Tanja Jutzi

956 total citations
9 papers, 360 citations indexed

About

Tanja Jutzi is a scholar working on Oncology, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Tanja Jutzi has authored 9 papers receiving a total of 360 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Oncology, 5 papers in Artificial Intelligence and 3 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Tanja Jutzi's work include Cutaneous Melanoma Detection and Management (5 papers), AI in cancer detection (5 papers) and Radiomics and Machine Learning in Medical Imaging (3 papers). Tanja Jutzi is often cited by papers focused on Cutaneous Melanoma Detection and Management (5 papers), AI in cancer detection (5 papers) and Radiomics and Machine Learning in Medical Imaging (3 papers). Tanja Jutzi collaborates with scholars based in Germany, Austria and United States. Tanja Jutzi's co-authors include Eva Krieghoff‐Henning, Titus J. Brinker, Achim Hekler, Jochen Utikal, Christof von Kalle, Stefan Fröhling, Roman C. Maron, Max Schmitt, Tim Holland‐Letz and Hermann Brenner and has published in prestigious journals such as European Journal of Cancer, British Journal of Urology and Experimental Dermatology.

In The Last Decade

Tanja Jutzi

8 papers receiving 354 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Tanja Jutzi Germany	6	166	123	121	64	57	9	360
Saba Shafi United States	10	135 0.8×	126 1.0×	123 1.0×	50 0.8×	50 0.9×	42	387
Roman C. Maron Germany	8	159 1.0×	70 0.6×	131 1.1×	58 0.9×	27 0.5×	8	304
Max Schmitt Germany	11	269 1.6×	148 1.2×	276 2.3×	74 1.2×	50 0.9×	16	549
Erica Pollack United States	8	84 0.5×	355 2.9×	106 0.9×	68 1.1×	71 1.2×	20	562
Tobias Paul Seraphin Germany	8	102 0.6×	145 1.2×	76 0.6×	35 0.5×	83 1.5×	12	429
Nicholas Meti Canada	9	109 0.7×	91 0.7×	96 0.8×	19 0.3×	46 0.8×	21	289
Anna-Maria Larsson Sweden	8	162 1.0×	167 1.4×	155 1.3×	121 1.9×	68 1.2×	10	463
Amber Donnelly United States	12	124 0.7×	104 0.8×	202 1.7×	30 0.5×	44 0.8×	35	430
Emmanuel Agosto‐Arroyo United States	5	81 0.5×	148 1.2×	207 1.7×	48 0.8×	39 0.7×	11	327
Eric F. Glassy United States	9	48 0.3×	103 0.8×	164 1.4×	41 0.6×	45 0.8×	21	358

Countries citing papers authored by Tanja Jutzi

Since Specialization

Citations

This map shows the geographic impact of Tanja Jutzi's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Tanja Jutzi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tanja Jutzi more than expected).

Fields of papers citing papers by Tanja Jutzi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tanja Jutzi. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Tanja Jutzi. The network helps show where Tanja Jutzi may publish in the future.

Co-authorship network of co-authors of Tanja Jutzi

This figure shows the co-authorship network connecting the top 25 collaborators of Tanja Jutzi. A scholar is included among the top collaborators of Tanja Jutzi based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Tanja Jutzi. Tanja Jutzi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

9 of 9 papers shown

Jutzi, Tanja, Eva Krieghoff‐Henning, & Titus J. Brinker. (2022). Künstliche Intelligenz auf dem Vormarsch – Hohe Vorhersage-Genauigkeit bei der Früherkennung pigmentierter Melanome. Aktuelle Dermatologie. 48(3). 84–91.

Haggenmüller, Sarah, et al.. (2021). Digital Natives’ Preferences on Mobile Artificial Intelligence Apps for Skin Cancer Diagnostics: Survey Study. JMIR mhealth and uhealth. 9(8). e22909–e22909. 16 indexed citations

Kuntz, Sara, Eva Krieghoff‐Henning, Jakob Nikolas Kather, et al.. (2021). Gastrointestinal cancer classification and prognostication from histology using deep learning: Systematic review. European Journal of Cancer. 155. 200–215. 116 indexed citations

Kuntz, Sara, Julia Höhn, Tanja Jutzi, et al.. (2021). Deep learning can predict lymph node status directly from histology in colorectal cancer. European Journal of Cancer. 157. 464–473. 43 indexed citations

Wessels, Frederik, Max Schmitt, Eva Krieghoff‐Henning, et al.. (2021). Deep learning approach to predict lymph node metastasis directly from primary tumour histology in prostate cancer. British Journal of Urology. 128(3). 352–360. 46 indexed citations

Jutzi, Tanja, Eva Krieghoff‐Henning, Tim Holland‐Letz, et al.. (2020). Artificial Intelligence in Skin Cancer Diagnostics: The Patients' Perspective. Frontiers in Medicine. 7. 233–233. 106 indexed citations

Kutzner, Heinz, Tanja Jutzi, Dieter Krahl, et al.. (2020). Overdiagnosis of melanoma – causes, consequences and solutions. JDDG Journal der Deutschen Dermatologischen Gesellschaft. 18(11). 1236–1243. 28 indexed citations

Brinker, Titus J., et al.. (2020). Computerassistierte Hautkrebsdiagnose. Der Hautarzt. 71(9). 669–676. 2 indexed citations

Lange, Andreas, et al.. (2009). Detergent fractionation with subsequent subtractive suppression hybridization as a tool for identifying genes coding for plasma membrane proteins. Experimental Dermatology. 18(6). 527–535. 3 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

Explore authors with similar magnitude of impact