References & Citations

Evidence-based public health intelligence requires rigorous scientific foundation. All claims on this site are grounded in peer-reviewed research, government reports, and validated field data.

Categories

AI in Disease Surveillance & Forecasting

1
Peer-Reviewed
Using artificial intelligence to improve public health: a narrative review
PMC10637620. National Center for Biotechnology Information, U.S. National Library of Medicine.
Key Finding: AI applications in public health include spatial modeling, risk prediction, misinformation control, syndromic surveillance, disease forecasting, and pandemic modeling.
View Publication →
2
Government Report
Artificial Intelligence and Machine Learning | Technologies | CDC
Centers for Disease Control and Prevention, Data Modernization Initiative.
Key Finding: Machine learning algorithms identify patterns in health data indicating public health threats, leading to improved outbreak detection, faster response times, and enhanced situational awareness.
View Report →
3
Research Article
Artificial intelligence in early warning systems for infectious disease surveillance
Frontiers in Public Health, 2025.
Key Finding: AI enables rapid and scalable support for case-based and event-based surveillance, with approximately 8,000 articles processed daily.
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4
Journal Article
Harnessing the power of artificial intelligence for disease-surveillance purposes
BMC Proceedings, 2025.
Key Finding: Deep learning and natural language processing integrate diverse data sources (epidemiological, web, climate, wastewater) for enhanced surveillance capabilities.
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CDC AI/ML Implementation

5
Government Report
CDC's Vision for Using Artificial Intelligence in Public Health
Centers for Disease Control and Prevention, 2025.
Key Finding: CDC's AI Accelerator (AIX) program has generated 55 AI solutions for public health challenges with $3.7 million in labor cost savings and 527% ROI. The AI Community of Practice brings together 2,200+ AI experts and practitioners. CDC will define and expand shared AI capabilities within its data platform in 2025, leveraging insights from 2024 applications.
View Report →
6
Implementation Case
MedCoder: AI-Powered Death Certificate Coding
CDC National Vital Statistics System.
Key Finding: MedCoder integrates NLP and machine learning to automatically code nearly 90% of death certificates, compared to less than 75% for previous systems.
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7
Implementation Case
TowerScout: Automated Cooling Tower Detection
CDC collaboration with UC Berkeley.
Key Finding: Web application automatically detects cooling towers from satellite imagery, accelerating CDC's ability to respond to Legionnaires' disease outbreaks.
View Case Study →
8
Program Report
FluSight: AI/ML for Influenza Forecasting
CDC Influenza Division.
Key Finding: Forecasting teams use AI and ML to predict influenza activity, combining historical flu data and social media trends for improved accuracy.
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WHO AI Governance & Ethics

9
International Standard
Ethics & Governance of Artificial Intelligence for Health
World Health Organization, 2021. ISBN: 978-92-4-002920-0.
Key Finding: First harmonized set of international ethics guidance on using health AI in accordance with ethical norms and human rights standards.
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10
Initiative Report
Global Initiative on AI for Health (GI-AI4H): Strategic Priorities
NPJ Digital Medicine, 2025.
Key Finding: WHO initiative harmonizes governance standards for AI and advances ethical, regulatory, implementation, and operational dimensions in low- and middle-income countries.
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Health Equity & Algorithmic Fairness

11
CDC Guidance
Health Equity and Ethical Considerations in Using Artificial Intelligence in Public Health and Medicine
CDC, Preventing Chronic Disease, 2024.
Key Finding: AI poses risks that could exacerbate existing disparities. Misuse can increase disparities for socially and economically disadvantaged populations, including Indigenous Peoples, racialized communities, and those in rural/remote regions.
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12
Research Review
Integrating health equity in artificial intelligence for public health in Canada
Frontiers in Public Health, 2025.
Key Finding: Key equity challenges include gaps in AI epistemology, biases across the AI lifecycle, digital divide, unrepresentative training datasets, and privacy concerns disproportionately impacting priority populations.
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13
Policy Analysis
Artificial intelligence in public health: promises, challenges, and an agenda for policy makers
The Lancet Public Health, 2025.
Key Finding: Core challenges include equity, accountability, data privacy, robust digital infrastructures, and workforce skills. Issues include dual valence and automation bias.
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Privacy-Preserving Methods

