AI and Personalised Medicine: Treatment Fitted to the Person

People differ in genes, lifestyle and how they respond to treatment. AI helps find patterns that point to better-matched care.

General information only, not medical advice.

• Genomics: interpreting DNA variants that affect disease risk or drug response.
• Pharmacogenomics: predicting how someone may respond to certain medicines.
• Risk prediction: combining records, tests and lifestyle data to estimate who is likely to develop a condition.
• Imaging analysis: assisting radiologists and pathologists in spotting patterns.
• Treatment matching: suggesting options based on similar patients' outcomes.
• Wearables and monitoring: tracking trends and flagging changes for clinicians.
• Cancer care: matching tumour profiles to targeted therapies.

• Earlier detection and prevention.
• Fewer trial-and-error prescriptions.
• Better-targeted therapies.

• Privacy: genetic and health data is deeply personal and can affect relatives too.
• Bias: models trained mostly on certain populations may work worse for others.
• Over-interpretation: risk scores aren't certainties.
• Equity: advanced testing may not be available to everyone.
• Insurance and employment worries around genetic information in some places.
• Consent and control over how data is reused.

1. Ask what the test can and can't tell you.
2. Understand who will hold your data and how it may be shared.
3. Ask about counselling before and after.
4. Discuss results with a clinician rather than interpreting alone.
5. Be cautious about direct-to-consumer claims.

Personalised medicine is promising, but it depends on good data, fair access and strong privacy protections, with clinicians guiding decisions.