Traditional breast cancer diagnosis rests on four pillars: mammography, which images the breast with X-rays; ultrasound, which uses sound waves; MRI, which builds detailed images with magnetic fields; and biopsy, which removes and examines tissue samples. These methods are well proven — but they are resource-intensive, and their accuracy depends heavily on expert interpretation, which can create delays and errors.
AI is integrated on top of this toolbox. Image-analysis models read the same mammograms and other examinations and detect suspicious regions with high accuracy. Predictive models estimate cancer progression and treatment outcomes from patient data. Genomic analysis helps identify risk factors and tailor treatment to each tumour's biology.
What AI adds in the clinic
- A second opinion on every image, at machine speed
- Consistent analysis that does not tire across long shifts
- Quantified risk to help prioritise urgent cases
- Decision support, with the clinician always in control
AI performs best alongside experienced clinicians — augmentation, not replacement.