Healthcare AI Resources

Comprehensive catalog of resources for learning and practicing healthcare AI, including software tools, educational materials, research communities, and professional development opportunities.

Software Libraries and Tools

Clinical NLP

ClinicalBERT

Pre-trained BERT for clinical text (see Clinical NLP)
Trained on MIMIC-III clinical notes (newer variants can use MIMIC-IV with additional ED notes)
30-40% better than general BERT on medical tasks
HuggingFace Model

BioBERT

BERT pre-trained on biomedical literature (PubMed)
Good for medical entity recognition
GitHub

ScispaCy

spaCy models for biomedical text
NER, linking to medical ontologies
Supports UMLS, RxNorm, ICD-10
Documentation

MedCAT

Medical Concept Annotation Tool
Extract and link medical concepts
Works with clinical text in real-time
GitHub

EHR Processing

See EHR Structure for data format details.

MIMIC-Extract

Preprocessing pipeline for MIMIC-III/IV
Feature extraction, cohort selection
Handles temporal data, lab values, diagnoses
GitHub

FHIR (Fast Healthcare Interoperability Resources)

Standard for healthcare data exchange
JSON/XML formats for clinical data
Widely adopted in modern EHR systems
Official Site

Python FHIR Client

Parse and create FHIR resources
Handle patient, observation, medication resources
Documentation

Medical Imaging

See Medical Imaging with CNNs for deep learning applications.

MONAI (Medical Open Network for AI)

PyTorch-based framework for medical imaging
Pre-trained models, specialized transforms, losses
Handles 2D/3D medical images (DICOM, NIfTI)
Domain-specific augmentations
GitHub | Documentation

TorchIO

Medical image preprocessing and augmentation
Handles 3D medical images natively
Patch-based sampling for large volumes
Documentation

SimpleITK

Medical image registration and segmentation
Supports DICOM, NIfTI, MetaImage formats
Advanced registration algorithms
Website

PyRadiomics

Radiomic feature extraction
Texture, shape, intensity features
Standardized feature definitions
Documentation

Model Interpretability

Critical for clinical validation (see Clinical Interpretability).

SHAP (SHapley Additive exPlanations)

Model-agnostic interpretability
Game theory-based feature importance
Works with any model (tree, neural net, etc.)
GitHub

LIME (Local Interpretable Model-agnostic Explanations)

Explain individual predictions
Creates local linear approximations
Image, text, tabular data support
GitHub

Captum

PyTorch model interpretability
Attribution methods, feature importance
Integrated gradients, Grad-CAM, attention visualization
Documentation

Fairness and Bias

AIF360 (AI Fairness 360)

IBM toolkit for bias detection and mitigation
Metrics for fairness across demographics
Preprocessing, in-processing, post-processing techniques
GitHub

Fairlearn

Microsoft fairness toolkit
Metrics and mitigation algorithms
Group fairness, individual fairness
Documentation

Privacy and Security

PySyft

Privacy-preserving machine learning
Federated learning, differential privacy
Secure multi-party computation
GitHub

Opacus

Differential privacy for PyTorch
Training with privacy guarantees
DP-SGD implementation
GitHub

Online Courses

Free Courses

MIT 6.S897: Machine Learning for Healthcare (2019)

Comprehensive graduate-level course
EHR analysis, medical imaging, clinical NLP
Lectures on YouTube
Course Website

Stanford CS270: Machine Learning for Medical Imaging

Medical imaging focus
CNNs for radiology, pathology
Course Materials

Coursera: AI for Medicine Specialization

3-course series by deeplearning.ai
Medical diagnosis, prognosis, treatment
Hands-on with chest X-rays, EHR data
Coursera Link

Fast.ai: Practical Deep Learning for Coders

General deep learning with medical imaging examples
Transfer learning for medical applications
Practical, code-first approach
Course Website

Paid Courses/Bootcamps

Udacity: AI for Healthcare Nanodegree

3-month program
Projects with real medical data
2D/3D medical imaging, EHR, wearables
Udacity Link

DataCamp: Healthcare Analytics Tracks

Multiple tracks on healthcare data
Hands-on coding exercises
SQL for healthcare, clinical data analysis

Books

Healthcare AI

“Deep Medicine” by Eric Topol (2019)

Overview of AI in healthcare
For clinical and non-technical audiences
Future of AI in medicine
Covers diagnostic imaging, drug discovery, clinical workflows

“The AI Revolution in Medicine” by Peter Lee, Carey Goldberg, Isaac Kohane (2023)

GPT and large language models in healthcare
Practical implications for clinicians
Recent developments (Med-PaLM, GPT-4)

“Machine Learning for Healthcare” MIT Press (Coming)

Comprehensive textbook
Mathematical foundations
Healthcare-specific methods
EHR analysis, medical imaging, fairness

Medical Knowledge for ML Practitioners

“Pathophysiology of Disease” by McPhee & Hammer

Disease mechanisms
Essential for understanding clinical problems
Organ systems approach

“Current Medical Diagnosis & Treatment” (CMDT)

Clinical decision-making
Updated annually
Reference for disease management
Evidence-based guidelines

Medical Coding

See EHR Structure for coding system details.

“ICD-10-CM Official Guidelines”

Understanding diagnosis codes
70,000+ diagnosis codes
CDC Website

“CPT Professional Edition”

Procedure codes (10,000+ codes)
Published by American Medical Association
Essential for understanding clinical workflows

Research Papers

Foundational Papers

“ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission” (2019)

Alsentzer et al.
Pre-training BERT on clinical text
30-40% improvement over general BERT

“BEHRT: Transformer for Electronic Health Records” (2020)

Li et al.
BERT-style model for EHR event sequences
See Healthcare Foundation Models

“Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs” (2016)

Gulshan et al. (Google)
First major success of DL in medical imaging
Ophthalmologist-level performance

“Dermatologist-level classification of skin cancer with deep neural networks” (2017)

Esteva et al. (Stanford)
Nature paper, CNN for skin lesion classification
130,000 images across 2,000 diseases

Recent Healthcare AI

“Large Language Models Encode Clinical Knowledge” (2023)

Med-PaLM (Google)
LLMs for medical question answering
Achieves passing score on USMLE

“Towards Expert-Level Medical Question Answering with Large Language Models” (2023)

Med-PaLM 2
GPT-4 level performance on medical exams
Still requires clinical validation

“Foundation models for generalist medical artificial intelligence” (2023)

Multi-task medical AI
Unified models across imaging, text, structured data

Survey Papers

“A Survey on Deep Learning in Medical Image Analysis” (2017)

Litjens et al.
Comprehensive review of DL in medical imaging
300+ papers analyzed

“Machine Learning in Medicine” (2019)

Rajkomar et al.
Clinical applications and challenges
Deployment, fairness, interpretability

Publication Venues

Medical Journals (High Impact)

NEJM AI

New England Journal of Medicine AI
Top medical journal’s AI section
Clinical validation required
Website

Nature Medicine

High-impact medical research
Rigorous review process
Clinical + technical validation
Website

Lancet Digital Health

Digital health and AI
Clinical focus, global perspective
Website

JAMA Network Open

Open access from JAMA
Broad medical audience
Clinical AI applications
Website

Medical Informatics

Journal of the American Medical Informatics Association (JAMIA)

Top informatics journal
Clinical informatics and AI
EHR, clinical decision support
Website

Journal of Biomedical Informatics (JBI)

Biomedical data science
Methods and applications
Technical focus
Website

AMIA Annual Symposium

American Medical Informatics Association
Conference proceedings
Research and clinical tracks
Website

AI Conferences with Healthcare Tracks

NeurIPS (Neural Information Processing Systems)

ML for Health Workshop
Research-focused
State-of-the-art methods
Website

ICML (International Conference on Machine Learning)

Healthcare track and workshops
Fairness, interpretability emphasis
Website

ICLR (International Conference on Learning Representations)

Healthcare papers accepted
Representation learning for medical data
Website

Healthcare AI Conferences

MLHC (Machine Learning for Healthcare)

Dedicated healthcare ML conference
Research and Clinical abstract tracks
Emphasis on deployment and validation
Website

CHIL (Conference on Health, Inference, and Learning)

ACM conference on health AI
Causal inference, fairness, interpretability
Website

Domain-Specific

RSNA (Radiological Society of North America)

Machine Learning track
Annual meeting and journal
Medical imaging focus
Website

MICCAI (Medical Image Computing and Computer Assisted Intervention)

Top medical imaging conference
Technical focus, segmentation challenges
Website

Professional Communities

Online Communities

Healthcare ML Slack

Active community of practitioners
Job postings, paper discussions
Request invite online

r/HealthcareAI (Reddit)

Healthcare AI discussions
News and resources
Beginner-friendly
Subreddit

Healthcare NLP on Discourse

Clinical NLP discussions
Tool recommendations, problem-solving
Forum

Professional Organizations

American Medical Informatics Association (AMIA)

Professional membership ($175-275/year)
Annual symposium, working groups
Student discounts available
Website

