Artificial Intelligence and Genomic Data Analysis: New Frontiers in Precision Medicine
Abstract
The rapid expansion of next-generation sequencing technologies has generated unprecedented volumes of genomic data; however, translating these data into reliable and clinically actionable insights remains a major challenge in precision medicine. Artificial intelligence (AI) has emerged as a key enabling technology across the genomic medicine pipeline, supporting variant detection, variant interpretation, polygenic risk prediction, disease subtyping, biomarker discovery and treatment-response modelling. This review provides a clinically oriented, pipeline-based synthesis of contemporary AI applications in genomic medicine. Major computational paradigms, including machine learning, deep learning, ensemble methods, multimodal AI, explainable AI frameworks and emerging foundation models, are discussed in the context of their contribution to genomic analysis and clinical decision support. Particular emphasis is placed on the factors that determine model robustness and clinical utility, including dataset composition, class imbalance, label noise, calibration, ancestry representation, distributional shift and external validation. Evidence from rare genetic disorders, cardiovascular genetics and precision oncology is examined to illustrate both successful translational applications and persistent barriers to implementation. The review further analyses common sources of failure in real-world genomic AI systems, including overfitting, limited transportability across populations and…
How strong is the evidence?
Evidence Hub reads this as a review, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
Read the paper
Related studies
- Active Learning during Federated Learning for Object Detection
- Fairness-aware federated learning
- Decentralized Federated Learning
- Building a Personally Identifiable Information Recognizer in a Privacy Preserved Manner Using Automated Annotation and Federated Learning
- Preservation of Patient Level Privacy: Federated Classification and Calibration Models
- Efficient explainable federated learning
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.