Fine Tuned Private Model for Clinical Visit Summary Processing
Fine Tuned Private Model
AI-powered assistant automating standardized, compliant clinical trial visit summaries, reducing documentation time and compliance risk.
Clinical Trial Assistant - Research Support | GenAI Protos
AI-powered Clinical Trial Assistant accelerates study design, data synthesis, recruitment optimization and regulatory insights to streamline clinical research workflows.
Fine Tuned Private Model for Clinical Visit Summary Processing
Our Solution
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Executive Summary
The Clinical Trial Assistant is an AI-powered documentation solution designed to automate and standardize clinical visit summaries. Built on a fine-tuned Small Language Model (SLM), it converts raw, unstructured clinical notes into protocol-compliant, structured 8-section visit summaries in under a minute. By significantly reducing documentation time, improving consistency, and minimizing compliance risk, the solution enables Clinical Research Coordinators (CRCs) to focus more on patient care while ensuring high-quality, regulatory-ready trial documentation.
Challenges
CRCs spend 8–15 minutes per visit manually writing summaries, reducing time available for patient interaction and core research activities.
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Time-Intensive Manual Entry
Variations in terminology, structure, and completeness across coordinators and sites lead to downstream reconciliation and data-cleaning efforts.
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Inconsistent Data Quality
Incomplete or inconsistent documentation increases the risk of protocol deviations, audit findings, and regulatory non-compliance.
ShieldAlert
Compliance & Protocol Risk
Solution Overview
The Clinical Trial Assistant introduces Smarter Clinical Summaries, powered by a fine-tuned Small Language Model: Automatically transforms unstructured visit notes into standardized, protocol-aligned 8-section summaries Supports multiple therapeutic areas including Urology, Oncology, Cardiovascular, CNS, and Metabolic/GLP-1 Adapts to diverse study designs, visit types, and protocol requirements Deployable securely on-premise or in the cloud, including local inference on NVIDIA DGX Spark, ensuring full data privacy and regulatory compliance
How it Works
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Functional Workflow
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Pre-Processing & Data Extraction
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Identifies participant ID, phase, therapeutic area, visit type
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Extracts symptoms, safety notes, compliance data
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Captures labs, procedures, deviations, and next-visit details
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Fine-Tuned Visit Summaries
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Generates consistent, protocol-aligned summaries
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Outputs structured JSON ready for EDC, CTMS, and RTSM systems
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Template & Consistency Enforcement
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Ensures all 8 sections are always present
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Prevents hallucinated or inferred content
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Enforces standardized terminology and formatting
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Privacy-Safe, On-Device Deployment
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Fully on-prem inference using DGX Spark (128GB unified memory)
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Zero data egress with support for HIPAA and 21 CFR Part 11
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Optional cloud deployment on GCP or Azure
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Business Impact
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70% of visit summaries required no CRC edits
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Remaining summaries needed only minor formatting or terminology adjustments
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Consistent documentation across sites, coordinators, and studies
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Seamless integration into mock EDC and RTSM workflows
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Key Benefits
Raw, unstructured clinical visit notes are securely ingested from CRC workflows.
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Input Ingestion
A custom pipeline normalizes text and extracts all critical clinical and operational data.
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Pre-Processing & Extraction
The fine-tuned SLM generates a complete, protocol-aligned 8-section visit summary.
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SLM-Driven Summary Generation
SOP-aware constraints validate accuracy, completeness, and JSON structure.
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Guardrails & Validation
Final summaries are produced as human-readable reports and structured JSON for direct system integration.
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Structured Output
key Outcomes with Fine Tune Private Model For Clinical Visit Summary
Documentation Time
Under 1 minute per visit - 90% Reduction
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Accuracy Rate
70% of summaries are 100% accurate - High Consistency
TableProperties
Data Readiness
Structured JSON for direct integration - Clean Integration
Technical Foundation
Google Gemma 3 (fine-tuned Small Language Model)
Brain
Model
1,000+ curated clinical visit–summary pairs
Training Data
NVIDIA DGX Spark (on-prem) or GCP / Azure
Runtime
Custom pre-processor and normalization pipeline
Puzzle
Framework
SOP-aware validation layer enforcing accuracy, structure, and compliance
ShieldCheck
Guardrails
From Manual Documentation to AI-Driven Clinical Precision
The Clinical Trial Assistant demonstrates how specialized, fine-tuned AI models can deliver real value in regulated clinical environments. By reducing documentation time by nearly 90%, improving first-draft accuracy, and ensuring standardized, compliant outputs, the solution addresses one of the most persistent operational bottlenecks in clinical research. Its secure, on-premise deployment model and strong guardrails make it a practical, scalable foundation for modernizing clinical trial operations without compromising privacy, precision, or regulatory trust.
