Artificial Intelligence in Healthcare: From Diagnostics to Decision-making
DOI:
https://doi.org/10.55938/wlp.v1i1.93Keywords:
Precision Medicine, Wearable Sensing Technologies, Personalized Therapies, EhrsAbstract
The COVID-19 pandemic has transformed healthcare making use of artificial intelligence (AI)-assisted healthcare and wearable technologies, however ethical and regulatory issues dominate. Data privacy and algorithm transparency are crucial, and governance frameworks should be developed for the implementation of AI in the healthcare sector. This paper presents a comprehensive examination of the AI environment in diagnostics, highlighting its potential for improvement in diagnoses and assist healthcare, as well as the challenges that must be worked out before it may be effectively implemented. AI contributes insights into mitigation, treatments, and patient satisfaction at various stages of medication, monitoring, and nursing. Advanced hospitals are incorporating AI technology to optimize precision and cost-effectiveness. Robotics supports the handicapped, and predictive analytics and healthcare management participate in medical decision-making. Network connectivity facilitates cost-effective worldwide healthcare access. The rapid advancement of machine learning algorithms, particularly deep learning, has an enormous impact on the healthcare business. This is primarily due to an increase in digital data and processing competence, which has been made possible by advances in hardware technology. AI is increasingly frequently utilized in healthcare for performing high-accuracy undertakings. This study explores the machine learning algorithms and methodologies utilized in healthcare decision making. In accordance with the enormous processing capacity offered by modern technology, neural network-based deep learning approaches have proven advantageous for computational biology. They are frequently employed owing to their outstanding predictive accuracy and dependability. The study highlights the reliance of computational biology and biomedicine-based decision making in healthcare on machine learning algorithms, which make them crucial for AI applications.
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