Artificial Intelligence in the Healthcare Sector

Abstract

To an extent as never before in the history of medicine, computers are supporting human input, decision making and provision of data. In today' s healthcare sector and medical profession, AI, algorithms, robotics and big data are used to derive inferences for monitoring large-scale medical trends, detecting and measuring individual risks and chances based on data-driven estimations. A knowledge-intensive industry like the healthcare profession highly depends on data and analytics to improve therapies and practices. In recent years, there has been tremendous growth in the range of medical information collected, including clinical, genetic, behavioral and environmental data. Every day, healthcare professionals, biomedical researchers and patients produce vast amounts of data from an array of devices. These include electronic health records (EHRs), genome sequencing machines, high-resolution medical imaging, smartphone applications and ubiquitous sensing, as well as Internet of Things (IoT) devices that monitor patient health (OECD 2015). Through machine learning algorithms and unprecedented data storage and computational power, AI technologies have most advanced abilities to gain information, process it *The following article is based on a study initiated and curated by Dr. Dieter Feierabend at NEOS Lab and executed by Julia M. Puaschunder during Summer and Fall of 2019. Funding of the European Liberal Forum at the European Parliament is most gratefully acknowledged.

Development and Technology Application of Smart hospitals in China

“…Second, decision support applications will form a decision support system that integrates multi-source knowledge in the future. In terms of image data application, a decision support system covering multiple diseases, multiple sites and the whole process will be formed, and a decision support system of "Authoritative Knowledge + Clinical Experience" will be formed in the application of clinical text data [8]. Third, with the continuous progress and integration of multidisciplinary technology, executive applications will also further mature and develop in the direction of humanmachine collaboration.…”

Traditional, complementary, and integrative medicine and artificial intelligence: Novel opportunities in healthcare

“…2 , 3 AI, on the other hand, has garnered significant attention in recent years for its potential to revolutionize healthcare through data-driven decision-making, predictive analytics, and personalized treatment plans. 4 , 5 …”

Machine learning-based ABA treatment recommendation and personalization for autism spectrum disorder: an exploratory study

“…Further, the limited availability of licensed ABA clinicians impacts the quality of the baseline ABA program and its subsequent revisions during the patient treatment journey [ 19 ] limiting rehabilitation outcomes for ASD children. The above limitations underscore the importance of technological advancements, particularly in developing technology-driven personalized ABA recommendation systems (RS), enhancing therapist capacity [ 20 , 21 ] and intervention quality, disencumbering both caregivers and healthcare professionals of the challenges they face. Additionally, there is growing interest in applying ML algorithms to the field of brain disorders and mental health [ 22 , 23 ].…”