AI in Healthcare: Integrating Advanced Technologies with Traditional Practices for Enhanced Patient Care
Keywords:
Data privacy, algorithmic bias, transparency, accountability, ethical considerations, regulatory frameworks, personalized medicine, predictive analytics, preventive care, drug discovery, drug developmentAbstract
The field of healthcare is fast changing due to artificial intelligence (AI), which presents previously unheard-of possibilities for bettering patient care, expediting medical research, and boosting the delivery of healthcare as a whole. The many facets of AI's influence on healthcare are examined in this review, with particular attention paid to drug research and discovery, personalized medicine, predictive analytics and preventive care, and ethical and legal issues. AI has made tremendous advances in personalized medicine, enabling the creation of individualized treatment regimens based on a patient's unique genetic, environmental, and behavioral characteristics. AI-powered technologies improve treatment plans and make it easier to identify genetic markers, improving the accuracy and potency of medical therapies. AI's ability to analyze large datasets has transformed predictive analytics and preventive care by enabling precise health risk projections and early detection of possible problems. By encouraging ongoing monitoring and individualized preventive care, this proactive strategy enhances both operational effectiveness and health outcomes. The process of drug discovery and development has been made more efficient by AI-driven innovations that have improved target identification, optimized compound screening, and clinical trial management. These developments speed up the release of novel medicines by cutting development time and expenses and improving the chance of a drug's beneficial effects. But there are also a lot of moral and legal issues with integrating AI in healthcare. To ensure the appropriate and fair use of AI technology, concerns including data privacy, algorithmic bias, transparency, accountability, and changing legislative frameworks need to be addressed. Sustaining patient confidence and attaining successful results depend heavily on implementing strong data protection, reducing biases, and encouraging openness.
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