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4,752,659 results
  • Artificial Intelligence for Surveillance in Public Health. [Journal Article]
    Yearb Med Inform 2019; 28(1):232-234Thiébaut R, Cossin S, Section Editors for the IMIA Yearbook Section on Public Health and Epidemiology Informatics
  • CONCLUSIONS: Surveillance is a central topic in public health informatics with the growing use of machine learning approaches in regards of the size and complexity of data. The evaluation of the approaches developed remains a serious challenge.
  • Clinical Research Informatics: Contributions from 2018. [Journal Article]
    Yearb Med Inform 2019; 28(1):203-205Daniel C, Kalra D, Section Editors for the IMIA Yearbook Section on Clinical Research Informatics
  • CONCLUSIONS: Research efforts in the CRI field currently focus on consolidating promises of early Distributed Research Networks aimed at maximizing the potential of large-scale, harmonized data from diverse, quickly developing digital sources. Data quality assessment methods and tools as well as privacy-enhancing techniques are major concerns. It is also notable that, following examples in the US and Asia, ambitious regional or national plans in Europe are launched that aim at developing big data and new artificial intelligence technologies to contribute to the understanding of health and diseases in whole populations and whole health systems, and returning actionable feedback loops to improve existing models of research and care. The use of "real-world" data is continuously increasing but the ultimate role of this data in clinical research remains to be determined.
  • Contributions from the 2018 Literature on Bioinformatics and Translational Informatics. [Journal Article]
    Yearb Med Inform 2019; 28(1):190-193Smaïl-Tabbone M, Rance B, Section Editors for the IMIA Yearbook Section on Bioinformatics and Translational Informatics
  • CONCLUSIONS: Thanks to the normalization of open data and open science practices, research in BTI continues to develop and mature. Noteworthy achievements are sophisticated applications of leading edge machine-learning methods dedicated to personalized medicine.
  • Artificial Intelligence in Health: New Opportunities, Challenges, and Practical Implications. [Journal Article]
    Yearb Med Inform 2019; 28(1):174-178Lau AYS, Staccini P, Section Editors for the IMIA Yearbook Section on Education and Consumer Health Informatics
  • CONCLUSIONS: While discussion remains active on how AI could 'revolutionise' healthcare delivery, there is a lack of direction and evidence on how AI could actually benefit patients and consumers. Perhaps instead of primarily focusing on data and algorithms, researchers should engage with patients and consumers early in the AI research agenda to ensure we are indeed asking the right questions, and that important use cases and critical contexts are identified together with patients and consumers. Without a clear understanding on why patients and consumers need AI in the first place, or how AI could support individuals with their healthcare needs, it is difficult to imagine the kinds of AI applications that would have meaningful and sustainable impact on individual daily lives.
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