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SUMMARY:Measurements: The Key to Reliable AI in Healthcare - Francesco Lam
 onaca\, University of Calabria\, Italy
DTSTART:20250218T173000Z
DTEND:20250218T183000Z
UID:TALK228454@talks.cam.ac.uk
CONTACT:Antonella Iuliano
DESCRIPTION:The study of biological phenomena and physiological parameters
  relies on precise measurements. However\, many mistakenly believe that a 
 measurement is just a simple numerical value representing the ratio betwee
 n the quantity being measured and its unit. This is far from true! Measure
 ment is a complex process influenced not only by potential errors but also
  by multiple factors that make the "true" value of a biomedical parameter 
 inherently uncertain. \nRecognizing this uncertainty is essential for maki
 ng informed medical and scientific decisions\, especially when relying on 
 artificial intelligence (AI) for diagnostics and treatment recommendations
 .\nThe goal of this seminar is to introduce methods and approaches aligned
  with the UNI CEI ENV 13005 standard: "Guide to the Expression of Measurem
 ent Uncertainty." This framework enables the quantification of uncertainty
  in biomedical measurements\, ensuring a clearer understanding of the reli
 ability of medical data—an essential step when integrating AI-driven alg
 orithms in healthcare.\nQuantifying uncertainty is crucial for comparing d
 iagnostic results\, assessing data quality\, and ultimately making well-in
 formed healthcare decisions. When AI algorithms process medical data\, any
  inaccuracy in measurement can lead to misleading predictions\, incorrect 
 diagnoses\, or flawed treatment plans. \n
LOCATION: Lecture Theatre 2
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