Appendix M: Further Reading

Courses, books, journals, datasets, software, and professional communities for learning AI in public health, grouped by type and level.

Online Courses

Foundational Machine Learning

Supervised Machine Learning: Regression and Classification

  • Instructor: Andrew Ng
  • Provider: DeepLearning.AI / Stanford Online
  • Platform: Coursera
  • Level: Intermediate
  • Duration: 3 weeks at the provider’s suggested pace
  • Topics: Regression, classification, regularization, and supervised-learning practice
  • Why take it: Structured introduction to supervised machine learning
  • Link: https://www.coursera.org/learn/machine-learning

Fast.ai: Practical Deep Learning for Coders

  • Instructors: Jeremy Howard, Rachel Thomas
  • Platform: Fast.ai
  • Level: Beginner to advanced
  • Duration: Self-paced
  • Topics: Deep learning, computer vision, NLP, ethics
  • Why take it: Top-down approach, code-first, healthcare examples
  • Link: https://course.fast.ai/

MIT 6.S191: Introduction to Deep Learning

  • Platform: MIT OpenCourseWare
  • Level: Intermediate
  • Duration: 1 week intensive
  • Topics: Neural networks, CNNs, RNNs, GANs, reinforcement learning
  • Why take it: Comprehensive, includes labs
  • Link: https://introtodeeplearning.com/

Healthcare-Specific AI

AI in Healthcare Specialization (Stanford)

  • Platform: Coursera
  • Level: Intermediate
  • Duration: 3 months
  • Topics: Medical imaging, EHR data, clinical trials, deployment
  • Why take it: Healthcare-focused, taught by Stanford faculty
  • Link: https://www.coursera.org/specializations/ai-healthcare

AI for Medicine Specialization (deeplearning.ai)

  • Instructor: Andrew Ng et al.
  • Platform: Coursera
  • Level: Intermediate
  • Duration: 3 months
  • Courses:
  1. AI for Medical Diagnosis
  2. AI for Medical Prognosis
  3. AI for Medical Treatment
  • Link: https://www.coursera.org/specializations/ai-for-medicine

MIT Critical Data: Secondary Analysis of Electronic Health Records

  • Platform: MIT OpenCourseWare
  • Level: Advanced
  • Duration: Self-paced
  • Topics: EHR data, MIMIC database, clinical prediction
  • Link: https://ocw.mit.edu/

Public Health and Epidemiology

Epidemiology in Public Health Practice (Johns Hopkins)

  • Platform: Coursera
  • Level: Beginner
  • Duration: 4 weeks
  • Topics: Study design, measures of disease, screening, causation
  • Why take it: Essential epidemiology foundation for AI applications
  • Link: https://www.coursera.org/specializations/professional-epidemiology

Epidemiology for Public Health (Imperial College London)

  • Platform: Coursera
  • Level: Intermediate
  • Duration: 6 months
  • Topics: Epidemiologic methods and public health applications
  • Link: https://www.coursera.org/specializations/public-health-epidemiology

Ethics and Fairness

Data Science Ethics (University of Michigan)

  • Platform: Coursera
  • Level: Beginner to intermediate
  • Duration: 4 weeks
  • Topics: Privacy, fairness, transparency, accountability
  • Why take it: Critical thinking about AI ethics
  • Link: https://www.coursera.org/learn/data-science-ethics

Exploring Fairness in Machine Learning for International Development (MIT)

  • Platform: MIT OpenCourseWare
  • Level: Advanced
  • Duration: Self-paced
  • Topics: Fairness definitions, bias mitigation, fairness-aware ML
  • Link: https://ocw.mit.edu/courses/res-ec-001-exploring-fairness-in-machine-learning-for-international-development-spring-2020/

Books

Technical Foundations

“Pattern Recognition and Machine Learning” by Christopher Bishop

  • Level: Advanced
  • Topics: Bayesian methods, neural networks, graphical models
  • Best for: Mathematical foundations, reference text
  • Why read it: Rigorous, standard graduate text

“Deep Learning” by Goodfellow, Bengio, and Courville

  • Level: Intermediate to advanced
  • Topics: Neural networks, optimization, CNNs, RNNs, regularization
  • Best for: Deep learning theory and practice
  • Why read it: Authoritative, free online
  • Link: https://www.deeplearningbook.org/

“Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron

  • Level: Beginner to intermediate
  • Topics: Practical ML, neural networks, implementation
  • Best for: Learning by doing, practical projects
  • Why read it: Code-heavy, practical examples

“Probabilistic Machine Learning” by Kevin Murphy

  • Level: Advanced
  • Topics: Probabilistic graphical models, Bayesian methods, deep learning
  • Best for: Rigorous treatment of ML from probabilistic perspective
  • Link: https://probml.github.io/pml-book/

Healthcare AI

“Machine Learning for Healthcare” edited by Ranganath, Perotte, Zitnik

  • Level: Advanced
  • Topics: Clinical prediction, imaging, EHR analysis, interpretability
  • Best for: Advanced research in healthcare ML
  • Why read it: Broad coverage of healthcare AI research

“Artificial Intelligence in Medicine” by Markus Wenzel

  • Level: Intermediate
  • Topics: Medical imaging, diagnosis, treatment planning, drug discovery
  • Best for: Overview of AI applications in medicine

“Clinical Prediction Models” by Ewout Steyerberg

  • Level: Intermediate to advanced
  • Topics: Risk prediction, model development, validation, updating
  • Best for: Rigorous approach to clinical prediction
  • Why read it: Standard reference for clinical prediction methodology

Ethics and Society

“Weapons of Math Destruction” by Cathy O’Neil

  • Level: General audience
  • Topics: Algorithmic bias, societal impact, fairness
  • Best for: Understanding societal implications
  • Why read it: Accessible, compelling examples, critical perspective

“Automating Inequality” by Virginia Eubanks

  • Level: General audience
  • Topics: Algorithms in social services, child welfare, healthcare
  • Best for: Case studies of algorithmic harm
  • Why read it: Real-world impact on vulnerable populations

“The Ethical Algorithm” by Kearns and Roth

  • Level: Intermediate
  • Topics: Fairness, privacy, game theory, algorithmic design
  • Best for: Technical approaches to ethical AI
  • Why read it: Bridges theory and practice

“Race After Technology” by Ruha Benjamin

  • Level: General audience
  • Topics: Race, technology, algorithmic bias, social justice
  • Best for: Critical race perspective on AI
  • Why read it: Essential perspective on tech and inequality

AI Safety and Risk

“Introduction to AI Safety, Ethics, and Society” by Dan Hendrycks

  • Level: Intermediate to advanced
  • Topics: Catastrophic AI risks, safety engineering, complex systems, machine ethics, AI governance
  • Best for: Foundational understanding of AI safety frameworks applicable to public health AI deployment
  • Why read it: Open access, endorsed by Yoshua Bengio, covers bioterrorism risk and evaluation methodology
  • Link: https://www.aisafetybook.com/

Public Health

“Modern Epidemiology” by Rothman, Greenland, Lash

  • Level: Advanced
  • Topics: Causal inference, study design, bias, confounding
  • Best for: Rigorous epidemiologic foundation
  • Why read it: Standard graduate text, essential for healthcare AI

“Infectious Disease Epidemiology” by Nelson and Williams

  • Level: Intermediate
  • Topics: Disease transmission, outbreak investigation, surveillance
  • Best for: Understanding infectious disease dynamics

Academic Journals

AI and Machine Learning

Top-Tier General ML:

  • NeurIPS (Conference on Neural Information Processing Systems)
  • ICML (International Conference on Machine Learning)
  • ICLR (International Conference on Learning Representations)
  • JMLR (Journal of Machine Learning Research)
  • Machine Learning (Springer)

Access: Many papers are available as arXiv.org preprints

Healthcare AI

Clinical Journals Publishing AI Research:

  • The Lancet Digital Health

  • Focus: Digital health technologies, AI applications

  • Open access

  • Link: https://www.thelancet.com/journals/landig

  • npj Digital Medicine (Nature)

  • Focus: Digital health, AI, sensors, apps

  • Open access

  • Link: https://www.nature.com/npjdigitalmed/

  • Journal of the American Medical Informatics Association (JAMIA)

