Clinical & Safety

How accurate are PreConsult's AI assessments?

Clinical validation and safety measures

Evidence-Based Clinical Accuracy

PreConsult uses advanced large language model reasoning with Australian medical guidelines, SNOMED CT-AU standardized terminology, and continuous validation. Importantly, all AI outputs are suggestions requiring practitioner review—PreConsult augments clinical decisions, never replaces them.

Our Approach to Clinical Accuracy

Human-in-the-Loop Design

All AI-generated suggestions require practitioner review and acceptance before clinical use. This ensures that clinical expertise always guides final decisions, with AI serving as a preparation and documentation tool.

Ongoing Validation

We continuously monitor AI performance through practitioner feedback loops. When practitioners edit or dismiss suggestions, this data improves future outputs and helps us identify areas for improvement.

How We Ensure Accuracy

Evidence-Based Foundation

Australian Clinical Guidelines
eTG, RACGP Red Book, therapeutic guidelines
SNOMED CT-AU Integration
National Terminology Server standardization
Advanced LLM Reasoning
State-of-the-art clinical language understanding

Continuous Validation

Clinical Expert Review
Board-certified GPs validate all outputs
Real-World Feedback Loop
GP edits improve model performance
Performance Monitoring
Continuous accuracy tracking and reporting

Clinical Safety Measures

Safety Layer	Description	Purpose
Human-in-the-Loop Mandatory GP review	All AI suggestions require qualified GP review and acceptance	Primary safety control
Non-Prescriptive Language TGA-exempt compliance	Uses "commonly associated with" not "diagnosis is"	Regulatory compliance
Emergency Guidance Patient safety reminders	Patients reminded to call 000; practitioners notified of urgent symptoms	Patient safety
Audit Trails Complete documentation	All AI suggestions and GP decisions logged	Quality assurance
Language Compliance Validation Automated checking	Ensures all outputs use appropriate non-prescriptive language	Regulatory compliance

Types of AI Clinical Support

History Extraction

Symptom documentation
Timeline extraction
Associated factors
Medical history review

Differential Suggestions

Condition suggestions
Clinical reasoning
Risk considerations
Investigation prompts

Treatment Considerations

Management options
Guideline alignment
Patient education points
Follow-up recommendations

Known Limitations & Transparency

We're Transparent About What AI Can and Cannot Do

What AI Does Well

Comprehensive history collection
Pattern recognition in symptoms
Guideline-based suggestions
Consistent documentation quality

What Requires Human Expertise

Physical examination findings
Contextual clinical judgment
Patient-specific nuances
Final diagnostic decisions

Validation & Research

PreConsult is committed to evidence-based validation of our clinical decision support capabilities. Our approach includes:

Continuous monitoring of practitioner acceptance, edit, and dismissal patterns
Regular review by qualified medical practitioners
Ongoing refinement based on real-world clinical feedback
Alignment with Australian clinical guidelines and SNOMED CT-AU standards

Transparency: We believe in honest communication about AI capabilities. As we gather more real-world data from clinical use, we will publish validated accuracy metrics and performance data.