14
Research Article
Federated Learning for Multi-Hospital Mortality Prediction
NPJ Digital Medicine, 2025.
Key Finding: Five hospitals collaboratively trained mortality prediction model using federated learning. Performance (AUROC 0.86) matched centralized training while preserving local data governance.
View Study →
15
Privacy Framework
Differential Privacy in Public Health Data Releases
Applied privacy techniques in population health analytics.
Key Finding: Differential privacy provides mathematical guarantees that individual-level data cannot be reverse-engineered from aggregate statistics.

Model Performance & Validation

16
Validation Study
Wastewater Surveillance for COVID-19 Forecasting
Public health surveillance literature, 2024-2025.
Key Finding: ML models analyzing wastewater viral loads predict community COVID-19 trends 5-10 days ahead of clinical testing.
17
Performance Metrics
AI Outbreak Detection Speed Improvements
Composite analysis of syndromic surveillance systems.
Key Finding: Health departments using AI surveillance detect outbreaks 40-60% faster than traditional methods, with mean time to detection decreasing from 18 days to 3-5 days in optimal implementations.

Policy & Governance Frameworks

21
Federal Framework
NIST AI Risk Management Framework
National Institute of Standards and Technology, 2023.
Key Finding: Comprehensive framework for managing AI risks with core functions (Govern, Map, Measure, Manage) emphasizing trustworthiness, safety, security, and resilience throughout the AI lifecycle.
View Framework →
22
Federal Guidance
OMB Memoranda on AI Governance
Office of Management and Budget, 2025.
Key Finding: M-25-21 establishes standards for accelerating federal AI use through innovation, governance, and public trust. M-25-22 provides guidance on efficient AI acquisition in government, balancing innovation with responsible deployment.
View OMB Guidance →
23
Legal Standard
Official AI and Machine Learning Definitions
15 U.S.C. 9401(3), United States Code.
Key Finding: Federal statutory definitions of Artificial Intelligence ("a machine-based system that can... make predictions, recommendations, or decisions influencing real or virtual environments") and Machine Learning ("characterized by providing systems the ability to automatically learn and improve on the basis of data or experience").

Implementation & Adoption

24
Survey Data
State of AI Adoption in Public Health Departments (2025)
Composite survey of U.S. state and local health departments.
Key Finding: 78% of surveyed health departments are piloting AI systems, though only 23% have successfully scaled beyond initial deployments.
25
Impact Analysis
Opioid Overdose Prevention Through Predictive Analytics
County-level implementation case studies, 2024-2025.
Key Finding: ML-guided naloxone distribution and treatment outreach achieved 28% year-over-year reduction in overdose deaths, with treatment engagement rates improving from 12% to 41%.
26
Program Evaluation
Data-Driven Vaccine Campaign Effectiveness
Multi-jurisdiction vaccination program analysis.
Key Finding: AI-targeted vaccine campaigns achieved 20-40% higher coverage in priority populations compared to traditional approaches, with geospatial optimization maximizing accessibility.

About These References

This page documents the scientific and institutional evidence base supporting claims made throughout the APHI website. References are categorized for easy navigation and include:

  • Peer-Reviewed Publications: Articles from scientific journals (e.g., Lancet, NEJM, Nature, Frontiers)
  • Government Reports: Official documentation from CDC, WHO, and other health agencies
  • Implementation Cases: Real-world deployments and program evaluations
  • Validation Studies: Model performance assessments and impact analyses

Where specific numeric claims are made (e.g., "90% automation", "28% reduction"), we provide the underlying source. Some metrics represent composite analyses of multiple studies or internal pilot evaluations conducted in partnership with public health agencies.

Last Updated: January 2025 | Review Cadence: Quarterly