Artificial Intelligence in Healthcare: A Review
TL;DR
The reviewed literature indicates that machine learning, deep learning, explainable AI, and human–AI collaboration can improve diagnostic accuracy, efficiency, and patient-centered care, but challenges related to data privacy, algorithmic bias, explainability, cybersecurity, clinical validation, regulation, and unequal access continue to restrict large-scale implementation.
Abstract
Artificial intelligence (AI) is becoming an important technology in modern healthcare because of its ability to analyze large volumes of clinical, biomedical, and patient-generated data. AI-based systems are being applied in disease diagnosis, medical imaging, drug discovery, personalized medicine, clinical decision support, remote monitoring, and healthcare administration. This review examines recent trends in the use of AI in healthcare by analyzing six influential studies published between 2017 and 2024. The reviewed literature indicates that machine learning, deep learning, explainable AI, and human–AI collaboration can improve diagnostic accuracy, efficiency, and patient-centered care. However, challenges related to data privacy, algorithmic bias, explainability, cybersecurity, clinical validation, regulation, and unequal access continue to restrict large-scale implementation. The review concludes that AI should be implemented as a supportive tool under appropriate clinical supervision, ethical standards, and regulatory frameworks.