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In what ways does AI help personalize treatment plans?

AI enables precision medicine by matching treatments to individual patient characteristics. Genomic analysis uses machine learning to identify genetic variants influencing drug response, disease risk, and prognosis. Cancer treatment selection employs AI analyzing tumor genomics to recommend targeted therapies most likely to succeed based on specific mutations—increasing response rates from 30-40% (trial-and-error approaches) to 60-80% (matched therapies). Medication optimization considers patient-specific factors: genetics (pharmacogenomics predicting metabolism rates), comorbidities, current medications, allergies, weight, kidney function, and liver function to determine optimal drug selection and dosing. AI predicts which patients will experience adverse effects, enabling safer prescribing. Treatment response prediction analyzes clinical data, biomarkers, and imaging to forecast which interventions will succeed for individual patients. This avoids ineffective treatments, reducing trial-and-error cycles, side effects, and costs. Diabetes management AI personalizes insulin dosing based on continuous glucose data, activity patterns, and meal composition. Risk stratification identifies patients needing aggressive intervention vs. those safe with conservative management. Surgical planning uses AI on pre-operative imaging to customize procedural approaches. Recovery prediction models inform rehabilitation intensity and duration. The fundamental shift: from population-based protocols ('most patients respond to treatment X') to individualized plans ('your specific characteristics suggest treatment Y'). This precision approach improves outcomes while reducing unnecessary treatments and side effects.