@article{scholars19103,
            note = {cited By 4; Conference of 8th International Congress on Information and Communication Technology, ICICT 2023 ; Conference Date: 20 February 2023 Through 23 February 2023; Conference Code:298799},
            year = {2023},
         journal = {Lecture Notes in Networks and Systems},
             doi = {10.1007/978-981-99-3091-3{$_8$}{$_9$}},
           pages = {1085--1104},
           title = {Artificial Intelligence Applications in Healthcare},
       publisher = {Springer Science and Business Media Deutschland GmbH},
          volume = {694 LN},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174709494&doi=10.1007\%2f978-981-99-3091-3\%5f89&partnerID=40&md5=850fbadec600a6795240c1a560e82812},
            isbn = {9789819930906},
            issn = {23673370},
        abstract = {The demand of healthcare services is rising, for e.g., Europe and the US are experiencing shortage of healthcare professionals. Artificial intelligence (AI) holds a promise to assist healthcare professionals in wide range of tasks. There is already a large amount of clinical and non-clinical evidence that AI algorithms can analyze both structured and unstructured clinical data (including images) data from electronic medical records (EMRs) with the characterization and prognosis of the disease. However, there is lack of study that provides an overview on what are the clinical applications that currently exist. This study provides an overview on the most powerful AI applications in healthcare, including those that are directly related to healthcare as well as those that are part of the healthcare value chain, such as drug development and ambient assisted living. Moreover, this article also provides an overview on the ethical concerns that may arise with the use of AI in healthcare domain. {\^A}{\copyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.},
        keywords = {Diagnosis; Industry 4.0; Medical computing; Medical imaging, Artificial intelligence algorithms; Digitalization; Employee shortage; Health care professionals; Healthcare; Healthcare services; Healthcare sustainability; Large amounts; Machine-learning; Pandemic, Machine learning},
          author = {Usmani, U. A. and Happonen, A. and Watada, J. and Khakurel, J.}
}