AI Meets Big Data Smarter Healthcare Diagnostics
Keywords:
Integration of AI and Big Data for Smart Healthcare Diagnostics; Big Data; Artificial Intelligence; Decision Support Systems; Diagnostics; Healthcare Cloud; Machine Learning; Smart Healthcare.Abstract
The integration of artificial intelligence (AI) with big data is widely perceived as a promising direction for smart healthcare diagnostics. Various definitions and conjectures sustain the power of such an amalgamation. Data-driven medicine constitutes the theoretical foundation and encompasses both mainstream and alternative AI paradigms. To capture the potential, a data ecosystem addressing the data supply, an arsenal of AI algorithms appropriate for diagnostic testing, and a big data infrastructure capable of handling large volumes are outlined. The data ecosystem focuses on knowledge dissemination, reproducibility, and the avoidance of data leakage effects. Moreover, a supporting diagnostic AI lifecycle emphasizes AI validation and evaluation in terms of accuracy, bias, fairness, and generalizability in a health-related context.
The recent interweaving of AI and big data with clinical practice and healthcare activity has drawn considerable attention over the past few years, bottoming out at various facets. Despite an initial quest for solutions targeting real-world problems, several AI leaders have steered the discussion toward testing AI algorithms under their own terms, fostering some bewilderment. Data-driven medicine—medicine addressed towards its own data by applying data-centric solutions—has remained a shadowy concept because diagnostic testing or diagnostic examination is understood differently across the clinical landscape. Clinical practitioners specializing in a certain disease group commonly talk about diagnostic testing or core diagnostic tests for such diseases, whereas diagnostic pathology and forensic medicine are sometimes perceived as distinct specialties dealing with much less population-associated diseases.
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