Impact of Artificial Intelligence in Healthcare Applications, Opportunities, Challenges, and Responsible Implementation

This review paper looks at how artificial intelligence (AI) is being tried across healthcare, from reading medical images and supporting clinical decisions to patient monitoring, telemedicine, drug discovery, and hospital administration. It describes the main AI technologies in use, where they can be applied, and the technical and ethical conditions needed for responsible use. We followed a narrative literature-review approach based on published reviews and guidance from the World Health Organization. The literature points to some possible gains: complex information can be analysed faster, early detection can be supported, routine work can be eased, and risk can be assessed in a more personalised way. Whether these gains actually appear depends on data quality, how representative the training data is, external validation, how well the tool fits into daily workflow, and continued clinical oversight. The main concerns are privacy and cybersecurity, algorithmic bias, limited explainability, performance drift, cost, and accountability. This paper does not report original clinical experiments or new performance measurements. We conclude that AI should be introduced for clearly defined tasks, tested in the setting where it will be used, monitored after deployment, and kept in a supporting role for qualified healthcare professionals.