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Uncertainty: Overview00:59

Uncertainty: Overview

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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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Propagation of Uncertainty from Random Error00:59

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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Propagation of Uncertainty from Systematic Error01:10

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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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Uncertainty in Measurement: Accuracy and Precision03:37

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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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Uncertainty in Measurement: Reading Instruments02:46

Uncertainty in Measurement: Reading Instruments

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Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
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Data Reporting and Recording01:24

Data Reporting and Recording

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Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
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Related Experiment Video

Updated: Jan 7, 2026

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
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Semantic Consistency-Based Uncertainty Quantification for Factuality in Radiology Report Generation.

Chenyu Wang1, Weichao Zhou2, Shantanu Ghosh1

  • 1Boston University.

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|January 5, 2026
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Summary
This summary is machine-generated.

This study introduces a new framework to quantify uncertainty in AI-generated radiology reports, improving factual accuracy by detecting and rejecting inaccurate information. The method enhances the reliability of automated radiology report generation (RRG).

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Natural Language Processing

Background:

  • Automated radiology report generation (RRG) shows promise in assisting radiologists.
  • Current generative models, including Vision Large Language Models (VLLMs), struggle with factual accuracy and hallucinations.
  • Ensuring the factual correctness of AI-generated reports is a critical challenge.

Purpose of the Study:

  • To introduce a novel Semantic Consistency-Based Uncertainty Quantification (SCUQ) framework for RRG.
  • To provide both report-level and sentence-level uncertainty measures.
  • To develop a plug-and-play module that enhances factual accuracy without altering existing models.

Main Methods:

  • Developed a Semantic Consistency-Based Uncertainty Quantification (SCUQ) framework.
  • Implemented a method that quantifies uncertainty without model modification or access to inner states.
  • Integrated the SCUQ framework as a plug-and-play module with state-of-the-art RRG models.

Main Results:

  • The SCUQ framework effectively detects hallucinations and improves the factual accuracy of generated reports.
  • Rejecting high-uncertainty reports improved factuality scores by 10% on the MIMIC-CXR dataset using the Radialog model.
  • Sentence-level uncertainty successfully flagged low-precision sentences with an 82.9% success rate.

Conclusions:

  • The SCUQ framework significantly enhances the factual accuracy of AI-generated radiology reports.
  • This plug-and-play approach offers a practical solution for improving the reliability of RRG systems.
  • The open-source implementation facilitates further research and adoption in medical AI.