Artificial Intelligence (AI) is transforming healthcare by improving patient care, diagnosis, treatment, and clinical laboratory testing. This review explores the current state of AI in clinical practice, its potential applications, challenges, and future directions. AI has the potential to enhance disease diagnosis, treatment recommendations, and patient engagement by leveraging large datasets and identifying patterns. It can increase accuracy, reduce costs, and save time while minimizing human errors. AI can revolutionize personalized medicine, optimize medication dosages, enhance population health management, establish guidelines, provide virtual health assistants, support mental health care, improve patient education, and influence patient-physician trust. AI is used in healthcare settings to develop diagnostic tools and personalized treatment plans. It has been applied in various areas such as disease diagnosis, treatment recommendations, and patient monitoring. AI can assist in decision-making, manage workflow, and automate tasks in a timely and cost-effective manner. Deep learning algorithms can detect patterns in large datasets, leading to breakthroughs in fields such as genomics and drug discovery. AI has been used in healthcare to develop diagnostic tools and personalized treatment plans. It has the potential to improve the accuracy of diagnoses, reduce the risk of human errors, and provide more accurate results in less time. AI can support medical decisions by providing real-time assistance and insights. Researchers continue exploring ways to use AI in medical diagnosis and treatment, such as analyzing medical images, X-rays, CT scans, and MRIs. AI has the potential to transform clinical laboratory testing by improving the accuracy, speed, and efficiency of laboratory processes. It can assist in choosing appropriate antibiotic treatment regimens, which is critical for achieving high cure rates for various infectious diseases. AI can also help in optimizing health care sources in the emergency department by predicting patient demand, optimizing therapy selection, and suggesting emergency department length of stay. AI has the potential to revolutionize genomic medicine by identifying genetic markers associated with increased susceptibility to specific diseases. It can predict phenotypes, or observable characteristics shaped by genes and environmental factors, and identify genetic variants associated with distinctive traits or pathologies. AI can also aid in the development of personalized treatment plans by analyzing complex datasets and predicting outcomes. AI can assist in dose optimization and adverse drug event prediction, offering significant benefits in enhancing patient safety and improving treatment outcomes. It can help in developing predictive models for drug dosing and adverse drug events. AI can also be used in therapeutic drug monitoring to optimize drug dosing in individual patients. AI can be used to optimize healthcare by improving the accuracy and efficiency of predictive models. It can analyze large amounts of data and identify patterns and relationships that may not be obvious to human analysts. AI can also automate specific public health management tasks, such as patient outreach and care coordination. AI is transforming how guidelines are established in various fields. In healthcare, guidelines usually take much time, from establishing the knowledge gap that needs to be fulfilled to publishing and disseminating these guidelines. AI can help identify newly published data based on data fromArtificial Intelligence (AI) is transforming healthcare by improving patient care, diagnosis, treatment, and clinical laboratory testing. This review explores the current state of AI in clinical practice, its potential applications, challenges, and future directions. AI has the potential to enhance disease diagnosis, treatment recommendations, and patient engagement by leveraging large datasets and identifying patterns. It can increase accuracy, reduce costs, and save time while minimizing human errors. AI can revolutionize personalized medicine, optimize medication dosages, enhance population health management, establish guidelines, provide virtual health assistants, support mental health care, improve patient education, and influence patient-physician trust. AI is used in healthcare settings to develop diagnostic tools and personalized treatment plans. It has been applied in various areas such as disease diagnosis, treatment recommendations, and patient monitoring. AI can assist in decision-making, manage workflow, and automate tasks in a timely and cost-effective manner. Deep learning algorithms can detect patterns in large datasets, leading to breakthroughs in fields such as genomics and drug discovery. AI has been used in healthcare to develop diagnostic tools and personalized treatment plans. It has the potential to improve the accuracy of diagnoses, reduce the risk of human errors, and provide more accurate results in less time. AI can support medical decisions by providing real-time assistance and insights. Researchers continue exploring ways to use AI in medical diagnosis and treatment, such as analyzing medical images, X-rays, CT scans, and MRIs. AI has the potential to transform clinical laboratory testing by improving the accuracy, speed, and efficiency of laboratory processes. It can assist in choosing appropriate antibiotic treatment regimens, which is critical for achieving high cure rates for various infectious diseases. AI can also help in optimizing health care sources in the emergency department by predicting patient demand, optimizing therapy selection, and suggesting emergency department length of stay. AI has the potential to revolutionize genomic medicine by identifying genetic markers associated with increased susceptibility to specific diseases. It can predict phenotypes, or observable characteristics shaped by genes and environmental factors, and identify genetic variants associated with distinctive traits or pathologies. AI can also aid in the development of personalized treatment plans by analyzing complex datasets and predicting outcomes. AI can assist in dose optimization and adverse drug event prediction, offering significant benefits in enhancing patient safety and improving treatment outcomes. It can help in developing predictive models for drug dosing and adverse drug events. AI can also be used in therapeutic drug monitoring to optimize drug dosing in individual patients. AI can be used to optimize healthcare by improving the accuracy and efficiency of predictive models. It can analyze large amounts of data and identify patterns and relationships that may not be obvious to human analysts. AI can also automate specific public health management tasks, such as patient outreach and care coordination. AI is transforming how guidelines are established in various fields. In healthcare, guidelines usually take much time, from establishing the knowledge gap that needs to be fulfilled to publishing and disseminating these guidelines. AI can help identify newly published data based on data from