Modeling unknowns: A vision for uncertainty-aware machine learning in healthcare
Andrea Campagner1, Elia Mario Biganzoli2, Clara Balsano3
1IRCCS Ospedale Galeazzi-Sant'Ambrogio, Via Cristina Belgioioso, 173, Milano, 20157, Italy.
International Journal of Medical Informatics
|July 2, 2025
Summary
Machine learning in healthcare needs better uncertainty modeling. Addressing uncertainty is crucial for safe and trustworthy AI-driven clinical decisions.
Area of Science:
- Medical Artificial Intelligence (AI)
- Machine Learning (ML) in Healthcare
Background:
- The rapid integration of machine learning into healthcare is hampered by inadequate management of uncertainty in clinical decision-making.
- Uncertainty is often underrepresented in the design and evaluation of clinical AI systems, despite its critical importance.
Discussion:
- This special issue addresses the gap in uncertainty modeling for medical AI, presenting theoretical, methodological, and practical contributions.
- Fewer than 4% of studies explicitly address uncertainty, highlighting the need for alternative design principles like optimizing for clinical net benefit or incorporating explainability with confidence estimates.
Key Insights:
- Contributions include the RelAI system for real-time prediction reliability, studies on uncertainty communication's impact on clinical interpretation, and benchmarks for out-of-distribution detection.
- Causal reasoning and anomaly detection are highlighted for enhancing AI system robustness and accountability in healthcare.
Outlook:
- Representing, communicating, and operationalizing uncertainty are essential for clinical safety and building trust in AI-driven care.
- The special issue reframes uncertainty from a limitation to a foundational asset for the responsible deployment of ML in healthcare.
Related Concept Videos
00:59Uncertainty: Overview
995
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.
995
00:54Uncertainty: Confidence Intervals
4.8K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
4.8K
01:10Propagation of Uncertainty from Systematic Error
894
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...
894
01:29Issues And Trends In Healthcare Delivery System
5.9K
The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
5.9K
00:59Propagation of Uncertainty from Random Error
1.1K
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...
1.1K
03:37Uncertainty in Measurement: Accuracy and Precision
82.2K
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.
82.2K