Uncertainty-Aware Remaining Lifespan Prediction from Images
Authors: name: Tristan Kenneweg, name: Philip Kenneweg, name: Barbara Hammer

Predicting mortality-related outcomes from images offers the prospect of
accessible, noninvasive, and scalable health screening. We present a method
that leverages pretrained vision transformer foundation models to estimate
remaining lifespan from facial and whole-body images, alongside robust
uncertainty quantification. We show that predictive uncertainty varies
systematically with the true remaining lifespan, and that this uncertainty can
be effectively modeled by learning a Gaussian distribution for each sample. Our
approach achieves state-of-the-art mean absolute error (MAE) of 7.48 years on
an established Dataset, and further improves to 4.79 and 5.07 years MAE on two
new, higher-quality datasets curated and published in this work. Importantly,
our models provide well-calibrated uncertainty estimates, as demonstrated by a
bucketed expected calibration error of 0.62 years. While not intended for
clinical deployment, these results highlight the potential of extracting
medically relevant signals from images. We make all code and datasets available
to facilitate further research.