  • Focus: Health informatics, clinical decision support, AI

  • Link: https://academic.oup.com/jamia

  • NEJM AI (New England Journal of Medicine)

  • Focus: AI in medicine, launched 2024

  • High-impact clinical AI research

  • Link: https://ai.nejm.org/

Specialized Healthcare AI:

  • Radiology: Artificial Intelligence
    • Focus: AI in medical imaging
  • Journal of Medical Internet Research
    • Focus: Digital health, eHealth
  • IEEE Journal of Biomedical and Health Informatics
    • Focus: Biomedical informatics, signals

Public Health

  • American Journal of Public Health
  • American Journal of Epidemiology
  • Epidemiology
  • International Journal of Epidemiology
  • BMC Public Health

Ethics and Fairness

  • ACM Conference on Fairness, Accountability, and Transparency (FAccT)
    • Venue for research on algorithmic fairness, accountability, and transparency
    • Interdisciplinary: CS, law, social science
    • Proceedings open access
  • AI and Ethics (Springer)
  • Ethics and Information Technology

Datasets and Data Repositories

Clinical Datasets

MIMIC-III / MIMIC-IV

  • Content: ICU patient data (over 60,000 admissions)
  • Includes: Vitals, labs, medications, notes, outcomes
  • Access: Free after credentialing
  • Use cases: Clinical prediction, NLP, time-series analysis
  • Link: https://mimic.mit.edu/

eICU Collaborative Research Database

  • Content: Multi-center ICU data (over 200,000 admissions)
  • Access: Free after credentialing
  • Link: https://eicu-crd.mit.edu/

SEER (Surveillance, Epidemiology, and End Results)

  • Content: Cancer registry data
  • Access: Free, public
  • Link: https://seer.cancer.gov/

Medical Imaging

ChestX-ray14

  • Content: 112,000 chest X-rays with labels
  • Labels: 14 pathologies
  • Access: Free
  • Link: https://nihcc.app.box.com/v/ChestXray-NIHCC

CheXpert

  • Content: 224,000 chest X-rays
  • Labels: 14 conditions
  • Access: Free
  • Link: https://stanfordmlgroup.github.io/competitions/chexpert/

RSNA Pneumonia Detection Challenge

  • Content: 30,000 chest X-rays
  • Labels: Pneumonia bounding boxes
  • Access: Kaggle
  • Link: https://www.kaggle.com/c/rsna-pneumonia-detection-challenge

The Cancer Imaging Archive (TCIA)

  • Content: Cancer imaging across modalities
  • Access: Free
  • Link: https://www.cancerimagingarchive.net/

Public Health / Surveillance

CDC Data

  • FluView: Influenza surveillance
  • COVID Data Tracker: COVID-19 data
  • WONDER: Mortality, natality data
  • Link: https://data.cdc.gov/

WHO Global Health Observatory

  • Content: Global health statistics
  • Topics: Mortality, disease burden, risk factors
  • Link: https://www.who.int/data/gho

Johns Hopkins COVID-19 Data

  • Content: Global COVID-19 cases, deaths, vaccinations
  • Status: Historical repository, archived March 10, 2023; no longer updated
  • Link: https://github.com/CSSEGISandData/COVID-19

HHS AI Use Case Inventory

  • Content: Federal AI applications across CDC, FDA, CMS, NIH, and other HHS agencies
  • Includes: 271 use cases (FY2024), development stages, agency breakdown
  • Updated: Annually (mandated by Executive Order 13960)
  • Link: https://healthit.gov/artificial-intelligence/hhs-ai-use-case-inventory/
  • Data access: Use the current official inventory page; no stable current raw CSV export URL was verified for this edition

Synthetic / Benchmark

Synthea

  • Content: Synthetic patient generator
  • Creates: Synthetic EHR records
  • Use: Development, testing, education
  • Link: https://synthetichealth.github.io/synthea/

Tools and Software

Machine Learning Frameworks

Scikit-learn

  • Language: Python
  • Focus: Classical ML algorithms
  • Best for: Tabular data, rapid prototyping
  • Docs: https://scikit-learn.org/

PyTorch

  • Language: Python
  • Focus: Deep learning
  • Best for: Research, flexibility
  • Docs: https://pytorch.org/

