Potential Supervisor: Tim Kilgus
Level: Bachelor / Master / both
Strategic Orientation: AI and Analytics

Background

The U.S. Food and Drug Agency does allow Software as a Medical Device, specifically Artificial Intelligence-Enabled Medical Devices. The authorization as such enables marketing in the United States, but also other countries globally. At the forefront of AI in medicine is the field of radiology. Of the 1245 authorized AI-enabled medical devices, 956 are in the Radiology panel.

As part of a collaboration with the Department of Radiology at the University of Cambridge, we aim to better understand the current state of authorized software. Specifically, we are interested in the technical (training data, ML-algorithms), and organizational (organizations, platforms) aspects related to the development and clinical implementation.

The list of authorized AI-enabled medical devices is publicly available.

Research Directions

• AI Landscape in Radiology
Radiology is at the forefront of AI adoption in medicine. Analyzing the spectrum of FDA-authorized AI-enabled medical devices can reveal dominant imaging modalities, algorithmic approaches, and clinical application areas. Such an overview helps to map technological focus areas and identify trends across disease domains.

• Organizational Ecosystems and Innovation
The development of AI tools in radiology involves diverse actors—from startups to multinational medtech firms. Exploring these organizational networks and partnerships can shed light on how innovation emerges and diffuses across the medical imaging industry.

• Data and Algorithm Transparency
AI systems in radiology rely heavily on the quality and representativeness of training data. Examining how companies disclose information about datasets, validation methods, and performance metrics provides insights into current transparency practices and potential biases.

References

Lebovitz, S., Levina, N., & Lifshitz-Assa, H. (2021). Is AI Ground Truth Really True? The Dangers of Training and Evaluating AI Tools Based on Experts’ Know-What. MIS Quarterly, 45(3), 1501–1526. https://doi.org/10.25300/MISQ/2021/16564

Lawrence, R., Dodsworth, E., Massou, E., Sherlaw-Johnson, C., Ramsay, A., Walton, H., O’Regan, T., Gleeson, F., Crellin, N., Herbert, K., Ng, P. L., Elphinstone, H., Mehta, R., Lloyd, J., Halliday, A., Morris, S., & Fulop, N. (2025). Artificial Intelligence for diagnostics in radiology practice: A rapid systematic scoping review. The Royal College of Radiologists Open, 3, 100218. https://doi.org/10.1016/j.rcro.2024.100218

Jacobides, M. G. (2024). Externalities and complementarities in platforms and ecosystems: From structural solutions to endogenous failures. Research Policy.

Potential Supervisor: Nazokatkhon Akhmadjonova
Level: Adaptable
Strategic Orientation: Digital Health · Sustainability · Data Platforms

Background

Medical devices placed on the European market are subject to extensive regulatory requirements, including registration, conformity assessment, and post-market surveillance. With the introduction of EUDAMED, the European Database on Medical Devices, a large-scale digital infrastructure has been established to enhance transparency across the medical device lifecycle. EUDAMED integrates data on manufacturers, notified bodies, and certificates, creating new opportunities for system-level analysis of the European medical device market from an information systems perspective.

At the same time, circular economy principles are gaining importance in healthcare, particularly with respect to waste reduction, reuse, reprocessing, and improved lifecycle management of medical devices. However, empirical insights into how circularity-relevant characteristics are reflected in digital regulatory infrastructures remain limited. It is largely unclear how actors are interconnected within the EUDAMED ecosystem, how device categories differ in terms of regulatory and organizational structures, and where potential leverage points for circular strategies may exist.

This thesis aims to analyze the European medical device market using EUDAMED data, with a focus on stakeholder networks and their implications for circularity, governance, and information flows within regulated digital infrastructures.

Research Directions

Possible research directions include:

• Mapping the European medical device market using EUDAMED data, including manufacturers, device categories, and regulatory actors
• Analyzing stakeholder networks and structural patterns within the medical device ecosystem
• Exploring how market structures and regulatory characteristics relate to circularity-relevant aspects of medical devices
• Identifying device categories or actor constellations that may be particularly relevant for circular economy strategies

The thesis is exploratory in nature and focuses on descriptive and network-oriented analysis rather than causal inference.

Methodological Orientation

The project emphasizes data-driven and exploratory methods, potentially including:

• Descriptive analysis of EUDAMED datasets
• Network analysis of stakeholder relationships
• Comparative analysis across device categories

The scope and analytical depth can be adapted depending on the student’s background and interests.

Requirements and Administration (optional)

An interest in digital health, sustainability, and data-driven analysis is expected. Basic skills in data analysis or network analysis are advantageous. The topic can be scaled for Bachelor’s or Master’s theses.

References

Hoveling, T., Nijdam, A. S., Monincx, M., Faludi, J., & Bakker, C. (2024). Circular economy for medical devices: Barriers, opportunities, and best practices from a design perspective. Resources, Conservation & Recycling, 208, 107719. https://doi.org/10.1016/j.resconrec.2024.107719

Melville, N. P. (2010). Information systems innovation for environmental sustainability. MIS Quarterly, 34(1), 1–21. https://doi.org/10.2307/20721419

Recker, J., Zeiss, R., & Mueller, M. (2024). iRepair or I Repair? A dialectical process analysis of control enactment in the iPhone repair aftermarket. MIS Quarterly, 48(1), 321–346.

Background

The results of data analyses from (randomized) clinical trials typically form the basis for the approval of medications and interventions in the United States and Europe. Following growing demands for greater transparency regarding individual patient data, pharmaceutical and medical technology companies, as well as public institutions, have adapted their data management practices and now make these data available to other researchers through dedicated data-sharing infrastructures.