Launch Your Clinical Documentation POC
Evaluate how AI-generated, protocol-compliant visit summaries can streamline your clinical workflows. Run a secure, on-prem or cloud-based POC to measure documentation time savings, first-draft accuracy, and integration readiness with your EDC and CTMS systems.
Book a Demo
https://calendly.com/contact-genaiprotos/3xde
Fine Tuned Private Model for Clinical Visit Summary Processing
Executive Summary
The Clinical Trial Assistant is an AI-powered documentation solution designed to automate and standardize clinical visit summaries. Built on a fine-tuned Small Language Model (SLM), it converts raw, unstructured clinical notes into protocol-compliant, structured 8-section visit summaries in under a minute. By significantly reducing documentation time, improving consistency, and minimizing compliance risk, the solution enables Clinical Research Coordinators (CRCs) to focus more on patient care while ensuring high-quality, regulatory-ready trial documentation.
Challenges
Time-Intensive Manual Entry
CRCs spend 8–15 minutes per visit manually writing summaries, reducing time available for patient interaction and core research activities.
Inconsistent Data Quality
Variations in terminology, structure, and completeness across coordinators and sites lead to downstream reconciliation and data-cleaning efforts.
Compliance & Protocol Risk
Incomplete or inconsistent documentation increases the risk of protocol deviations, audit findings, and regulatory non-compliance.
Solution Overview
The Clinical Trial Assistant introduces Smarter Clinical Summaries, powered by a fine-tuned Small Language Model: Automatically transforms unstructured visit notes into standardized, protocol-aligned 8-section summaries Supports multiple therapeutic areas including Urology, Oncology, Cardiovascular, CNS, and Metabolic/GLP-1 Adapts to diverse study designs, visit types, and protocol requirements Deployable securely on-premise or in the cloud, including local inference on NVIDIA DGX Spark, ensuring full data privacy and regulatory compliance
How it Works
Functional Workflow
Pre-Processing & Data Extraction
- Identifies participant ID, phase, therapeutic area, visit type
- Extracts symptoms, safety notes, compliance data
- Captures labs, procedures, deviations, and next-visit details
Fine-Tuned Visit Summaries
- Generates consistent, protocol-aligned summaries
- Outputs structured JSON ready for EDC, CTMS, and RTSM systems
Template & Consistency Enforcement
- Ensures all 8 sections are always present
- Prevents hallucinated or inferred content
- Enforces standardized terminology and formatting
Privacy-Safe, On-Device Deployment
- Fully on-prem inference using DGX Spark (128GB unified memory)
- Zero data egress with support for HIPAA and 21 CFR Part 11
- Optional cloud deployment on GCP or Azure
Business Impact
- 70% of visit summaries required no CRC edits
- Remaining summaries needed only minor formatting or terminology adjustments
- Consistent documentation across sites, coordinators, and studies
- Seamless integration into mock EDC and RTSM workflows
Key Benefits
key Outcomes with Fine Tune Private Model For Clinical Visit Summary
Documentation Time
Under 1 minute per visit - 90% Reduction
Accuracy Rate
70% of summaries are 100% accurate - High Consistency
Data Readiness
Structured JSON for direct integration - Clean Integration
Technical Foundation
Model
Google Gemma 3 (fine-tuned Small Language Model)
Training Data
1,000+ curated clinical visit–summary pairs
Runtime
NVIDIA DGX Spark (on-prem) or GCP / Azure
Framework
Custom pre-processor and normalization pipeline
Guardrails
SOP-aware validation layer enforcing accuracy, structure, and compliance
From Manual Documentation to AI-Driven Clinical Precision
The Clinical Trial Assistant demonstrates how specialized, fine-tuned AI models can deliver real value in regulated clinical environments. By reducing documentation time by nearly 90%, improving first-draft accuracy, and ensuring standardized, compliant outputs, the solution addresses one of the most persistent operational bottlenecks in clinical research. Its secure, on-premise deployment model and strong guardrails make it a practical, scalable foundation for modernizing clinical trial operations without compromising privacy, precision, or regulatory trust.
