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Artificial intelligence utilises massive amounts of data to help with predicting illness, prevention, and diagnosis, as well as patient monitoring. In obstetrics, artificial intelligence is utilized in magnetic resonance imaging, ultrasound, and foetal cardiotocography. AI contributes in the resolution of a variety of obstetrical diagnostic issues.
In the healthcare industry, health informatics has provided such technological solutions as telemedicine, surgical robots, electronic health records (EHR), Picture Archiving and Communication Systems (PACS), and decision support, artificial intelligence, and machine learning innovations including IBM's Watson and Google's DeepMind platform.
The ITU-WHO Focus Group on Artificial Intelligence for Health (AI for Health) is an inter-agency collaboration between the World Health Organization and the ITU, which created a benchmarking framework to assess the accuracy of AI in health. [1] [2]
Risk Management and Healthcare Policy. 17: 1339– 1348. doi: 10.2147/RMHP.S461562. PMC 11127648. PMID 38799612. Liu, Feng; Ju, Qianqian; Zheng, Qijian; Peng, Yujia (2024). "Artificial intelligence in mental health: innovations brought by artificial intelligence techniques in stress detection and interventions of building resilience".
Digital pathology is a major part of pathology informatics, and encompasses topics including slide scanning, digital imaging, image analysis and telepathology.. Digital pathology is a sub-field of pathology that focuses on managing and analyzing information generated from digitized specimen slides.
Healthcare quality and safety require that the right information be available at the right time to support patient care and health system management decisions. Gaining consensus on essential data content and documentation standards is a necessary prerequisite for high-quality data in the interconnected healthcare system of the future.
The biggest benefit of the digital twin on the healthcare industry is the fact that healthcare can be tailored to anticipate on the responses of individual patients. Digital twins will not only lead to better resolutions when defining the health of an individual patient but also change the expected image of a healthy patient.
Healthcare information technology can also result in iatrogenesis if design and engineering are substandard, as illustrated in a 14-part detailed analysis done at the University of Sydney. [40] Numerous examples of bias introduced by artificial intelligence (AI) have been cited as the use of AI-assisted healthcare increases. See Algorithmic bias.