EC Clinical and Medical Case Reports

Artificial intelligence (AI) is an umbrella term that denotes the use of a computer to simulate intelligent behavior with minimal or no human involvement. AI implementation will significantly benefit the healthcare industry and people's general health. Although AI cannot wholly replace clinical judgment, it can help medical experts make better clinical decisions. AI unlocks new possibilities for learning, training, exploration, and development. Machine learning (ML) and deep learning (DL) are AI techniques for disease diagnosis, patient risk detection, and appropriate treatment options. Medical data from several sources, including ultrasonography, magnetic resonance imaging, mammography, genomics, computed tomography (CT), and positron emission tomography (PET), are essential to diagnose diseases using AI applications accurately. AI techniques diagnose major diseases in cancer, neurology, ophthalmology, gastroenterology, diabetology, and cardiology. AI has dramatically improved the hospital experience and accelerated patient preparation for home rehabilitation. Algorithm-based AI suggestions are highly systematic and eliminate human inconsistencies and errors. However, the sociological and ethical complexities of AI applications need more consideration, evidence of their economic and medical benefits, and the creation of multidisciplinary methods for their wider deployment. This review aims to investigate, summarize, and simplify AI's origin, development, types, uses (diagnosis and treatment), benefits (self-care, medical training, healthcare administration), limitations, and cost efficacy. It also discusses future perspectives and research on the use of AI in medical diagnosis and treatment.

Keywords: Algorithm-Based AI; Enhanced Hospital Experience; Enhancing Clinical Judgment; Diagnosis and Treatment; Machine Learning; Simulating Intelligent Behavior

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