The Building Blocks: Machine Learning and Deep Learning in Medicine

The Building Blocks: Machine Learning and Deep Learning in Medicine

Introduction

Many clinicians hear the terms machine learning (ML) and deep learning (DL) but find them abstract. At their core, these are computational methods that learn from data rather than relying on hand-coded rules. This post introduces the fundamentals, contrasts ML and DL, and illustrates how they underpin the medical AI systems clinicians encounter.

Machine Learning Basics

Machine learning refers to algorithms that improve performance by finding patterns in data. Common approaches include:

  • Supervised learning: model learns from labeled examples (e.g., “cancer” vs “no cancer” in pathology slides).
  • Unsupervised learning: model finds clusters or structures in unlabeled data (e.g., patient phenotyping).
  • Reinforcement learning: model learns by trial-and-error with feedback, used in robotic surgery or adaptive dosing.

Clinically, ML is used in tools like sepsis risk calculators, hospital length-of-stay prediction, and readmission scoring.

Deep Learning Basics

Deep learning is a subset of ML that uses artificial neural networks with many layers. It excels at handling complex, high-dimensional data such as medical images, ECG signals, or free-text notes.

Key applications include:

  • Radiology: CNNs detecting lung nodules in CT scans.
  • Dermatology: classifiers distinguishing malignant from benign lesions.
  • Pathology: slide-level detection of rare cell morphologies.
  • Speech/notes: natural language models flagging critical diagnoses in EHRs.

The AI Development Pipeline

Whether ML or DL, most AI tools follow a standard pipeline: data collection → preprocessing → model training → validation → deployment. Understanding this flow helps clinicians critically appraise new tools.

Data Preprocessing Model Training Validation Deployment

Clinical Relevance

For clinicians, the takeaway is that ML/DL are tools—not magic. Their outputs are only as good as the training data, the validation strategy, and how they are integrated into workflow. Critical appraisal of these aspects should be part of AI literacy.

Conclusion & Next Step

ML and DL are the backbone of clinical AI. By understanding their basic mechanics and development pipeline, healthcare professionals can better judge where AI may—or may not—add value. In the next post, we will explore AI in Diagnostics, where these methods are already transforming radiology, pathology, and dermatology.