Trustworthy AI in Wound Care Documentation

Oskar Gustafsson, Jens Lundström, Ulf Johansson, John Pavia, Niclas Ståhl, Cecilia Sönströd, Ernst Ahlberg
Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications, PMLR 329:958-983, 2026.

Abstract

Efficient and trustworthy support for clinical documentation is becoming increasingly important as reporting requirements expand and EHR systems continue to increase in complexity. In wound care, where highly detailed structured assessments are a routine part of practice, there is a particular need for methods that assist clinicians through reliable recommendations. AI-based tools offer promising opportunities to reduce the documentation burden, but their value ultimately depends on the trustworthiness of the guidance they provide, as their recommendations feed directly into patient care and outcomes. In this work, we build on a unified uncertainty-quantification framework that combines Conformal Prediction with Venn–Abers predictors and extend it by integrating Shapley-value explanations into the same workflow. The resulting system produces prediction sets that narrow the label space for structured wound-care documentation while providing statistical coverage guarantees, calibrated inclusion probabilities, and interpretable explanations for the associated confidence. In practice, this kind of system could reduce documentation time and give clinicians a straightforward view of both the model’s uncertainty and the reasons behind its recommendations.

Cite this Paper

BibTeX
@InProceedings{pmlr-v329-gustafsson26b, title = {Trustworthy AI in Wound Care Documentation}, author = {Gustafsson, Oskar and Lundstr{\"o}m, Jens and Johansson, Ulf and Pavia, John and St{\aa}hl, Niclas and S{\"o}nstr{\"o}d, Cecilia and Ahlberg, Ernst}, booktitle = {Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications}, pages = {958--983}, year = {2026}, editor = {Ahlberg, Ernst and Johansson, Ulf and Boström, Henrik and Carlevaro, Alberto and Hallberg Szabadváry, Johan and Carlsson, Lars}, volume = {329}, series = {Proceedings of Machine Learning Research}, month = {02--04 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v329/main/assets/gustafsson26b/gustafsson26b.pdf}, url = {https://proceedings.mlr.press/v329/gustafsson26b.html}, abstract = {Efficient and trustworthy support for clinical documentation is becoming increasingly important as reporting requirements expand and EHR systems continue to increase in complexity. In wound care, where highly detailed structured assessments are a routine part of practice, there is a particular need for methods that assist clinicians through reliable recommendations. AI-based tools offer promising opportunities to reduce the documentation burden, but their value ultimately depends on the trustworthiness of the guidance they provide, as their recommendations feed directly into patient care and outcomes. In this work, we build on a unified uncertainty-quantification framework that combines Conformal Prediction with Venn–Abers predictors and extend it by integrating Shapley-value explanations into the same workflow. The resulting system produces prediction sets that narrow the label space for structured wound-care documentation while providing statistical coverage guarantees, calibrated inclusion probabilities, and interpretable explanations for the associated confidence. In practice, this kind of system could reduce documentation time and give clinicians a straightforward view of both the model’s uncertainty and the reasons behind its recommendations.} }
Endnote
%0 Conference Paper %T Trustworthy AI in Wound Care Documentation %A Oskar Gustafsson %A Jens Lundström %A Ulf Johansson %A John Pavia %A Niclas Ståhl %A Cecilia Sönströd %A Ernst Ahlberg %B Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications %C Proceedings of Machine Learning Research %D 2026 %E Ernst Ahlberg %E Ulf Johansson %E Henrik Boström %E Alberto Carlevaro %E Johan Hallberg Szabadváry %E Lars Carlsson %F pmlr-v329-gustafsson26b %I PMLR %P 958--983 %U https://proceedings.mlr.press/v329/gustafsson26b.html %V 329 %X Efficient and trustworthy support for clinical documentation is becoming increasingly important as reporting requirements expand and EHR systems continue to increase in complexity. In wound care, where highly detailed structured assessments are a routine part of practice, there is a particular need for methods that assist clinicians through reliable recommendations. AI-based tools offer promising opportunities to reduce the documentation burden, but their value ultimately depends on the trustworthiness of the guidance they provide, as their recommendations feed directly into patient care and outcomes. In this work, we build on a unified uncertainty-quantification framework that combines Conformal Prediction with Venn–Abers predictors and extend it by integrating Shapley-value explanations into the same workflow. The resulting system produces prediction sets that narrow the label space for structured wound-care documentation while providing statistical coverage guarantees, calibrated inclusion probabilities, and interpretable explanations for the associated confidence. In practice, this kind of system could reduce documentation time and give clinicians a straightforward view of both the model’s uncertainty and the reasons behind its recommendations.
APA
Gustafsson, O., Lundström, J., Johansson, U., Pavia, J., Ståhl, N., Sönströd, C. & Ahlberg, E.. (2026). Trustworthy AI in Wound Care Documentation. Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications, in Proceedings of Machine Learning Research 329:958-983 Available from https://proceedings.mlr.press/v329/gustafsson26b.html.

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