TensorFlow / Keras

  • Language: Python
  • Focus: Deep learning
  • Best for: Production deployment
  • Docs: https://www.tensorflow.org/

XGBoost

  • Language: Python, R, others
  • Focus: Gradient boosting
  • Best for: Tabular data, competitions
  • Docs: https://xgboost.readthedocs.io/

Healthcare-Specific Libraries

lifelines

  • Focus: Survival analysis
  • Link: https://lifelines.readthedocs.io/

scikit-survival

  • Focus: Survival analysis with ML
  • Link: https://scikit-survival.readthedocs.io/

MNE-Python

  • Focus: EEG/MEG analysis
  • Link: https://mne.tools/

nibabel

  • Focus: Neuroimaging data (fMRI, MRI)
  • Link: https://nipy.org/nibabel/

Fairness and Explainability

Fairlearn (Microsoft)

  • Focus: Fairness assessment and mitigation
  • Link: https://fairlearn.org/

AI Fairness 360 (IBM)

  • Focus: Fairness metrics and algorithms
  • Link: https://github.com/Trusted-AI/AIF360

SHAP

  • Focus: Model explanations
  • Link: https://shap.readthedocs.io/

LIME

  • Focus: Local explanations
  • Link: https://github.com/marcotcr/lime

InterpretML (Microsoft)

  • Focus: Interpretable models (EBM)
  • Link: https://interpret.ml/

Deployment and MLOps

MLflow

  • Focus: ML lifecycle management
  • Link: https://mlflow.org/

Weights & Biases

  • Focus: Experiment tracking, collaboration
  • Link: https://wandb.ai/

BentoML

  • Focus: Model serving
  • Link: https://bentoml.com/

Communities and Forums

Online Communities

Reddit:

  • r/MachineLearning: ML research and news
  • r/datascience: Data science practice
  • r/healthinformatics: Healthcare informatics
  • r/publichealth: Public health discussions

Stack Overflow:

  • Tags: machine-learning, healthcare, scikit-learn, tensorflow

Cross Validated (stats.stackexchange.com):

  • Statistical questions, study design, interpretation

Professional Organizations

American Medical Informatics Association (AMIA)

  • Focus: Health informatics, clinical informatics
  • Benefits: Conferences, networking, journals
  • Link: https://amia.org/

Healthcare Information and Management Systems Society (HIMSS)

  • Focus: Health IT, digital health
  • Link: https://www.himss.org/

American Public Health Association (APHA)

  • Focus: Public health practice and research
  • Link: https://www.apha.org/

Society for Medical Decision Making (SMDM)

  • Focus: Clinical decision making, modeling
  • Link: https://smdm.org/

RAISE Consortium (Responsible AI for Social and Ethical Healthcare)

  • Focus: Ethical frameworks for clinical and public health AI, value alignment
  • Partners: Harvard Medical School, MaineHealth, Roux Institute at Northeastern
  • Key output: “Values In the Model” (VIM) framework for transparent disclosure of embedded values in AI systems (Goldberg et al., NEJM AI, 2026)
  • Link: Harvard Bioethics

Vital Strategies: Foundations & Futures

  • Focus: AI readiness assessment for public health systems, country-level adoption strategies
  • Scope: 63-country readiness framework across five dimensions: governance, data systems, connectivity, human capacity, and sustainable financing
  • Key output: AI Use-Case Prerequisites Matrix distinguishing minimum safety conditions from scale-critical foundations
  • Best for: Public health program leaders, government officials, and funders planning AI adoption in low- and middle-income countries
  • Link: Vital Strategies

Center for AI Safety (CAIS)

  • Focus: Reducing societal-scale risks from AI through research, field-building, and advocacy
  • Key outputs: WMDP benchmark for evaluating hazardous AI knowledge, AI safety textbook, Statement on AI Risk signed by leaders from OpenAI, Anthropic, and DeepMind
  • Relevance to public health: Biosecurity evaluation tools, safety engineering frameworks applicable to health AI deployment
  • Link: safe.ai

Conferences

AI/ML in Healthcare:

  • MLHC (Machine Learning for Healthcare): August
  • CHIL (Conference on Health, Inference, and Learning): April
  • AMIA Annual Symposium: November

General ML:

