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Researcher profile

Marcus R. Makowski

Marcus R. Makowski contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Artificial IntelligenceBiological PhysicsComputation and LanguageComputer Visioneess.IVMachine Learningphysics.app-phphysics.med-ph

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Published work

3 published item(s)

preprint2026arXiv

Atomic Fact-Checking Increases Clinician Trust in Large Language Model Recommendations for Oncology Decision Support: A Randomized Controlled Trial

Question: Does atomic fact-checking, which decomposes AI treatment recommendations into individually verifiable claims linked to source guideline documents, increase clinician trust compared to traditional explainability approaches? Findings: In this randomized trial of 356 clinicians generating 7,476 trust ratings, atomic fact-checking produced a large effect on trust (Cohen's d = 0.94), increasing the proportion of clinicians expressing trust from 26.9% to 66.5%. Traditional transparency mechanisms showed a dose-response gradient of improvement over baseline (d = 0.25 to 0.50). Meaning: Decomposing AI recommendations into individually verifiable claims linked to source guidelines produces substantially higher clinician trust than traditional explainability approaches in high-stakes clinical decisions.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

3D U-Net for segmentation of COVID-19 associated pulmonary infiltrates using transfer learning: State-of-the-art results on affordable hardware

Segmentation of pulmonary infiltrates can help assess severity of COVID-19, but manual segmentation is labor and time-intensive. Using neural networks to segment pulmonary infiltrates would enable automation of this task. However, training a 3D U-Net from computed tomography (CT) data is time- and resource-intensive. In this work, we therefore developed and tested a solution on how transfer learning can be used to train state-of-the-art segmentation models on limited hardware and in shorter time. We use the recently published RSNA International COVID-19 Open Radiology Database (RICORD) to train a fully three-dimensional U-Net architecture using an 18-layer 3D ResNet, pretrained on the Kinetics-400 dataset as encoder. The generalization of the model was then tested on two openly available datasets of patients with COVID-19, who received chest CTs (Corona Cases and MosMed datasets). Our model performed comparable to previously published 3D U-Net architectures, achieving a mean Dice score of 0.679 on the tuning dataset, 0.648 on the Coronacases dataset and 0.405 on the MosMed dataset. Notably, these results were achieved with shorter training time on a single GPU with less memory available than the GPUs used in previous studies.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

3D-Imaging and Quantification of Magnetic Nanoparticle Uptake by Living Cells

Magnetic particle imaging (MPI) is a non-invasive, non-ionizing imaging technique for the visualization and quantification of magnetic nanoparticles (MNPs). The technique is especially suitable for cell imaging as it offers zero background contribution from the surrounding tissue, high sensitivity, and good spatial and temporal resolutions. Previous studies have demonstrated that the dynamic magnetic behaviour of MNPs changes during cellular binding and internalization. In this study, we demonstrate how this information is encoded in the MPI imaging signal. Through MPI imaging we are able to discriminate between free and cell-bound MNPs in reconstructed images. This technique was used to image and quantify the changes that occur in-vitro when free MNPs come into contact with cells and undergo cellularuptake over time. The quantitative MPI results were verified by a phenanthroline assay. The results showed a mean relative difference of 23.8% for the quantification of cell-bound MNPs. The insights gained from such observations provide a new window into fundamental biological processes and associated pathological changes occurring at a cellular level. This technique could therefore offer new opportunities for the early diagnosis of inflammatory diseases.

0 citations0 reviewsNo code linkedOpen access