Rajaraman S, Zamzmi G, Yang F, Xue Z, Antani SK. Data Characterization for Reliable AI in Medicine.
RECENT TRENDS IN IMAGE PROCESSING AND PATTERN RECOGNITION : 5TH INTERNATIONAL CONFERENCE, RTIP2R 2022, KINGSVILLE, TX, USA, DECEMBER 01-02, 2022, REVISED SELECTED PAPERS. INTERNATIONAL CONFERENCE ON RECENT TRENDS IN IMAGE PROCESSING AND... 2023;
1704:3-11. [PMID:
36780238 PMCID:
PMC9912175 DOI:
10.1007/978-3-031-23599-3_1]
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Abstract
Research in Artificial Intelligence (AI)-based medical computer vision algorithms bear promises to improve disease screening, diagnosis, and subsequently patient care. However, these algorithms are highly impacted by the characteristics of the underlying data. In this work, we discuss various data characteristics, namely Volume, Veracity, Validity, Variety, and Velocity, that impact the design, reliability, and evolution of machine learning in medical computer vision. Further, we discuss each characteristic and the recent works conducted in our research lab that informed our understanding of the impact of these characteristics on the design of medical decision-making algorithms and outcome reliability.
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