Leveraging AI, ML, and Big Data for Precision Patient Care in Modern Healthcare Systems

Abstract

Healthcare tasks are often complex and data-driven, with involved agents that observe one another over time to perform a wide range of activities with evolving roles. Low-power sensors generate and monitor abundant data, which increases the complexity of these tasks. Recent advances in Artificial Intelligence (AI) are tailored to support the realization of advanced services in smart healthcare systems built around ambient, human-centric intelligent environments. Many scientific and engineering challenges arise and need to be addressed to develop AI algorithms for precise event detection, data modeling and interpretation, privacy protection, and human-agent collaboration. Corresponding applications in various healthcare domains are discussed. In conclusion, open challenges motivate further research on smart healthcare systems.

New technologies are quickly changing every aspect of our lives. With the fast-paced advances of artificial intelligence (AI), important data privacy issues arise, especially in health care systems. Both practitioners and researchers are keenly aware of the security and privacy issues that big data processing incurs. To compensate for the low-data facility in new data-sensitive environments, more personal data should be shared. Real-time and well-distributed datasets for analytics in AI applications are needed but challenges arise from protecting sensitive data legitimately. Essential information governance and engineering solutions should be proposed to regulate the sacrifice between data privacy and data utility, to create trustworthy, transparent, and fair information governance paradigms for AI-based intelligent health care systems. Recent advances in privacy-preserving information governance will be highlighted stage-by-stage from data collection to cleaning, processing, presenting, and clinical applications.