Georg Starke

464 total citations
22 papers, 265 citations indexed

About

Georg Starke is a scholar working on Cognitive Neuroscience, Health Informatics and Public Health, Environmental and Occupational Health. According to data from OpenAlex, Georg Starke has authored 22 papers receiving a total of 265 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Cognitive Neuroscience, 8 papers in Health Informatics and 7 papers in Public Health, Environmental and Occupational Health. Recurrent topics in Georg Starke's work include Artificial Intelligence in Healthcare and Education (8 papers), Neuroethics, Human Enhancement, Biomedical Innovations (6 papers) and Clinical Reasoning and Diagnostic Skills (4 papers). Georg Starke is often cited by papers focused on Artificial Intelligence in Healthcare and Education (8 papers), Neuroethics, Human Enhancement, Biomedical Innovations (6 papers) and Clinical Reasoning and Diagnostic Skills (4 papers). Georg Starke collaborates with scholars based in Switzerland, Germany and Netherlands. Georg Starke's co-authors include Bernice S. Elger, Eva De Clercq, Marcello Ienca, Stefan Borgwardt, David Shaw, Pim Haselager, Julia E. Vogt, Xiyao Xie, Christian Sorg and Felix Brandl and has published in prestigious journals such as Scientific Reports, Psychological Medicine and Journal of Medical Internet Research.

In The Last Decade

Georg Starke

19 papers receiving 255 citations

Peers

Georg Starke

PHEOH

Jon Rueda Spain

Bert Heinrichs Germany

Joshua Hatherley Australia

Melanie F. Pradier United States

Ernest Lim United Kingdom

Aparna Balagopalan Canada

Alka V. Menon United States

Isabel Straw United Kingdom

Jonathan Marsh United States

Agrima Srivastava India

Jon Rueda Spain

Georg Starke

56 ×0.5

33 ×0.4

46 ×0.8

PHEOH

42 ×1.0

67 ×1.7

Citations per year, relative to Georg Starke Georg Starke (= 1×) peers Jon Rueda

Countries citing papers authored by Georg Starke

Since Specialization

Citations

This map shows the geographic impact of Georg Starke'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 Georg Starke with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Georg Starke more than expected).

Fields of papers citing papers by Georg Starke

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Georg Starke. 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 Georg Starke. The network helps show where Georg Starke may publish in the future.

Co-authorship network of co-authors of Georg Starke

This figure shows the co-authorship network connecting the top 25 collaborators of Georg Starke. A scholar is included among the top collaborators of Georg Starke 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 Georg Starke. Georg Starke is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Starke, Georg, et al.. (2025). Clinician perspectives on explainability in AI-driven closed-loop neurotechnology. Scientific Reports. 15(1). 34638–34638.

Starke, Georg, et al.. (2025). Machine Learning–Based Patient Preference Prediction: A Proof of Concept. NEJM AI. 2(10). 3 indexed citations

Starke, Georg, et al.. (2025). Ethical gaps in closed-loop neurotechnology: a scoping review. npj Digital Medicine. 8(1). 510–510. 2 indexed citations

Starke, Georg, Felix Gille, Yves Saint James Aquino, et al.. (2025). Finding Consensus on Trust in AI in Health Care: Recommendations From a Panel of International Experts. Journal of Medical Internet Research. 27. e56306–e56306. 10 indexed citations

Starke, Georg, Amanda van Beinum, Christoph Bublitz, et al.. (2024). HYBRIDMINDS—summary and outlook of the 2023 international conference on the ethics and regulation of intelligent neuroprostheses. Frontiers in Human Neuroscience. 18. 1489307–1489307. 1 indexed citations

Starke, Georg, Amanda van Beinum, Surjo R. Soekadar, et al.. (2024). Qualitative studies involving users of clinical neurotechnology: a scoping review. BMC Medical Ethics. 25(1). 89–89. 3 indexed citations

Huber, Maria, et al.. (2023). Human threat circuits: Threats of pain, aggressive conspecific, and predator elicit distinct BOLD activations in the amygdala and hypothalamus. Frontiers in Psychiatry. 13. 1063238–1063238. 2 indexed citations

Starke, Georg, Bernice S. Elger, & Eva De Clercq. (2023). Machine learning and its impact on psychiatric nosology: Findings from a qualitative study among German and Swiss experts. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 4. 3 indexed citations

Starke, Georg, et al.. (2023). Out of their minds? Externalist challenges for using AI in forensic psychiatry. Frontiers in Psychiatry. 14. 1209862–1209862. 6 indexed citations

10.

Shaw, David, et al.. (2023). Playing Brains: The Ethical Challenges Posed by Silicon Sentience and Hybrid Intelligence in DishBrain. Science and Engineering Ethics. 29(6). 38–38. 5 indexed citations

11.

Starke, Georg & Marcello Ienca. (2022). Misplaced Trust and Distrust: How Not to Engage with Medical Artificial Intelligence. Cambridge Quarterly of Healthcare Ethics. 33(3). 360–369. 25 indexed citations

12.

Clercq, Eva De, Georg Starke, & Michael Rost. (2022). “Waking up” the sleeping metaphor of normality in connection to intersex or DSD: a scoping review of medical literature. History & Philosophy of the Life Sciences. 44(4). 50–50. 3 indexed citations

13.

Starke, Georg, et al.. (2022). Karl Jaspers and artificial neural nets: on the relation of explaining and understanding artificial intelligence in medicine. Ethics and Information Technology. 24(3). 5 indexed citations

14.

Starke, Georg, et al.. (2022). Re-focusing explainability in medicine. Digital Health. 8. 2282129848–2282129848. 53 indexed citations

15.

Starke, Georg, Benedikt R. Schmidt, Eva De Clercq, & Bernice S. Elger. (2022). Explainability as fig leaf? An exploration of experts’ ethical expectations towards machine learning in psychiatry. AI and Ethics. 3(1). 303–314. 12 indexed citations

16.

Starke, Georg, Eva De Clercq, & Bernice S. Elger. (2021). Towards a pragmatist dealing with algorithmic bias in medical machine learning. Medicine Health Care and Philosophy. 24(3). 341–349. 36 indexed citations

17.

Starke, Georg, et al.. (2021). Intentional machines: A defence of trust in medical artificial intelligence. Bioethics. 36(2). 154–161. 34 indexed citations

18.

Zimmermann, Bettina, et al.. (2020). Actionability and scope should determine the extent of counselling for presymptomatic genetic testing. Swiss Medical Weekly. 150(2728). w20274–w20274.

19.

Starke, Georg, Eva De Clercq, Stefan Borgwardt, & Bernice S. Elger. (2020). Computing schizophrenia: ethical challenges for machine learning in psychiatry. Psychological Medicine. 51(15). 2515–2521. 43 indexed citations

20.

Bratec, Satja Mulej, et al.. (2020). Your presence soothes me: a neural process model of aversive emotion regulation via social buffering. Social Cognitive and Affective Neuroscience. 15(5). 561–570. 15 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.

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