BackgroundArtificial intelligence (AI)–based technologies develop rapidly and have myriad applications in medicine and health care. However, there is a lack of comprehensive reporting on the productivity, workflow, topics, and research landscape of AI in this field.ObjectiveThis study aimed to evaluate the global development of scientific publications and constructed interdisciplinary research topics on the theory and practice of AI in medicine from 1977 to 2018.MethodsWe obtained bibliographic data and abstract contents of publications published between 1977 and 2018 from the Web of Science database. A total of 27,451 eligible articles were analyzed. Research topics were classified by latent Dirichlet allocation, and principal component analysis was used to identify the construct of the research landscape.ResultsThe applications of AI have mainly impacted clinical settings (enhanced prognosis and diagnosis, robot-assisted surgery, and rehabilitation), data science and precision medicine (collecting individual data for precision medicine), and policy making (raising ethical and legal issues, especially regarding privacy and confidentiality of data). However, AI applications have not been commonly used in resource-poor settings due to the limit in infrastructure and human resources.ConclusionsThe application of AI in medicine has grown rapidly and focuses on three leading platforms: clinical practices, clinical material, and policies. AI might be one of the methods to narrow down the inequality in health care and medicine between developing and developed countries. Technology transfer and support from developed countries are essential measures for the advancement of AI application in health care in developing countries.

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In this paper novel cloud based fleet analytics was utilized for advanced asset management. The target was to optimize the maintenance and testing procedures of the secondary system in primary distribution substations. In the case study a series of 3007 different fault cases were automatically analysed in the cloud environment, gathered from 51 substation from the period of 18 months, containing both SCADA events and disturbance records. The result of the analysis was that 68% of the required secondary testing was actually tested already during the normal operation of the network, leaving only 32% that still need dedicated manual testing.