AI in Precision Healthcare Diagnostics

Abstract

Artificial intelligence holds transformative potential for precision healthcare -- matching diagnostic accuracy, treatment selection, and prognostic estimation to individual patient characteristics rather than population averages. Yet the translation of AI diagnostic models from research benchmarks to clinical practice has been slower than anticipated, hampered by challenges in data heterogeneity, model generalisability, regulatory approval, and clinician trust. This study presents a controlled multi-site evaluation of six AI diagnostic approaches -- convolutional neural networks (DenseNet-121), vision transformers (ViT-B/16), multimodal fusion (imaging + clinical notes + lab values), federated learning across hospitals, few-shot transfer from large pre-trained models, and ensemble with uncertainty quantification - across four clinical diagnostic tasks: chest X-ray pathology detection (CheXpert, 224K images, 14 pathologies), retinal disease classification (OCTMNIST, 109K OCT images, 4 classes), skin lesion diagnosis (ISIC 2019, 25K dermoscopy images, 8 classes), and ECG arrhythmia classification (PTB-XL, 21K 12-lead ECGs, 5 superclasses). Models were trained at a primary site and evaluated at 4 external validation sites with different patient populations, imaging equipment, and clinical protocols. The multimodal fusion model achieved the highest mean AUC across tasks (0.924 +- 0.008) but showed the largest external validation drop (-4.8 AUC points). The ensemble with uncertainty quantification achieved the best calibration (ECE = 0.028 +- 0.004) and the smallest external drop (-2.2 points) by abstaining on 8.4% of cases where uncertainty exceeded a threshold. Federated learning across 4 sites achieved 96.4% of centralised performance while keeping patient data local. Dataset shift between sites (measured by domain distance) was the dominant predictor of external validation degradation (partial R2 = 0.62). A practical clinical AI deployment checklist addressing generalisability, calibration, uncertainty, and regulatory requirements is proposed.