  • NeurIPS: December
  • ICML: July
  • ICLR: May

Public Health:

  • APHA Annual Meeting - November
  • Society for Epidemiologic Research - June
  • NACCHO360 - July, with practitioner-oriented local health department programming such as the 2026 AI for Public Health track

Staying Current

Newsletters

The Batch (deeplearning.ai)

  • Weekly AI news curated by Andrew Ng
  • Link: https://www.deeplearning.ai/the-batch/

Import AI

  • Weekly AI research highlights by Jack Clark
  • Link: https://jack-clark.net/

AI Ethics Weekly

  • Weekly AI ethics news and research
  • Link: https://lighthouse3.com/newsletter/

Blogs

Distill.pub (archive)

  • Interactive ML explanations
  • On hiatus since 2021; archive remains available
  • Link: https://distill.pub/

Towards Data Science

  • Online publication on data science
  • Practical tutorials and case studies

Google Research Blog

  • Google AI research updates
  • Link: https://blog.research.google/

OpenAI Blog

  • OpenAI research and developments
  • Link: https://openai.com/blog/

DeepMind Blog

  • DeepMind research highlights
  • Link: https://www.deepmind.com/blog

Podcasts

The TWIML AI Podcast

  • Interviews with AI researchers and practitioners
  • Link: https://twimlai.com/

Data Skeptic

  • Data science, statistics, ML topics
  • Link: https://dataskeptic.com/

Linear Digressions (archive)

  • Machine learning explained accessibly; ended July 2020
  • Link: https://lineardigressions.com/

Practical Skill Development

Kaggle

Healthcare Competitions:

  • RSNA Pneumonia Detection
  • Diabetic Retinopathy Detection
  • SIIM-ACR Pneumothorax Segmentation

Learn: Kaggle Learn micro-courses on ML, deep learning, ethics

DataCamp / Coursera Projects

Guided Projects:

  • Clinical data analysis
  • Medical image classification
  • Survival analysis
  • Time series forecasting

Advanced Topics

Causal Inference

“The Book of Why” by Judea Pearl

  • Accessible introduction to causality
  • Why the distinction between correlation and causation matters for AI

“Causal Inference: What If” by Hernán and Robins

  • Rigorous causal inference methods
  • Free online: https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/

Course: Introduction to Causal Inference (Brady Neal)

  • Free online course
  • Link: https://www.bradyneal.com/causal-inference-course

Survival Analysis

“Survival Analysis: A Self-Learning Text” by Kleinbaum and Klein

  • Accessible introduction
  • Clinical focus

“Modeling Survival Data” by Therneau and Grambsch

  • Advanced methods, R focus

Time Series

“Forecasting: Principles and Practice” by Hyndman and Athanasopoulos

  • Comprehensive forecasting methods
  • Free online: https://otexts.com/fpp3/

“Deep Learning for Time Series Forecasting” by Jason Brownlee

  • Practical guide to DL for time series

Privacy-Preserving ML

Differential Privacy Book (Dwork and Roth)

  • Foundational text on differential privacy
  • Link: https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf

Federated Learning Tutorial

  • Distributed ML preserving privacy
  • Link: https://federated.withgoogle.com/

Career Development

Certifications

Google Professional ML Engineer

  • Cloud-based ML deployment
  • Link: https://cloud.google.com/certification/machine-learning-engineer

AWS Certified Machine Learning - Specialty

  • ML on AWS platform
  • Link: https://aws.amazon.com/certification/certified-machine-learning-specialty/

Clinical Informatics Board Certification (ABPM)

  • For physicians interested in informatics
  • Link: https://www.abpm.org/

Job Boards

  • AI/ML Healthcare Jobs: https://www.aijobs.com/
  • Health Informatics Jobs: https://jobmine.himss.org/
  • Academic Positions: https://academicjobsonline.org/

Conclusion

AI methods and their public health applications change quickly. Practitioners stay current by:

  • Following 2–3 key journals in your area
  • Subscribing to 1–2 newsletters
  • Attending 1–2 conferences annually
  • Practicing on real datasets
  • Engaging with online communities
  • Contributing to open-source projects

Pick a project and learn by building it.

Start with one resource from each category that matches your current level and interests.