Authoritative Source Ledger

Research cutoff and link check: 2026-07-23.

Research used parallel-cli search and parallel-cli extract, constrained to official agencies, standards groups, guideline hosts, and primary publications. Status labels below reflect the source on the cutoff date. A link in this ledger is not an endorsement of an artifact or a substitute for checking the current source before use.

FDA: CDS and AI-Enabled Devices

• Clinical Decision Support Software — FDA final guidance, January 2026; page reissued/content current January 29, 2026. Defines FDA's current interpretation of non-device CDS criteria and device-software boundaries. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/clinical-decision-support-software
• Marketing Submission Recommendations for a Predetermined Change Control Plan for AI-Enabled Device Software Functions — FDA final guidance, August 2025. Used for planned modifications, validation methodology, impact assessment, and change-control concepts. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/marketing-submission-recommendations-predetermined-change-control-plan-artificial-intelligence
• AI-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations — FDA draft guidance, January 2025; explicitly draft/not for implementation on the cutoff date. Used only as clearly labeled draft lifecycle context. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/artificial-intelligence-enabled-device-software-functions-lifecycle-management-and-marketing
• Good Machine Learning Practice for Medical Device Development: Guiding Principles — FDA page current December 19, 2025, linking the January 2025 IMDRF final principles. Used for lifecycle, representative data, human-AI team, and independent testing themes. https://www.fda.gov/medical-devices/software-medical-device-samd/good-machine-learning-practice-medical-device-development-guiding-principles
• Transparency for Machine Learning-Enabled Medical Devices: Guiding Principles — FDA/Health Canada/MHRA, June 13, 2024. Used for intended users, limitations, data characterization, uncertainty, human factors, monitoring, and update communication. https://www.fda.gov/medical-devices/software-medical-device-samd/transparency-machine-learning-enabled-medical-devices-guiding-principles
• Artificial Intelligence in Software as a Medical Device — FDA topic page, content current March 25, 2025 in search results. Used to cross-check the guidance sequence. https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-software-medical-device

ONC / HTI-1

• HTI-1 Final Rule — official Federal Register text, January 2024. Used for the legal scope of predictive DSI/source-attribute and intervention-risk-management requirements. https://www.federalregister.gov/citation/89-FR-1391
• HTI-1 Decision Support Interventions Fact Sheet — ONC, December 2023. Used for the section 170.315(b)(11) overview and predictive-DSI transparency categories. https://www.healthit.gov/wp-content/uploads/2023/12/HTI-1_DSI_fact-sheet_508.pdf
• Requirements for Decision Support Interventions and Predictive Models — ONC final-rule presentation, January 18, 2024. Used for intended use, population, user, decision role, out-of-scope use, fairness, validation, performance, and maintenance source attributes. https://healthit.gov/wp-content/uploads/2024/01/DSI_HTI1-Final-Rule-Presentation_508.pdf
• HTI-1 Final Rule landing page — ONC. Used to verify official supporting materials and current resource location. https://healthit.gov/regulations/hti-rules/hti-1-final-rule

GRADE

• GRADE Working Group — official overview and minimum requirements. Used for outcome-specific certainty, explicit domain judgments, evidence profiles, and Evidence-to-Decision separation. https://www.gradeworkinggroup.org/
• GRADE Book — official current resource, progressively replacing the prior handbook by 2026. Used as the preferred methodology entry point. https://book.gradepro.org/
• GRADE Handbook — legacy/current transition resource. Retained for comparison where a GRADE Book chapter is not yet available; verify against the GRADE Book. https://gradepro.org/handbook

AI and Clinical-Study Reporting

• TRIPOD+AI — Collins et al., BMJ 2024;385:e078378, published April 16, 2024. Reporting of prediction-model development/evaluation using regression or machine learning. https://www.bmj.com/content/385/bmj-2023-078378
• TRIPOD+AI EQUATOR record — scope, checklist, and related materials. https://www.equator-network.org/reporting-guidelines/tripod-statement
• CONSORT-AI — Liu et al., Nature Medicine 2020;26:1364-1374, published September 9, 2020. AI-intervention randomized-trial reports. https://www.nature.com/articles/s41591-020-1034-x
• CONSORT 2025 — Hopewell et al., BMJ 2025;389:e081123, published April 14, 2025. Current generic base statement used with CONSORT-AI. https://www.bmj.com/content/389/bmj-2024-081123
• SPIRIT-AI — Rivera et al., Nature Medicine 2020;26:1351-1363, published September 9, 2020. AI-intervention trial protocols. https://www.nature.com/articles/s41591-020-1037-7
• SPIRIT 2025 — current generic base statement used with SPIRIT-AI. https://pubmed.ncbi.nlm.nih.gov/40295741
• DECIDE-AI — Vasey et al., Nature Medicine 2022;28:924-933, published May 18, 2022. Early-stage live clinical evaluation of AI-based decision-support systems. Included for reporting context; live evaluation is outside this skill. https://www.nature.com/articles/s41591-022-01772-9
• DECIDE-AI EQUATOR record — scope and publication links. https://www.equator-network.org/reporting-guidelines/reporting-guideline-for-the-early-stage-clinical-evaluation-of-decision-support-systems-driven-by-artificial-intelligence-decide-ai/
• STARD-AI — Sounderajah et al., Nature Medicine, published September 15, 2025, DOI 10.1038/s41591-025-03953-8. Final reporting guideline for AI diagnostic-accuracy studies. https://www.nature.com/articles/s41591-025-03953-8
• STARD-AI EQUATOR record — final status, scope, citation, and checklist location. https://www.equator-network.org/reporting-guidelines/the-stard-ai-reporting-guideline-for-diagnostic-accuracy-studies-using-artificial-intelligence/

