This study examines the most prominent artificial intelligence (AI) studies in health and life sciences during the last three years, highlighting AI's expanding relevance in these domains. We selected and evaluated the top 50 referenced publications on AI in biomedicine, indicating major trends and theme classifications such as Drug Development, Real- World Clinical Implementation, and Ethical and Regulatory Aspects, among others. Our findings show a strong emphasis on AI applications in clinical contexts, particularly diagnostics, telemedicine, and medical education, which has been pushed by the COVID-19 pandemic. The introduction of AlphaFold represented a watershed moment in protein structure prediction, sparking a chain reaction of related research and indicating a broader trend toward AI-driven methodologies in biological research. The paper emphasizes AI's critical role in illness subtyping and patient stratification, which facilitate more customized medical tactics, namely in parsing complicated genomic and proteomic data, which improves our ability to unravel complex, inter-related chemical processes. As artificial intelligence balances rapid technological developments, ethical stewardship and regulatory monitoring will be critical for its long-term.