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Employee photo Mr. Kim Tang
© MedizinFotoKöln

Fu-Sung Kim-Benjamin Tang (M.Sc.)

Researcher
ORCID: 0000-0002-6132-0089

Biography

Kim Tang's area of expertise in his current PhD is the utilization of biomedical text data using natural language processing and artificial intelligence for data-driven medical applications. During his previous academic career at RWTH Aachen University, he focused on AI in the context of explainability and distributed learning. Through two semesters abroad at the Kwantlen Polytechnic University in Vancouver and at the National Chiao Tung University in Hsinchu, he gained insights into international research regarding AI-based image processing and information retrieval. Since January 2025, he is responsible for the software development and research direction of distributed learning at BI-K within the PrivateAIM and BETTER project.

Contact

Academic Background

Areas of Expertise

Research Focus

  • Federated Learning
  • Transparency
  • Researcher (Distributed Analytics)
    University of Cologne, Medical Faculty and University Hospital Cologne, Institute of Biomedical Informatics
  • PhD Student (Computer Science in Medicine)
    RWTH Aachen University, Institute of Applied Medical Engineering
  • -
    Master Student (Computer Science)
    RWTH Aachen University

    Master thesis focus on distributed analytics

  • -
    Bachelor Student (Computer Science)
    RWTH Aachen University

    Bachelor thesis focus on explainability of biomedical text classification

Current Teachings

Medical Image processing

Bachelor Studies Clinical Semesters Clinicians Doctoral Studies Master Studies PostDoc Preclinical Semesters WiSe & SoSe

This course provides an introduction to the most important concepts in medical image processing. The aim of the course is to impart practical basic knowledge required for the evaluation of medical image data.

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Medical AI - Explainable AI for Diabetes Prediction: A Hands-On Seminar with Python

Clinical Semesters Clinicians Doctoral Studies PostDoc Preclinical Semesters & SoSe WiSe

In diesem Seminar lernen Medizinstudierende, wie künstliche Intelligenz zur Vorhersage von Diabetes eingesetzt werden kann. Anhand eines konkreten Beispieldatensatzes entwickeln sie ein einfaches KI-basiertes Klassifikationsmodell mit Python in einer interaktiven vorinstallierten Programmierumgebung. Es werden dabei Inhalte zur Grundthematik, dem Einlesen und Analysieren der Ausgangsdaten, der Klassifizierung sowie der Evaluierung der Ergebnisse vorgestellt und durch diverse Aufgaben vertieft. Besonderer Fokus wird dabei auf die Erklärbarkeit gesetzt, um Nachvollziehbarkeit der Klassifizierungen zu ermöglichen und Ergebnisse auch klinisch genauer betrachten zu können. Inhalte des Seminars: Einführung in die Problemstellung: Wie kann KI bei der Diabetes-Diagnose unterstützen? Datenexploration: Verständnis der Features und ihrer Bedeutung Aufbau eines KI-basierten Klassifikationsmodells Evaluierung der Modellleistung Einblick in Modellinterpretation: Welche Merkmale (Features) sind entscheidend? Studierende, die an beiden Sitzungen teilnehmen, erhalten auf Anfrage eine Teilnahmebescheinigung.

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AI in Medicine Series

Bachelor Studies Clinical Semesters Clinicians Doctoral Studies Master Studies PostDoc Preclinical Semesters & SoSe WiSe

Artificial intelligence is already fundamentally changing medicine, but how do the underlying methods work, and what opportunities and challenges do they present? In this series of seminars, each session will cover a new, practical topic, including the basics of some AI methods, ethical challenges and possible solutions. The lectures, depending on the speaker, could be in German or English, are thematically linked but self-contained

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Coding Basics in Python

Clinical Semesters Preclinical Semesters Doctoral Studies Clinicians PostDoc SoSe & WiSe

Einführung in die grundlegenden Konzepte der Programmierung in Python, die für die Auswertung von medizinischen und Forschungsdaten erforderlich sind. Die Teilnehmer werden in diesem interaktiven Seminar aus erster Hand lernen, wie sie ihren eigenen Code entwickeln und ausführen können.

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WissPro - Literaturrecherche

Preclinical Semesters SoSe

This course is offered to medical students interested in WissPro 1 and 2, involving literature research. It includes an introductory lecture on literature search strategies and best practices, as well as presentations on the topics offered by different members of our institute. The work is organised according to a schedule with several checkpoints and concludes with on-site presentations by the participating students.

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Publications from Fu-Sung Kim-Benjamin Tang

Guidance for clinical evaluation under the medical device regulation through automated scoping searches

2023 - Open Access -
Fu-Sung Kim-Benjamin Tang, Mark Bukowski, Thomas Schmitz-Rode, Robert Farkas

The Medical Device Regulation (MDR) in Europe aims to improve patient safety by increasing requirements, particularly for the clinical evaluation of medical devices. Before the clinical evaluation is initiated, a first literature review of existing clinical knowledge is necessary to decide how to proceed. However, small and medium-sized enterprises (SMEs) lacking the required expertise and funds may disappear from the market. Automating searches for the first literature review is both possible and necessary to accelerate the process and reduce the required resources. As a contribution to the prevention of the disappearance of SMEs and respective medical devices, we developed and tested two automated search methods with two SMEs, leveraging Medical Subject Headings (MeSH) terms and Bidirectional Encoder Representations from Transformers (BERT). Both methods were tailored to the SMEs and evaluated through a newly developed workflow that incorporated feedback resource-efficiently. Via a second evaluation with the established CLEF 2018 eHealth TAR dataset, the more general suitability of the search methods for retrieving relevant data was tested. In the real-world use case setting, the BERT-based method performed better with an average precision of 73.3%, while in the CLEF 2018 eHealth TAR evaluation, the MeSH-based search method performed better with a recall of 86.4%. Results indicate the potential of automated searches to provide device-specific relevant data from multiple databases while screening fewer documents than in manual literature searches.