Skip to main content

AI & Machine Learning

Why AI & ML matter in the NHS​

AI and ML already support:

  • Predicting A&E demand
  • Detecting disease in imaging
  • Automating clinical coding
  • Analysing patient feedback

This section is your jumping-off point to adopt these methods responsibly using open-source tools and safe AI assistants.

Python ML toolkit​

⚙️ scikit-learn

Classic ML algorithms: regression, classification, clustering.
Best for: Tabular NHS data.

🔦 XGBoost / LightGBM

Gradient boosting for structured datasets.
Best for: Predictive risk scores.

🧠 TensorFlow / Keras

Deep learning framework.
Best for: Imaging and NLP.

🔥 PyTorch

Research-friendly deep learning.
Best for: Custom neural networks.

📊 Prophet

Forecasting library by Facebook.
Best for: Time-series demand prediction.

Assistive AI tools​

💬 ChatGPT

Rapid prototyping, SQL generation, documentation, and NHS-friendly data pseudonymisation (with safe inputs).

🖋 Claude

Large-context analysis of policy docs, IG checklists, or research papers.

🧑‍💻 GitHub Copilot

In-IDE code suggestions for Python, SQL, and web development projects.

🔍 Perplexity

AI-assisted literature search — useful for clinical research and service design.

NHS-safe AI principles​

  • Never paste patient-identifiable data into third-party tools.
  • Prefer on-prem or approved cloud for sensitive workloads.
  • Use explainable AI methods (e.g., LIME, SHAP) for transparency.
  • Test models on representative NHS datasets to avoid bias.
  • Keep a model card describing purpose, data, and limitations.

Quickstart: “Hello NHS” ML example​

import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

# Example: Predict readmission risk from synthetic NHS dataset
df = pd.read_csv("synthetic_patients.csv")
X = df.drop("readmitted", axis=1)
y = df["readmitted"]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)
preds = model.predict(X_test)

print(classification_report(y_test, preds))

Try it:

Save this script as readmission.py.

Run pip install pandas scikit-learn.

Execute with python readmission.py.

What’s next?

You’ve completed the AI & ML stage. Keep momentum: