No two patients are alike: genetics, age, lifestyle and co‑morbidities all influence how diseases manifest and respond to therapy. Personalised medicine aims to tailor treatments to each individual, and artificial intelligence is key to realising this vision. By analysing genomic sequences, proteomics, metabolomics and clinical history, machine learning models can predict which drugs will work best for a specific patient or suggest dosage adjustments to minimise side effects. Recommendation engines originally developed for movies or shopping are repurposed to rank therapeutic options in oncology, cardiology and psychiatry.
The statistical backbone of personalised medicine echoes other predictive analytics. Classification models differentiate responders from non‑responders based on biomarkers; regression estimates optimal dosages or anticipates adverse reactions; clustering groups patients into molecular subtypes that may share pathways and drug sensitivities. Random forests, gradient boosting and deep neural networks integrate heterogeneous data types, from gene expression to imaging, uncovering complex interactions. Causal inference techniques help distinguish correlation from causation, ensuring recommendations are not confounded by hidden variables.
Applications are already emerging. In oncology, AI systems pair tumour mutations with targeted therapies and clinical trial options. Pharmacogenomics platforms analyse DNA variants affecting drug metabolism to guide antidepressant or anticoagulant prescriptions. Cardiologists use models that combine electrocardiogram patterns with demographic data to predict arrhythmia risk and tailor preventive treatment. By continuously learning from new patient outcomes, these systems refine their recommendations over time and adapt to evolving standards of care.
Nevertheless, personalised medicine must be implemented thoughtfully. Models trained on populations of European ancestry may not generalise to other ethnic groups, leading to inequitable care. Data privacy is paramount when dealing with genetic information. Patients and clinicians should understand that AI suggestions are probabilistic, not deterministic, and require clinical judgement. Transparent algorithms, diverse training datasets and rigorous validation are essential to avoid exacerbating disparities. With these safeguards, AI can help fulfil the promise of treatments as unique as the individuals receiving them.
