025 explicitly embraces risk-based, technology-enabled trial conduct — including centralized monitoring, fit-for-purpose data systems, and quality by design. Our AI workflows are designed against this framework: they augment the human reviewer rather than replace them, they prioritize risk, and they generate the documented audit trail that GCP inspectors expect to see during BIMO inspections.

ICH E6(R3) GCP risk-based monitoring framework applied to Calyx eClinical AI workflows

What We Deliver in a Calyx AI Engagement

A complete AI integration package — discovery, build, validation, and operations — for sponsors and CROs ready to put AI to work on Calyx eClinical data.

Use Case Discovery

Workshop with clinical operations, data management, and safety teams to identify the highest-value AI workflows for your studies. We benchmark current effort, define success metrics, and align with regulatory expectations.

Start discovery

Integration Engineering

Build the integration layer between frontier AI models (Claude, GPT, Gemini) and Calyx EDC, IRT, and Imaging via REST API. Includes prompt template versioning, output schema validation, and full audit-trail logging.

Integration consult

Validation & Documentation

Execute IQ/OQ/PQ for the AI workflow as a GAMP 5 Cat 4 configured extension. Deliver URS, configuration spec, FMEA risk assessment, traceability matrix, and validation summary report aligned to FDA AI guidance.

Validation services

Human-in-the-Loop UI

Custom review interfaces — for coders, monitors, safety physicians, adjudicators — that surface AI suggestions with confidence scores, comparator context, and one-click accept/reject. Built to fit existing workflows.

UI examples

Model Operations

Continuous monitoring of model accuracy, drift, latency, and cost. Periodic prompt regression as MedDRA, WHODrug, and protocol versions change. Quarterly performance reviews against your defined success metrics.

Managed AI ops

Privacy & Data Residency

PHI handling via BAA-covered model endpoints, zero-data-retention agreements, and regional inference where required (US, EU, UK, JP, China). Documented data flows for DPIA and HIPAA/GDPR/PIPL compliance.

Privacy framework

The IntuitionLabs AI + Calyx Difference

Most consultancies treat AI as a feature pitch. We treat it as a regulated software lifecycle. Our team has shipped validated AI workflows on top of EDC, safety, and CTMS platforms for pharma sponsors — and every workflow is built to clear an FDA BIMO inspection, not just a demo.

eClinical Native

Our consultants have built CRFs, run database lock, and reconciled SAEs — they understand the workflows AI is augmenting.

Validation Discipline

Every AI workflow is a configured GAMP 5 product — versioned prompts, IQ/OQ/PQ, audit trail, periodic review, drift monitoring.

Multi-Model Architecture

We deploy Claude, GPT, and Gemini side-by-side with model-agnostic prompt frameworks — no vendor lock-in.

Calyx AI Architecture

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Calyx REST API Gateway

Authenticated, rate-limited gateway between Calyx and AI models. Handles OAuth, retry logic, idempotency, and field-level redaction so PHI is minimized before any model invocation.

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Versioned Prompt Library

All prompt templates are stored in version control with semantic versioning. A change to a prompt is a controlled change with regression testing — never a silent edit. This is the foundation of GAMP 5 prompt validation.

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Multi-Model Router

Route by use case to Claude for reasoning-heavy tasks, GPT for structured-output tasks, Gemini for long-context tasks. Fallback logic and cost-aware routing keep operations resilient.

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Human-in-the-Loop UI

Custom review screens for coders, monitors, and safety physicians. Every AI suggestion comes with confidence score, rationale, and one-click accept/reject. Decisions write back to Calyx with full provenance.

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Audit Trail Database

Every prompt, response, confidence score, reviewer ID, and final decision is logged in a Part 11-compliant audit store. Inspection-ready exports support FDA, EMA, MHRA, and PMDA requests.

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Model Performance Monitoring

Continuous tracking of accuracy, latency, cost, and drift. Quarterly periodic review against defined success metrics, with documented retraining or prompt-update decisions logged as change control records.

