Impact of Artificial Intelligence on Modern Healthcare Technology: Applications, Challenges and Future Perspectives

Artificial intelligence (AI) has moved from being primarily a subject of computational research to becoming an increasingly visible part of modern healthcare. The change has been driven by the rapid growth of digital health records, medical imaging, genomic information, wearable devices and other sources of health-related data. These developments have created opportunities for computational methods to assist with tasks that are difficult to perform efficiently through conventional approaches alone. Machine learning, deep learning, natural language processing, computer vision and, more recently, generative AI are now being investigated across a broad range of healthcare applications. The influence of AI is particularly evident in medical imaging, disease prediction, clinical decision support, drug discovery, pharmacovigilance, personalized medicine and remote patient monitoring. In pharmaceutical research, machine-learning approaches can be used to explore chemical and biological datasets, predict molecular properties and support the identification and optimization of potential drug candidates. However, the transition from a promising computational result to a clinically useful product is not straightforward. AI systems may perform differently when applied to populations, healthcare environments or datasets that differ from those used during development. Problems related to data quality, bias, interpretability, privacy and cybersecurity therefore remain important. The rapid emergence of generative AI has further broadened the discussion. Large language and multimodal models can summarize information, assist with documentation, support research activities and interact with users through natural language. At the same time, these systems may produce information that is incomplete or factually incorrect, and their apparently confident responses can make such errors difficult to recognize. The World Health Organization has consequently emphasized the importance of human rights, transparency, accountability, safety and appropriate governance in the development and use of AI for health. This review examines the current role of AI in modern healthcare technology, with particular attention to diagnosis, medical imaging, drug discovery, precision medicine, pharmaceutical care, telemedicine, wearable technologies, healthcare management and generative AI. The major limitations and ethical and regulatory concerns are also discussed. Rather than viewing AI as a substitute for healthcare professionals, the review considers a model in which computational systems and healthcare professionals work together, with clinical judgment and patient interests remaining central to healthcare decisions.