Revealing AI Innovations in Medicine: A Latent Dirichlet Allocation Approach

Abstract

Healthcare institutions have been revolutionized by artificial intelligence (AI) because it enables diagnostic imaging and personalized pharmaceuticals and medicine creation alongside predictive analytics. Medical researchers face challenges understanding vast medical research databases that hinder their ability to discover new research trends and breakthroughs. This research studies the application of Latent Dirichlet Allocation (LDA) for powerful topic modelling which reveals underlying patterns in AI-driven medical research data. Applying LDA across a substantial healthcare research database reveals important themes alongside emerging topics and knowledge deficiencies within the medical field. Our research demonstrates that LDA enables researchers to locate the leading AI healthcare technologies which directs ongoing research projects and influences clinical application development. This work shows that LDA provides a valuable tool which strengthens healthcare research through better decision-making and speeds up AI medicine developments.