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Marvin Kopka

Showing results (1-10 of 19) with videos related to

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JMIR Human Factors|January 16, 2026
How to Evaluate the Accuracy of Symptom Checkers and Diagnostic Decision Support Systems: Symptom Checker Accuracy Reporting Framework (SCARF)Marvin Kopka, Markus A Feufel
JMIR Biomedical Engineering|April 8, 2026
Increasing Large Language Model Accuracy for Care-Seeking Advice Using Prompts Reflecting Human Reasoning Strategies in the Real World: Validation StudyMarvin Kopka, Markus A Feufel
Frontiers in Digital Health|November 5, 2024
Statistical refinement of patient-centered case vignettes for digital health researchMarvin Kopka, Markus A Feufel
Communications Medicine|February 24, 2026
Evaluating the accuracy of ChatGPT model versions for giving care-seeking adviceMarvin Kopka, Longqi He, Markus A Feufel
PLOS Digital Health|March 29, 2023
Exploring how informed mental health app selection may impact user engagement and satisfactionMarvin Kopka, Erica Camacho, Sam Kwon, et al.
NPJ Digital Medicine|March 26, 2025
Accuracy of online symptom assessment applications, large language models, and laypeople for self-triage decisionsMarvin Kopka, Niklas von Kalckreuth, Markus A Feufel
JMIR Formative Research|October 12, 2022
The Triage Capability of Laypersons: Retrospective Exploratory AnalysisMarvin Kopka, Markus A Feufel, Felix Balzer, et al.
Clinical and Translational Science|July 24, 2024
Generalizability in real-world trialsAnatol-Fiete Näher, Marvin Kopka, Felix Balzer, et al.
PLOS Digital Health|July 2, 2026
The trust in AI-generated health advice (TAIGHA) scale and short version (TAIGHA-S): Development and validation studyMarvin Kopka, Azeem Majeed, Gabriella Spinelli, et al.
Digital Health|August 24, 2023
How suitable are clinical vignettes for the evaluation of symptom checker apps? A test theoretical perspectiveMarvin Kopka, Markus A Feufel, Eta S Berner, et al.
Pageof 2

Showing results (1-10 of 19) with videos related to

Sort By:
Pageof 2
JMIR Human Factors|January 16, 2026
How to Evaluate the Accuracy of Symptom Checkers and Diagnostic Decision Support Systems: Symptom Checker Accuracy Reporting Framework (SCARF)Marvin Kopka, Markus A Feufel
JMIR Biomedical Engineering|April 8, 2026
Increasing Large Language Model Accuracy for Care-Seeking Advice Using Prompts Reflecting Human Reasoning Strategies in the Real World: Validation StudyMarvin Kopka, Markus A Feufel
Frontiers in Digital Health|November 5, 2024
Statistical refinement of patient-centered case vignettes for digital health researchMarvin Kopka, Markus A Feufel
Communications Medicine|February 24, 2026
Evaluating the accuracy of ChatGPT model versions for giving care-seeking adviceMarvin Kopka, Longqi He, Markus A Feufel
PLOS Digital Health|March 29, 2023
Exploring how informed mental health app selection may impact user engagement and satisfactionMarvin Kopka, Erica Camacho, Sam Kwon, et al.
NPJ Digital Medicine|March 26, 2025
Accuracy of online symptom assessment applications, large language models, and laypeople for self-triage decisionsMarvin Kopka, Niklas von Kalckreuth, Markus A Feufel
JMIR Formative Research|October 12, 2022
The Triage Capability of Laypersons: Retrospective Exploratory AnalysisMarvin Kopka, Markus A Feufel, Felix Balzer, et al.
Clinical and Translational Science|July 24, 2024
Generalizability in real-world trialsAnatol-Fiete Näher, Marvin Kopka, Felix Balzer, et al.
PLOS Digital Health|July 2, 2026
The trust in AI-generated health advice (TAIGHA) scale and short version (TAIGHA-S): Development and validation studyMarvin Kopka, Azeem Majeed, Gabriella Spinelli, et al.
Digital Health|August 24, 2023
How suitable are clinical vignettes for the evaluation of symptom checker apps? A test theoretical perspectiveMarvin Kopka, Markus A Feufel, Eta S Berner, et al.
Pageof 2