Healthcare AI Model Evaluation

Comprehensive AI/ML model quality assessment following TRIPOD+AI prediction model reporting, CONSORT-AI clinical trial standards, STARD-AI diagnostic accuracy guidelines, and FDA SAMD clinical evaluation framework.

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Critical Disclaimer: Educational Use Only

This AI model evaluation tool is for educational, research methodology planning, and preliminary self-assessment only. It does NOT constitute: regulatory submission support, formal model validation, FDA clearance/approval, peer review, or professional scientific consultation. Healthcare AI models require rigorous validation by multidisciplinary teams including AI scientists, clinicians, biostatisticians, ethicists, and regulatory experts. This tool provides general guidance based on reporting standards and best practices but cannot replace comprehensive model development, validation studies, external audits, or regulatory pathways.Do not deploy AI models clinically based solely on this assessment. Engage qualified professionals for model validation, bias auditing, regulatory strategy, and clinical implementation. Publication in peer-reviewed journals and regulatory clearance are required for clinical deployment.

Assessment Methodology

Framework Basis

This assessment integrates cutting-edge AI reporting and validation frameworks:

TRIPOD+AI (2024): Transparent Reporting of prediction models using AI (updated guidance)
CONSORT-AI (2020): Reporting standards for AI intervention clinical trials
STARD-AI: Standards for Reporting Diagnostic accuracy studies using AI
FDA SAMD Guidance: Clinical evaluation requirements for AI/ML medical devices
IMDRF Framework: International Medical Device Regulators Forum SaMD standards

Scoring System

Weighted scoring across 7 model quality dimensions:

Study Design & Reporting (20%): Adherence to AI reporting guidelines, protocol, population definition
Data Quality (20%): Data QA, train/test split, preprocessing documentation
Model Architecture (15%): Architecture specification, training procedures, hyperparameters
Performance Evaluation (20%): Metrics, external validation, subgroup analysis, statistics
Bias & Fairness (10%): Fairness assessment, bias sources, equity analysis
Explainability (10%): Interpretability, limitations documentation, transparency
Clinical Integration (5%): Workflow assessment, clinical impact evidence

Interpretation Guidelines

80-100 (High Maturity): Deployment-ready; meets best-practice standards for regulatory submission
60-79 (Moderate Maturity): Good foundation; additional validation/documentation needed
40-59 (Low Maturity): Substantial gaps; significant additional development required
0-39 (Early Stage): Not ready for clinical use; major validation and methodology improvements needed

Each question includes detailed methodology notes citing AI research best practices and regulatory standards.

Questions answered: 0 / 18

Study Design & Reporting (Weight: 20%)

1. Does the study adhere to AI-specific reporting guidelines (TRIPOD-AI, CONSORT-AI, or STARD-AI)?

Full adherence with checklistComplete adherence to relevant AI reporting guideline with published checklist

Substantial adherenceCovers most guideline items with minor gaps

Partial adherenceKey elements present but incomplete reporting

Minimal adherenceStandard reporting without AI-specific elements

No adherenceDoes not follow AI reporting guidelines

Study Design & Reporting (Weight: 20%)

2. Is there a published protocol or pre-registration with pre-specified analysis plan?

Published protocol with pre-registrationDetailed protocol published before data collection with registry number

Pre-registeredStudy registered prospectively with analysis plan

Protocol availableProtocol exists but not pre-registered

Minimal documentationPost-hoc study plan only

No protocolNo documented protocol or pre-registration

Study Design & Reporting (Weight: 20%)

3. Is the study population clearly defined with inclusion/exclusion criteria?

Comprehensive definitionDetailed eligibility criteria, setting, recruitment period, patient flow diagram

Clear definitionGood description of eligibility and setting

Basic definitionGeneral description without detailed criteria

Vague definitionUnclear or incomplete population description

No definitionStudy population not described

Data Quality (Weight: 20%)

4. Is there comprehensive description of data sources, quality assurance, and missingness handling?

Comprehensive QA documentationDetailed data provenance, quality metrics, missingness analysis with sensitivity tests

Good QA documentationClear data source and missingness handling described

Basic documentationGeneral description without detailed QA

Minimal documentationLimited data quality information

No QA documentationData quality not addressed

Data Quality (Weight: 20%)

5. Are training, validation, and test sets properly separated with clear data split rationale?

Strict separation with external testTraining, internal validation, external test set from different sites/times

Proper train/val/test splitThree-way split with documented methodology

Train/test split onlyTwo-way split without separate validation

Unclear splitData splitting methodology unclear

No proper splitRisk of data leakage or overfitting

Data Quality (Weight: 20%)

6. Are preprocessing steps (feature engineering, normalization, augmentation) fully documented and justified?

Comprehensive preprocessing documentationAll steps detailed with code/parameters, justification, leakage prevention verified