Prediction-Model Risk of Bias

• PROBAST+AI — Moons et al., BMJ 2025;388:e082505, published March 24, 2025. Current quality/risk-of-bias/applicability tool for regression and AI prediction models; separates development from evaluation and uses participants/data sources, predictors, outcome, and analysis domains. https://pubmed.ncbi.nlm.nih.gov/40127903
• PROBAST+AI project site — tool resources and updates. https://www.probast.org/probast_ai

Privacy and De-identification

• HHS Guidance Regarding Methods for De-identification of PHI — official OCR guidance; page current March 20, 2026 in extraction. Used for Expert Determination, Safe Harbor, actual knowledge, derivatives, and free-text cautions. https://www.hhs.gov/hipaa/for-professionals/special-topics/de-identification/index.html
• 45 CFR 164.514 — current eCFR text for de-identification and related requirements. https://www.ecfr.gov/current/title-45/subtitle-A/subchapter-C/part-164/subpart-E/section-164.514

ICH

• ICH E6(R3) consolidated Step 4 guideline — final version adopted June 16, 2026, consolidating principles, Annex 1, and Annex 2. Used for quality by design, fit-for-purpose data, oversight, privacy, auditability, and modern trial settings. https://database.ich.org/sites/default/files/ICH%20E6(R3)_Step4_FinalConsolidatedGuideline_2026_0616_.pdf
• ICH E9(R1) Addendum on Estimands and Sensitivity Analysis — final, adopted November 20, 2019. Used for estimand-led planning and sensitivity analysis. https://database.ich.org/sites/default/files/E9-R1_Step4_Guideline_2019_1203.pdf
• ICH efficacy-guideline index — official status/version cross-check. https://www.ich.org/page/efficacy-guidelines

Cohort, Survival, and Biomarker Methods

• STROBE — official reporting guidance for observational studies. https://www.strobe-statement.org/
• RECORD — reporting extension for routinely collected health data. https://www.record-statement.org/
• REMARK — reporting recommendations for tumor-marker prognostic studies. https://www.equator-network.org/reporting-guidelines/reporting-recommendations-for-tumour-marker-prognostic-studies-remark
• FDA-NIH BEST Resource — living biomarker and endpoint terminology resource, 2016 onward. https://www.ncbi.nlm.nih.gov/books/NBK326791/
• External validation of clinical prediction models — Riley et al., BMJ 2024;384:e074820, published January 15, 2024. Used for locked-model evaluation, calibration, discrimination, utility, and transparent reporting. https://www.bmj.com/content/384/bmj-2023-074820
• External-validation sample size — Riley et al., Statistics in Medicine 2021. Used to reject blanket event-count rules and emphasize precision targets. https://pmc.ncbi.nlm.nih.gov/articles/PMC8352630
• Calibration: the Achilles heel of predictive analytics — Van Calster et al., BMC Medicine 2019. Used for calibration assessment and interpretation. https://pubmed.ncbi.nlm.nih.gov/31842878
• Restricted mean survival time — Royston and Parmar, BMC Medical Research Methodology 2013;13:152. Used as an alternative population-level summary when proportional hazards is doubtful. https://pubmed.ncbi.nlm.nih.gov/24314264/
• Competing risks introduction — Austin, Lee, and Fine, Circulation 2016;133:601-609. Used to distinguish cause-specific hazards, subdistribution hazards, and cumulative incidence. https://pubmed.ncbi.nlm.nih.gov/26858290/
• Fine-Gray reporting recommendations — Austin and Fine, Statistics in Medicine 2017;36:4391-4400. Used for careful interpretation of subdistribution hazard models. https://pmc.ncbi.nlm.nih.gov/articles/PMC5698744

Deliberately Out of Scope

HL7 CDS Hooks, SMART on FHIR, and FHIR implementation guidance were not added because the version 2.0 safety redesign deliberately removes recommendation-oriented and live CDS behavior. It produces offline research/governance artifacts only; implementation guidance would conflict with the hard boundary.

No source requiring an API key, external model, image generator, or network call is used at runtime.