Evidence map›Paper›PMID 41288691›Full record

ArticleEuropean journal of nuclear medicine and molecular imaging2026

Explainable AI in nuclear medicine.

Sune Holm, Daria Ferrara, Miriam Pepponi, Elisabetta Abenavoli, Armin Frille, Shaul Duke, Stefan Grünert, Marcus Hacker, Bengt Hennig, Swen Hesse and 8 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in European journal of nuclear medicine and molecular imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Sune HolmDepartment of Food and Resource Economics, University of Copenhagen, Copenhagen, Denmark. suneh@ifro.ku.dk.ORCID 0000-0002-3812-7942
Daria FerraraQuantitative Imaging and Medical Physics (QIMP) Team, Medical University of Vienna, Vienna, Austria.
Miriam PepponiNuclear Medicine, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.
Elisabetta AbenavoliNuclear Medicine, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.
Armin FrilleDepartment of Medicine II, Division of Respiratory Medicine, Leipzig University Medical Center, Leipzig, Germany.
Shaul DukeDepartment of Food and Resource Economics, University of Copenhagen, Copenhagen, Denmark.
Stefan GrünertDivision of Nuclear Medicine, Medical University of Vienna, Vienna, Austria.
Marcus HackerDivision of Nuclear Medicine, Medical University of Vienna, Vienna, Austria.
Bengt HennigDivision of Nuclear Medicine, Medical University of Vienna, Vienna, Austria.
Swen HesseDepartment of Nuclear Medicine, Leipzig University, Leipzig, Germany.
Lukas HofmannDepartment of Medicine II, Division of Respiratory Medicine, Leipzig University Medical Center, Leipzig, Germany.
Thomas B LundDepartment of Food and Resource Economics, University of Copenhagen, Copenhagen, Denmark.
Osama SabriDepartment of Nuclear Medicine, Leipzig University, Leipzig, Germany.
Peter SandøeDepartment of Food and Resource Economics, University of Copenhagen, Copenhagen, Denmark.
Roberto SciagràNuclear Medicine, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.
Lalith Kumar Shiyam SundarQuantitative Imaging and Medical Physics (QIMP) Team, Medical University of Vienna, Vienna, Austria.
Josef YuQuantitative Imaging and Medical Physics (QIMP) Team, Medical University of Vienna, Vienna, Austria.
Thomas BeyerQuantitative Imaging and Medical Physics (QIMP) Team, Medical University of Vienna, Vienna, Austria.

Funding

ERAPERMED ERAPERMED2021-324
6 · The paper itself

Abstract

purposeIn this short communication, we consider the need for explainable AI from the perspective of a large multi-disciplinary research project for predicting cachexia in cancer patients. MATERIALS AND

methodsIn a series of meetings, comprising expertise from medicine, data science, sociology, and philosophy, project participants discussed the need for explainability.

resultsWe distinguish between contexts in which a black box AI tool undertakes tasks that users can perform or validate themselves and contexts in which this is not the case.

conclusionWe conclude that explanations are likely required when a black box AI tool undertakes tasks that users cannot perform or validate themselves. If the user can verify outputs manually, documented reliability and accuracy may suffice, but explainability can still add value when outputs are uncertain or errors occur. More generally, close collaboration among physicians, AI developers, and other stakeholders is crucial to ensure that AI tools are trustworthy and useful in clinical practice.

Indexed as

Artificial IntelligenceCachexiaNuclear MedicineHumansNeoplasmsCachexiaClinical decision-makingExplainable AILung cancerMedical imagingTrustworthy AI

Identifiers

PMID41288691
PMCPMC12920353

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.