AI and Personalized Medicine in Radiological Imaging

Personalized (precision) medicine tailors diagnosis and treatment to each patient's characteristics. AI extracts information from images that humans cannot easily see and combines it with clinical and genetic data.

Area	AI contribution
Radiomics	Thousands of quantitative features from a tumour linked to its biology
Radiogenomics	Predict gene mutations (e.g. EGFR, MGMT) from images, avoiding some biopsies
Treatment selection	Predict response to chemo, radio or immunotherapy
Radiotherapy planning	Auto-contouring and dose optimisation for each patient
Risk prediction	Personal risk of cancer or cardiac events (e.g. coronary calcium, breast density)
Dose personalisation	Adjust scan protocol and contrast to body size, lower radiation
Prognosis and follow-up	Track tumour change over time

Benefits

Better outcomes, fewer side effects, avoids unnecessary procedures, early detection.

Challenges

Data privacy, bias in training data, validation, cost, integration with clinical workflow.

Exam tip

Explain radiomics and radiogenomics with the multi-source data flow.