A field guide to machine learning in healthcare
Tasks, data, evaluation, and deployment fundamentals for healthcare ML.
Articles on how machine learning is applied across healthcare, with an emphasis on clinical value, scientific evidence, and the constraints that make healthcare ML different from general-purpose computer vision and machine learning.
What makes healthcare ML different?
Calibration
In clinical settings, a model's probabilities often matter as much as its top-1 prediction. Miscalibrated confidence can drive unsafe decisions.
Grounding
Clinical outputs need to be grounded in patient-specific evidence and should avoid hallucinated rationales.
Traceability
Healthcare systems require traceability: which data were used, what model version, what preprocessing, and what the model "saw".
Privacy & Security
Health data is sensitive, regulated, and high-stakes. Training and deployment must account for privacy risks and governance.
Articles
Deep learning in medical imaging
Radiology, pathology, ophthalmology, and key papers in medical imaging AI.
Clinical NLP from notes to decision support
What works, what fails, and why in clinical natural language processing.
EHR time series and risk prediction
Sepsis, ICU deterioration, and model shift in electronic health records.
Privacy-preserving ML for healthcare
Federated learning, differential privacy, and threat models in healthcare ML.
Trustworthy ML in healthcare
Calibration, uncertainty, grounding, and traceability for clinical AI.
Future challenges and opportunities
Multimodal foundation models, regulation, and real-world evidence.