Good documentationMajor preprocessing steps described clearly

Basic documentationGeneral preprocessing mentioned

Minimal documentationLimited preprocessing detail

No documentationPreprocessing not described

Model Architecture (Weight: 15%)

7. Is the model architecture clearly described with hyperparameter tuning methodology?

Complete architecture specificationFull model details, hyperparameters, tuning methodology, code/weights available

Good specificationModel architecture and key hyperparameters described

Basic specificationModel type mentioned, limited detail

Vague specificationIncomplete model description

No specificationModel architecture not described

Model Architecture (Weight: 15%)

8. Are model training procedures (optimization, regularization, early stopping) documented?

Comprehensive training documentationComplete training details, convergence plots, computational requirements

Good documentationMajor training procedures described

Basic documentationTraining approach mentioned

Minimal documentationLimited training detail

No documentationTraining procedures not described

Performance Evaluation (Weight: 20%)

9. Are multiple performance metrics reported including discrimination, calibration, and clinical utility?

Comprehensive metricsDiscrimination, calibration, clinical utility (DCA/NNT), threshold analysis

Good metricsDiscrimination and calibration reported

Standard metricsBasic discrimination metrics (AUROC, accuracy)

Limited metricsOnly AUROC or accuracy

Inadequate metricsInappropriate or missing metrics

Performance Evaluation (Weight: 20%)

10. Is external validation performed at independent sites or time periods?

Multiple external validationsValidated at 3+ independent sites or multiple time periods

External validation performedValidated at 1-2 independent sites/times

Temporal internal validationValidated on future data from same site

Internal validation onlyCross-validation or holdout from same dataset

No validationOnly training performance reported

Performance Evaluation (Weight: 20%)

11. Is subgroup performance analyzed for demographic and clinical subgroups?

Comprehensive subgroup analysisPerformance stratified by demographics and clinical factors with fairness metrics

Good subgroup analysisPerformance analyzed for major subgroups

Basic subgroup analysisLimited subgroup analysis performed

Minimal analysisSubgroup performance mentioned but not detailed

No subgroup analysisOnly overall performance reported

Performance Evaluation (Weight: 20%)

12. Are confidence intervals and statistical significance tests appropriate and correctly interpreted?

Rigorous statistical analysisAppropriate CIs, significance tests, multiple testing correction, uncertainty quantification

Good statistical analysisCIs and significance tests reported appropriately

Basic statistical analysisSome CIs or p-values reported

Minimal statistical analysisStatistical testing incomplete

No statistical analysisNo uncertainty quantification

Bias & Fairness (Weight: 10%)

13. Is algorithmic bias systematically assessed with fairness metrics?

Comprehensive fairness analysisMultiple fairness metrics across subgroups with mitigation strategies

Good fairness analysisSystematic bias testing with fairness metrics

Basic fairness analysisPerformance compared across subgroups

Minimal analysisFairness mentioned but not quantified

No fairness analysisAlgorithmic bias not assessed

Bias & Fairness (Weight: 10%)

14. Are potential sources of bias (selection, measurement, confounding) discussed?

Comprehensive bias discussionDetailed analysis of bias sources with sensitivity analyses

Good bias discussionMajor bias sources identified and discussed

Basic bias discussionSome potential biases mentioned

Minimal discussionBias briefly mentioned

No bias discussionPotential biases not discussed

Explainability (Weight: 10%)

15. Are model predictions interpretable or explained using post-hoc methods?

Rigorous explainabilityInherently interpretable model or validated post-hoc explanations (SHAP/LIME)

Good explainabilityPost-hoc explanations provided with examples

Basic explainabilityFeature importance or simple explanations

Minimal explainabilityLimited interpretation provided

No explainabilityBlack-box model without explanations

Explainability (Weight: 10%)

16. Are model limitations, failure modes, and appropriate use cases clearly documented?

Comprehensive limitationsDetailed limitations, failure modes, contraindications, edge cases with examples

Good limitationsMajor limitations and appropriate use clearly described

Basic limitationsGeneral limitations mentioned

Minimal limitationsVague limitation statements

No limitationsLimitations not discussed

Clinical Integration (Weight: 5%)

17. Is there assessment of clinical workflow integration and usability?

Prospective implementation studyReal-world implementation with workflow analysis, usability testing, outcomes measured

Workflow analysisClinical workflow integration assessed with stakeholders

Usability consideredBasic usability or workflow discussion

Minimal considerationImplementation briefly mentioned

Not consideredClinical integration not addressed

Clinical Integration (Weight: 5%)

18. Is there evidence of clinical impact on patient outcomes or healthcare processes?

RCT with patient outcomesRandomized trial demonstrating impact on patient health outcomes

Implementation study with outcomesPre-post or cohort study with clinical outcome measurement

Process outcomesImpact on healthcare processes (time, efficiency) measured

Theoretical impactHypothetical or simulated clinical impact

No impact assessmentClinical impact not evaluated

Answer all 18 questions for comprehensive AI model quality evaluation and detailed recommendations.