AI-Driven Biomedical Literature Mining on Azure Cloud: Self-Supervised Machine Reading and Relation Extraction

Abstract

The biomedical research community faces a persistent and growing bottleneck: the exponential rise in scientific publications. With more than 4,000 new biomedical papers appearing daily, the ability of human experts to manually curate knowledge is increasingly outpaced by the volume of information. This delay hampers translational research and slows the application of discoveries to clinical decision-making. In this paper, we present the design and evaluation of self-supervised, transformer-based machine reading systems for biomedical literature mining. Our approach integrates domain-specific language models such as BioBERT [2] and PubMedBERT [3] with entity recognition frameworks, relation extraction strategies, semantic search pipelines, and cloud-native infrastructure on Microsoft Azure. By employing distant supervision [6] and human-in-the-loop feedback, we demonstrate measurable improvements in both recall and precision compared to keyword-based retrieval baselines. Experiments on PubMed and benchmark datasets including BC5CDR and BioRelEx [7] illustrate the feasibility of large-scale biomedical knowledge curation. Crucially, Azure cloud computing platforms provide elastic scaling, distributed training with DeepSpeed [10], integrated services like Azure Cognitive Search, and compliance with healthcare regulations such as HIPAA and GDPR, making them essential for operationalizing biomedical NLP pipelines in enterprise and clinical settings