Healthcare Information and Management Systems Society (HIMSS)

Healthcare IT professionals
Conferences, certifications
Global chapters
Website

ACM SIGHIT (Special Interest Group on Health Informatics)

Part of ACM
Academic and industry members
Website

Regulatory and Ethics Resources

Regulatory

FDA Software as Medical Device (SaMD)

Guidance documents for AI/ML in medical devices
Risk classification, clinical validation
FDA Website

HIPAA for Researchers

Privacy rule guidance
De-identification methods
HHS Website

EU AI Act

European AI regulation
High-risk AI systems include healthcare
Conformity assessment requirements
Official Text

Ethics and Fairness

See Clinical Interpretability for fairness requirements.

NIH Principles for Data Management and Sharing

Data sharing policies (effective 2023)
Repository requirements
NIH Website

World Health Organization (WHO): Ethics and Governance of AI for Health

Global perspective
Ethical principles (6 key principles)
Implementation guidance
WHO Document

The Belmont Report

Ethical principles for research with human subjects
Foundational document (respect, beneficence, justice)
HHS Website

Newsletters and Blogs

Newsletters

The Batch (deeplearning.ai)

Weekly AI news, healthcare updates
Andrew Ng’s newsletter
Accessible summaries
Subscribe

Import AI

Jack Clark’s weekly AI newsletter
Covers healthcare applications
Policy and technical developments
Subscribe

MLHC Newsletter

Machine Learning for Healthcare updates
Conference and paper announcements
Job postings

Blogs

Healthcare AI by Stanford AIMI

Stanford AI in Medicine & Imaging
Research updates, tool releases
Blog

Google Health AI

Research from Google Health
Medical imaging, EHR models
Blog

Microsoft Research Health

Healthcare AI research
Project Healthcare, Genomics
Blog

Podcasts

Healthcare AI Podcast (Practical AI)

Interviews with healthcare AI practitioners
Implementation challenges, success stories
Website

The TWIML AI Podcast

This Week in Machine Learning & AI
Frequent healthcare episodes
Technical depth
Website

Data Driven Healthcare

Healthcare data science
Clinical and operational analytics
Website

Career Resources

Job Boards

Healthcare AI Jobs

Curated healthcare AI positions
Startups and established companies

AI-Jobs.net

Healthcare AI filter
Remote and on-site positions
Website

LinkedIn

Search “Healthcare AI”, “Clinical ML”, “Medical AI”
Company pages: Health AI teams

Companies Hiring

Tech Giants:

Google Health
Microsoft Healthcare
Amazon HealthLake
Apple Health

Healthcare AI Startups:

PathAI (pathology)
Paige.AI (pathology)
Viz.ai (stroke detection)
Tempus (precision medicine)
Zebra Medical (imaging)
Insitro (drug discovery)

Health Systems:

Mayo Clinic AI Lab
Cleveland Clinic
UCSF Center for Digital Health Innovation
Mass General Brigham

Fellowships and Programs

Clinical Informatics Fellowship

ACGME-accredited programs (2-year)
For physicians pursuing informatics
Board certification pathway

Digital Health Postdocs

Many universities offer positions
Stanford, MIT, Harvard, UCSF, etc.
2-3 year research positions

Getting Started Checklist

For newcomers to healthcare AI:

✅ Complete an online course (MIT 6.S897 or Coursera AI for Medicine) ✅ Get PhysioNet credentialing for MIMIC access (see EHR Structure) ✅ Read 5 foundational papers in your area of interest ✅ Set up development environment (Python, PyTorch, healthcare libraries) ✅ Complete a tutorial with MIMIC data or medical imaging dataset ✅ Join Healthcare ML Slack and introduce yourself ✅ Identify 2-3 relevant publication venues ✅ Find potential collaborators (clinicians or ML researchers) ✅ Start a small project with public dataset

Next Steps

Bookmark this page for reference
Explore resources relevant to your focus area (EHR, imaging, NLP)
Join online communities for networking and learning
Stay updated with newsletters and conference announcements
Build portfolio with public healthcare datasets
Consider formal education (courses, bootcamps) if needed
Network at conferences (MLHC, AMIA, MICCAI)

Healthcare AI Overview - Start here for healthcare AI learning path
Healthcare EHR Learning Path - Structured curriculum
EHR Structure - MIMIC database access
Medical Imaging - Tools and techniques
Clinical Interpretability - Validation requirements

Last Updated: November 11, 2025 Contribute: Found a broken link or want to add a resource? Please submit an issue or pull request.