Abstract:
Background: Artificial intelligence (AI) has emerged as a transformative technology in healthcare, basic medical science, and disease management. By enhancing the accuracy and speed of diagnostic processes, AI has significantly influenced traditional medical practice. Applications in radiology, pathology, endoscopy, ultrasound, and biochemi cal analyses have demonstrated improved precision and reduced physician’s workload.
Objective: This review aims to summarize the current applications of AI in medicine and highlight its future perspec tives in the clinical and paraclinical fields.
Methods: A comprehensive literature review was conducted using PubMed, ResearchGate, Web of Science, Scopus, and Google Scholar databases. Relevant studies addressing AI applications in healthcare, diagnostics, and treatment were critically analyzed.
Results: AI algorithms, including machine learning and deep learning models, have shown promising roles in health care management, medical education, early disease detection, personalized drug prescription, and paraclinical eval uations. Furthermore, AI has enhanced treatment strategies, particularly in postsurgical care and recovery moni toring. Current clinical implementations extend to diagnostic laboratories, endoscopy, pathology, radiology, and ultrasound, where AI improves both the precision and efficiency.
Conclusion: AI is rapidly becoming an integral component of modern healthcare and medical research, offering innovative solutions for diagnosis, treatment, and medical education. Despite its potential, successful integration requires recognition of its strengths and limitations, as well as careful consideration of ethical and legal challenges. Future efforts should prioritize the development of standard guidelines to ensure the safe, effective, and sustainable adoption of AI in medicine.
