This systematic review examines the current applications of artificial intelligence (AI) in healthcare delivery and evaluates the potential future prospects for AI integration in medical practice. A comprehensive literature search was conducted using PubMed, Scopus, and Web of Science databases for studies published between 2020 and 2024, with keywords including "artificial intelligence," "machine learning," "healthcare," "medical diagnosis," and "clinical decision support." A total of 127 peer-reviewed articles met the inclusion criteria. AI applications in healthcare demonstrate significant potential across multiple domains including diagnostic imaging (accuracy rates of 85-95%), drug discovery (reducing development time by 30-40%), personalized medicine, and clinical decision support systems. Machine learning algorithms show particular promise in radiology, pathology, and genomics. However, implementation challenges include data privacy concerns, regulatory barriers, and the need for clinician training. While AI technologies offer transformative potential for healthcare delivery, successful implementation requires addressing ethical considerations, ensuring data security, and maintaining the human element in patient care. Future research should focus on developing explainable AI systems and establishing comprehensive regulatory frameworks.
Almutairi et al. (2024) studied this question.