ARTIFICIAL INTELLIGENCE IN BIOMEDICINE: CURRENT APPLICATIONS AND ETHICAL IMPLICATIONS

The integration of artificial intelligence (AI) into biomedical research and clinical practice has expanded rapidly over the past decade, profoundly influencing diagnostic strategies, therapeutic development, and the organization of healthcare systems. Current narrow AI approaches, including deep learning architectures and large language models (LLMs), have demonstrated substantial utility in the analysis of large-scale biomedical datasets, automated interpretation of medical images, synthesis of scientific literature, and prediction of clinical outcomes. These technologies have contributed to improved efficiency, enhanced decision support, and accelerated translational research. 

Beyond these established applications, artificial general intelligence (AGI) has emerged as a conceptual and technological frontier, attracting growing scientific interest due to its theoretical capacity to perform complex cognitive tasks autonomously across multiple domains. Unlike task-specific AI systems, AGI is envisioned as capable of integrating heterogeneous information, transferring knowledge between contexts, and adapting dynamically to novel problems. Such capabilities raise fundamental questions regarding the reliability, transparency, interpretability, and accountability of AI-driven decision-making in sensitive biomedical and clinical environments. 

This review provides a comprehensive and up-to-date overview of contemporary AI technologies applied to biomedicine, with particular emphasis on their methodological foundations and practical implications. It further examines the conceptual distinctions between narrow AI and AGI, highlighting the potential contributions of advanced AI systems to scientific discovery, personalized medicine, and integrated clinical care. In parallel, the review critically addresses key ethical, regulatory, and societal challenges, including data privacy, algorithmic bias, clinical responsibility, and equitable access to AI-enabled healthcare. 

 In summary, artificial intelligence constitutes a major driver of innovation in biomedicine, offering unprecedented opportunities to enhance research and patient care. However, its sustainable and ethical integration into healthcare systems requires not only technological advancement but also rigorous evaluation, transparent governance frameworks, and close collaboration among clinicians, researchers, engineers, and policymakers.

Keywords: Artificial intelligence; Artificial general intelligence; Machine learning; Deep learning; Ethical issues; Translational medicine.