The largest of these infrastructures is Vivli. Originating from a research initiative at Harvard Medical School, Vivli currently provides access to data from more than 5 million patients across over 7,500 clinical trials.

As part of a broader research project at the Chair of Information Systems (Prof. Fürstenau), both quantitative and qualitative data on this infrastructure have been collected. These include metadata on the 7,500 clinical trials, more than 800 data-sharing requests, and over 400 publications resulting from the reuse of these data.

Research Directions

• Data Reuse
Clinical trial data vary in their characteristics, such as the targeted disease area or the medication under investigation. The anonymization of clinical trial data is costly and complex. Therefore, it would be worthwhile to examine which criteria can predict the potential for secondary use of such data.

• Innovation Outcomes
For many data infrastructures, assessing the value created through data sharing remains a challenge. To ensure the long-term sustainability of these infrastructures, it is essential to capture the benefits generated. While usage metrics (such as data requests and resulting publications) provide a descriptive foundation, there is a lack of tools and indicators to measure the innovation outcomes that emerge from data reuse.

• Infrastructure Emergence and Development
In addition to the Vivli platform—considered the global hub for clinical trial data—there are other infrastructures that specifically focus on rare diseases. Comparing these infrastructures could help identify and explain key differences in their development and functioning.

References

Anckaert, P.-E. (2025). When the drugs (don’t) work: The role of science in product commercialization. Research Policy, 54(5), 105237. https://doi.org/10.1016/j.respol.2025.105237

Kilgus, T., Patecka, A., Schurig, T., Kari, A., Gubser, R., Gersch, M., Wessel, L., & Fürstenau, D. (2024). Creating Value from the Secondary Use of Health Data: International Examples, Best Practices, and Opportunities to Scale. Communication of the Association for Information Systems, 55, 507–534. https://doi.org/10.17705/1CAIS.05520

Kilgus, Tim; Kari, Arthur; Gubser, Rahel; Dewey, Marc; Gersch, Martin; and Fürstenau, Daniel (2024). Bridging the valley of death: balancing value creation and capture in health data sharing platforms. ICIS 2024 Proceedings. 19. https://aisel.aisnet.org/icis2024/ishealthcare/ishealthcare/19

Pujol Priego, Laia and Wareham, Jonathan. (2024). Data Commoning in the Life Sciences. MIS Quarterly, 48(2), 491–520.

Potential Supervisor: Nazokatkhon Akhmadjonova
Level: Master thesis
Strategic Orientation: Digital Health · Sustainability · Systems Thinking

Background

Digital medical devices increasingly shape healthcare delivery, yet their lifecycle management remains largely linear. Single-use designs, limited transparency, and strict regulatory requirements constrain reuse, reprocessing, and recycling strategies, particularly in highly regulated healthcare environments. At the same time, digital traceability solutions and policy initiatives aimed at circularity are gaining momentum, raising questions about their system-wide effects.

Understanding circularity in digital health requires moving beyond isolated technological or regulatory perspectives. Instead, it calls for a systems-oriented approach that captures feedback effects, delays, and interdependencies between regulatory frameworks, economic incentives, organizational behavior, and material flows. System dynamics modeling offers a suitable methodological lens to analyze these complex interactions and to explore how circular strategies evolve over time under different system configurations.

This thesis contributes to ongoing work in Circular Digital Health by applying system dynamics modeling to analyze the conditions under which circular strategies for digital medical devices may emerge, stabilize, or fail.

Research Directions

Possible research directions include:

• Modeling feedback relationships between regulation, traceability, economic incentives, and material flows in digital medical device systems
• Exploring how different policy or system configurations influence reuse, reprocessing, and recycling dynamics over time
• Identifying leverage points and unintended consequences in circular digital health strategies through scenario-based simulation

The thesis focuses on conceptual and simulation-based analysis rather than technical system implementation.

Methodological Orientation

The project emphasizes conceptual modeling and simulation-based analysis, potentially including:

• System dynamics model development
• Scenario-based simulation and sensitivity analysis
• Theory-informed model design grounded in sustainability and information systems research

The scope and complexity can be adjusted depending on the student’s background and methodological experience.

Requirements and Administration (optional)

Students should have an interest in systems thinking, sustainability, and digital health. Basic familiarity with modeling concepts or a willingness to learn system dynamics tools is beneficial. The topic allows flexibility in scope and complexity depending on the student’s background.

References

Fang, Y., Lim, K. H., Qian, Y., & Feng, B. (2018). System Dynamics Modeling for Information Systems Research: Theory Development and Practical Application. MIS Quarterly, 42(4), 1303–1330, A1–A5. https://www.jstor.org/stable/26635082

Georgantzas, N. C. (2008). Information systems research with system dynamics. System Dynamics Review, 24(3), 247–264. https://doi.org/10.1002/sdr.412

Zhang, A., & Seuring, S. (2024). Digital product passport for sustainable and circular supply chain management: A structured review of use cases. International Journal of Logistics Research and Applications. https://doi.org/10.1080/13675567.2024.2374256

Background

Das Teilen von Gesundheitsdaten ist eine zentrale Voraussetzung für datengetriebene Forschung und digitale Innovationen im deutschen Gesundheitswesen. In den vergangenen Jahren sind unterschiedliche Initiativen entstanden, die Datennutzung und -bereitstellung auf nationaler und institutioneller Ebene ermöglichen sollen, sich jedoch hinsichtlich Zielsetzung, Governance, technischer Umsetzung und Zugangsbedingungen unterscheiden. Ziel dieser Arbeit ist der vergleichende Analyse ausgewählter Initiativen des Datenteilens im deutschen Gesundheitswesen auf Basis von etwa acht bis zehn Experteninterviews.