Purpose
To establish rigorous, clinically sound, and reproducible standards for the acquisition, verification, processing, and interpretation of blood-derived laboratory data using artificial intelligence (AI). These guidelines are intended to support safe, ethical, and standardized integration of AI technologies into diagnostic workflows and may serve as a foundation for broader industry-wide adoption.

1. Scope
This standard applies to all AI-supported systems used for the analysis and interpretation of blood test results, including but not limited to:

• Complete Blood Count (CBC)
• Comprehensive and Basic Metabolic Panels
• Lipid and Liver Panels
• Coagulation Studies
• Immunological, Endocrine, and Inflammatory Markers
• AI-based clinical decision support systems using blood data for risk stratification or diagnostic hypothesis generation

2. Data Handling Protocols
2.1 Data Acquisition

• Laboratory results must be obtained from ISO 15189-accredited laboratories or equivalent.
• All test results must be accompanied by structured metadata, including:
• Anonymized patient identifier
• Time and date of sample collection
• Reference intervals
• Analytical method and instrument identifier

2.2 Data Integrity Checks

• Values must be verified against clinically accepted physiological ranges.
• Quality control measures must detect and flag:
• Biological implausibilities
• Sample degradation indicators
• Missing or corrupted values

3. Dual-Level Data Revalidation Framework
3.1 Level 1: Preprocessing Validation

• Upon data ingestion, all input values are subject to:
• Plausibility screening based on population-level clinical norms
• Intra-patient temporal consistency checks, if historical data are available
• Analytical variance modeling to identify instrument-specific anomalies

3.2 Level 2: Independent AI Cross-Validation

• Diagnostic outputs must be verified by a secondary, independently trained AI model.
• A divergence of >5% in predicted classification or risk scoring must:
• Trigger expert system review or
• Flag the case for human clinical oversight

🧠 4. AI System Standards
4.1 Model Transparency

• Every deployed model must include a model factsheet (“model card”) detailing:
• Data sources and preprocessing pipelines
• Training-validation-test distribution
• Performance metrics and known limitations
• Regulatory certification status (if applicable)

4.2 Explainability Requirements

• AI outputs must be accompanied by:
• A ranked list of salient input features (e.g. top contributing biomarkers)
• Confidence intervals or scoring
• Decision traceability via SHAP, LIME, or equivalent explainable AI (XAI) methodology

4.3 Bias Assessment

• Models must undergo stratified performance audits across:
• Sex and gender
• Age groups
• Racial/ethnic backgrounds
• Relevant comorbidity clusters

5. Data Security and Regulatory Compliance

• All systems must comply with GDPR, HIPAA, or applicable national data protection regulations.
• Data must be stored in encrypted environments, with transmission protected via TLS 1.3 or higher.
• All access and changes must be logged with immutable audit trails, retained for a minimum of 10 years.

6. Clinical Deployment Standards

• All diagnostic suggestions must be presented as clinical decision support, not final diagnoses, unless the AI system is certified as a medical device.
• Outputs must be clinically interpretable, using standard terminology and reference values.
• Interoperability must be ensured via HL7 FHIR, LOINC, and SNOMED CT coding where applicable.

7. Continuous Model Surveillance

• Deployed AI systems must undergo quarterly performance re-evaluation using real-world data.
• Model drift detection must be automated and tied to alert systems for retraining thresholds.
• Clinician feedback should be captured and integrated into the AI lifecycle for post-market surveillance.

8. Certification and Ecosystem Engagement
AIMA Diagnostics proposes these standards for open collaboration and peer review, with the intent of eventual harmonization through international regulatory and standards bodies, including:

• HL7 International
• ISO/TC 215 Health Informatics
• IMDRF (International Medical Device Regulators Forum)
• Local and national health data authorities

Appendix: Key Definitions

• Data Revalidation: The process of confirming the accuracy and reliability of data through independent review mechanisms before diagnostic use.
• Dual-AI Architecture: A model validation approach wherein outputs are confirmed via a second independently trained algorithm.
• Model Card: A structured summary describing an AI model’s performance, training context, limitations, and appropriate use cases.

Version 1.2 | July 2025
Developed by AIMA Diagnostics, Oslo, Norway