Artificial Intelligence in Life Sciences: Machine Learning Approaches for Disease Prediction, Biomarker Discovery, and Personalized Healthcare

Abstract

Artificial intelligence (AI), particularly machine learning (ML), is reshaping life sciences by enabling the analysis of complex biological and clinical data for disease prediction, biomarker discovery, and personalized healthcare. However, existing research remains fragmented across individual applications, while concerns regarding interpretability, data quality, bias, external validation, and clinical translation continue to limit the practical implementation of AI-driven approaches. This study qualitatively synthesizes recent scholarly literature to examine the applications, opportunities, and challenges of AI and ML across disease prediction, biomarker discovery, multi-omics and multimodal data integration, explainable AI (XAI), and personalized healthcare. An interpretivist qualitative documentary design was employed, drawing on peer-reviewed literature published primarily between 2020 and 2026 and analyzed through thematic analysis. Five interconnected themes emerged: AI-enabled disease prediction and early detection; AI-driven biomarker discovery and multi-omics integration; multimodal data analysis and personalized healthcare; explainability, transparency, and clinical trust; and barriers to responsible implementation, including data quality, algorithmic bias, privacy, limited external validation, and clinical integration. The synthesis indicates that AI can identify complex patterns across heterogeneous biological and clinical datasets and support more individualized approaches to risk assessment, diagnosis, and treatment. Nevertheless, computational performance alone does not guarantee biological validity or clinical utility. Effective translation requires representative datasets, rigorous external validation, clinically meaningful explanations, privacy protection, interdisciplinary collaboration, and appropriate human oversight. The study contributes an integrated perspective linking computational innovation with biological relevance and responsible clinical implementation, highlighting the conditions required for AI to move from predictive capability toward trustworthy and patient-centered healthcare.