Frequently Asked Questions

Calyx has announced AI initiatives across its product line — including AI-assisted edit-check generation, central monitoring signal detection, and adjudicator support inside the Medical Imaging platform. The Model Context Protocol (MCP) ecosystem is rapidly expanding across clinical platforms, and we expect Calyx to publish an MCP server in the same way Veeva and other vendors have. Until then, IntuitionLabs builds the equivalent capability ourselves by connecting external frontier models — Anthropic Claude, OpenAI GPT, Google Gemini — to Calyx via its REST API with validated prompts and human-in-the-loop review.
In January 2025 the FDA released its draft guidance "Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products", establishing a risk-based credibility assessment framework for AI models used in regulatory submissions. In January 2026 FDA followed with guiding principles on transparency for ML-enabled medical devices. EMA has parallel guidance under its AI workplan. The practical implications for Calyx integrations: AI outputs that influence a regulated decision (medical coding, eligibility, endpoint adjudication) must have model context-of-use documented, validation evidence, and human-in-the-loop sign-off. IntuitionLabs builds Calyx AI workflows aligned to this framework from day one.
AI integrations with Calyx routinely handle protected health information and personal data subject to HIPAA, GDPR, the UK Data Protection Act 2018, and growing regional frameworks (e.g. China PIPL, Japan APPI). We route AI calls through models with appropriate Business Associate Agreements and zero-data-retention agreements (Anthropic, OpenAI, Google Cloud Vertex AI), minimize the data shared with each model invocation, and operate dedicated regional inference endpoints when required. PHI never leaves the sponsor's trust boundary without an executed contract, and audit logs capture every prompt, response, and human decision.
Yes — and this is one of the highest ROI AI workflows on top of Calyx. Medical coders today spend significant time mapping free-text adverse event terms to MedDRA Lowest Level Terms (LLTs) and concomitant medications to WHODrug. Our AI coding agent ingests the verbatim term, the dictionary version, and the protocol-specific coding guidelines, then proposes the top three candidate codes with confidence scores and a rationale. The medical coder reviews and accepts/rejects; the choice is logged with full audit trail back into Calyx. We have seen coding throughput improve 3–5x while improving consistency, and the workflow aligns with the auditability expectations of ICH E2A safety reporting standards.
AI-augmented central monitoring layered on Calyx EDC detects patterns that rules-based edit checks miss — implausible value combinations, digit-preference signals, longitudinal outliers, and site-level fraud indicators. We build statistical and LLM-driven anomaly detection pipelines aligned to the TransCelerate Risk-Based Quality Management framework, integrated with central monitoring dashboards and tied back into Calyx EDC queries for resolution. The framework is consistent with the risk-based approach mandated by ICH E6(R3) GCP and the central monitoring expectations in the FDA risk-based monitoring guidance.
Yes — SAE narrative writing is a structured natural-language task that frontier LLMs handle well when given the structured Calyx EDC SAE dataset, concomitant medications, medical history, and treatment context. Our agent drafts a CIOMS-style narrative that the safety physician edits and approves before the case is submitted to Oracle Argus or ArisGlobal LifeSphere for E2B(R3) reporting. This shaves hours off each SAE while leaving final medical judgment with the human reviewer — exactly the boundary the FDA AI guidance expects.
AI-driven trial supply forecasting consumes Calyx IRT shipment history, depot inventory, enrollment curves, screen-failure rates, and expiry timelines to predict resupply demand. This is more accurate than static models because it adapts to actual enrollment dynamics — slower-than-projected enrollment in EMEA, faster-than-projected enrollment in Asia-Pacific, kit return rates, and protocol amendments that change dosing. The result is reduced overage, fewer stockouts, lower waste, and better cold-chain economics — all under the supply governance expectations of EU GMP Annex 13.
Every AI invocation against Calyx data is logged with the model version, prompt template version, input payload hash, output, confidence score, human reviewer ID, and final decision. This logging satisfies 21 CFR Part 11 audit trail expectations for any AI output that influences a regulated decision. Prompt templates are version-controlled, validated, and treated as configuration artifacts under GAMP 5 Category 4 rules. Periodic review of model performance, drift detection, and prompt regression testing complete the lifecycle.
AI assistance in Calyx Medical Imaging takes two forms: pre-read triage — flagging studies likely to show progression, suspicious findings, or quality issues to prioritize the reader queue — and adjudicator support, where models surface relevant comparator images and prior assessments. We do not use AI to replace blinded independent central review (BICR); the regulatory bar for that level of automation requires FDA Software as a Medical Device (SaMD) clearance for the AI itself. The AI assists the human reader and is fully audit-trailed; the read of record is always human.
A typical engagement runs in three phases. Phase 1 (discovery, 2–4 weeks): identify the highest-value AI use cases for your studies, map them to Calyx APIs and prompt templates, design the human-in-the-loop workflow, and align with regulatory and quality teams. Phase 2 (build & validate, 6–12 weeks): implement the integration layer, validate prompts against historical Calyx data, execute IQ/OQ/PQ for the AI workflow as a configured-product extension, and run user acceptance testing. Phase 3 (operate, ongoing): monitor model performance, run periodic prompt regression, retrain when underlying dictionaries change (e.g. quarterly MedDRA releases), and extend to additional use cases. See our broader AI enablement practice.
Ready to Add AI to Your Calyx Studies?
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Ready to Add AI to Your Calyx Studies?

Book a discovery session to scope validated AI workflows on top of Calyx EDC, IRT, and Imaging — and see how MedDRA coding, central monitoring, and SAE narratives can be 3–5x more efficient without compromising inspection readiness.

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