Artificial Intelligent Medical Systems in Health Care Services

This paper will discuss the relationship between healthcare stakeholders and intelligent medical systems. It examines how intelligent systems might affect healthcare. The study was prompted by the interest and investment shown in intelligent agents like Siemens since their initial trial deployments in healthcare organizations, prior to physician feedback. Here we discuss the pros and cons of using intelligent medical systems, as well as some ethical issues. The sociotechnical implications of intelligent systems in healthcare are explored. The article also compares Convolutional Neural Networks to state-of-the-art approaches and discusses potential decision makers' roles in assessing medical personnel's attitudes toward intelligent systems prior to final deployment. Clinical decision support systems promise to help clinicians make individualized, valued, and effective decisions that benefit patients. To make neural networks predictive, the company creates information architectures for hospitals. To help a clinician make a diagnosis the platform collects and organizes data Limited Memory AI systems have memory so they can use past experiences to inform future decisions. Machine learning analysis of genetic data from large populations will allow for personalized nutrition recommendations and drug treatment refinements, increasing the likelihood of successful therapy for a variety of diseases. AI-optimized therapy recommendations reduce the risk of unintended patient consequences. Preventing unexpected drug interactions can help improve patient outcomes