Aim: To evaluate the MONA.health artificial intelligence screening software for detecting referable diabetic retinopathy (DR) and diabetic macular edema (DME), including subgroup analysis. Methods: The algorithm’s threshold value was fixed at the 90% sensitivity operating point on the receiver operating curve to perform the disease classification. Diagnostic performance was appraised on a private test set and publicly available datasets. Stratification analysis was executed on the private test set considering age, ethnicity, sex, insulin dependency, year of examination, camera type, image quality, and dilatation status. Results: The software displayed an area under the curve (AUC) of 97.28% for DR and 98.08% for DME on the private test set. The specificity and sensitivity for combined DR and DME predictions were 94.24 and 90.91%, respectively. The AUC ranged from 96.91 to 97.99% on the publicly available datasets for DR. AUC values were above 95% in all subgroups, with lower predictive values found for individuals above the age of 65 (82.51% sensitivity) and Caucasians (84.03% sensitivity). Conclusion: We report good overall performance of the MONA.health screening software for DR and DME. The software performance remains stable with no significant deterioration of the deep learning models in any studied strata.

Objective: Clinical AI systems and models increasingly need traceable documentation that makes intended use, performance evidence, transparency, and lifecycle status explicit, yet existing model cards, datasheets, reporting guidelines, and other documentation approaches do not consistently bring together structured clinical fields, vocabulary-linked cohort descriptions, structured performance reporting, and governed documentation history. We introduce Structured, Meaningful, Auditable, Responsible, and Transparent (SMART), a framework that adapts the model card concept into structured, lifecycle-aware documentation with OMOP integration, role-based lifecycle governance, and a blockchain-backed audit trail for verifying documentation integrity and lifecycle history. Materials and Methods: Following a design-science-informed approach, we analyzed documentation requirements, reviewed existing approaches, implemented SMART through open-source packages and smart contracts, and evaluated it using example SMART model cards, an illustrative COPD exacerbation risk-prediction documentation case study, and blockchain-governed lifecycle experiments. Results: The design process resulted in the SMART framework in which documentation provisions are reflected across Schema, Lifecycle, and Chain (ie, Blockchain) components. Evaluation showed that, in multi-party settings, documentation history remains independently verifiable because lifecycle actions are role-attributed and linked to content hashes; median testnet gas use ranged from 352 722 to 852 399 gas across the evaluated lifecycle operations. Discussion: SMART positions clinical AI documentation as a shared infrastructure for preliminary model assessment, making role attribution, lifecycle state, and documentation changes more transparent and traceable over time. Conclusion: By providing an implementable framework, SMART illustrates how clinical AI documentation can be structured, clinically contextualized, auditable, responsible, transparent, and lifecycle-aware.