Computer-Based Systems And Artificial Intelligence In Healthcare: A Review Of Applications, Challenges, And Future Directions

Abstract

In the healthcare industry, AI and computer-based systems promise to advance patient care, improve diagnostic precision, and facilitate improved clinical decision-making. In this review, the use of cutting-edge AI tools like machine learning, deep learning, and natural language processing, along with telemedicine systems, clinical decision support systems, electronic health records, and lab automation systems, is explored. Early identification of disease, optimized treatment, evidence-based processes, real-time analytics, and efficient data management can be performed using artificial intelligence techniques. Currently, medical imaging, early diagnosis, drug development, personalized medicine, robotic-assisted surgery, and predictive analytics are important applications. Combining the technologies of Artificial Intelligence, Cloud Computing, and Internet of Medical Things allows for intelligent service in healthcare and constant monitoring of patients. It still, however, has challenges to address, such as the lack of skilled professionals, the cost of implementation, privacy and security issues, connectivity, and data quality. There's also an ethical concern because there's a lack of transparency and algorithmic prejudice. These challenges must be tackled by bridging the gap between disciplines, implementing robust regulations, and continually advancing technology to ensure the successful application of AI in healthcare. The technology could revolutionize the industry.