Block Course: AI in Medicine (AiM): Foundations, Methods, and Critical Perspectives
Image created with assistance from DALL·E for the AiM Course at the University of Bonn.
Block Course: AI in Medicine (AiM)
- Lecturers: Prof. Dr. Shadi Albarqouni
- Tutors: Adea Nesturi, David Gaviria, Jiajun Zeng
- Module: CM1659
- Credits: 2 SWS
- Programme: PhD Candidates at BIGC CPS and NeuroScience
- Language: English
- Format: Block Course (2 SWS)
- Time: Tuesday to Thursday, 14-16 April 2026, 09:00 – 18:00
- Location: TBD
Description
This block course offers an intensive introduction to artificial intelligence in medicine, with a focus on modern deep learning methods for biomedical and clinical applications. Designed as an interdisciplinary format, the course combines concept-driven lectures with interactive labs, allowing students to engage both with the theoretical foundations of AI and with practical challenges in medical data analysis.
The course is structured around a series of core topics that are central to contemporary AI in medicine. It begins with an introduction to deep learning, providing the conceptual basis for understanding neural networks and their role in medical applications. Building on this foundation, students are introduced to key tasks such as detection and classification, as well as segmentation, which are essential in areas such as diagnostic imaging, image-guided analysis, and automated clinical decision support. The course then expands to quality control in AI systems, with a particular emphasis on uncertainty estimation and interpretability, highlighting how trustworthy and transparent models can be developed and assessed. Further sessions address bias and causality, encouraging students to critically examine fairness, generalizability, and the difference between correlation-driven prediction and clinically meaningful reasoning. Finally, the course introduces vision-language models and their emerging role in connecting medical images, clinical text, and multimodal reasoning.
Each lecture is followed by an interactive lab in which students apply, explore, and critically reflect on the methods discussed. These labs are designed to deepen understanding through hands-on experimentation, collaborative problem-solving, and discussion of real-world medical use cases. In this way, the course not only introduces students to the technical building blocks of AI in medicine but also emphasizes the broader methodological and ethical questions that arise when such systems are developed for healthcare.
By the end of the course, students will have gained a structured overview of important AI methods in medicine, an understanding of their main areas of application, and a critical perspective on their limitations, risks, and clinical potential.
Learning Goals
Technical Skills
- Understand and critically appraise current research in AI in Medicine.
- Deepen expertise in medical image segmentation, registration, classification, uncertainty quantification, and bias/causality.
Topics and Focus Areas
Seminar topics are drawn from recent publications in top-tier venues and may include:
- Deep Learning Architectures (U-Nets, GANs, Vision Transformers)
- Segmentation & Classification (e.g., anatomical structure delineation, lesion detection)
- Uncertainty Quantification & Bias (Bayesian learning, causal inference, dataset shift)
đź—“ Block Course Schedule (14-16 April 2026) – TBC
| Date | Topic | Description | ||
|---|---|---|---|---|
| Tuesday, April 14 | Deep Learning Foundations | Introduction to deep learning concepts specific to medical imaging. | ||
| Lecture: Detection and Classification | Introduction to SoTA algorithms for Detection and Classification | |||
| Lab: Detection and Classification | Introduction to SoTA algorithms for Detection and Classification | |||
| Wednesday, April 15 | Lecture: Segmentation | Introduction to deep learning concepts specific to medical imaging. | ||
| Lecture: Quality Control: Interpretbility | Introduction to SoTA Interpretability algorithms | |||
| Lab: Segmentation and Interpretbility | Introduction to SoTA algorithms for Segmentation and Interpretability | |||
| Thursday, April 16 | Lecture: Quality Control: Uncertiantity | Introduction to uncertainty and quantification methods | ||
| Lecture: Foundation Models / Bias and Causality | TBC | |||
| Lab: | TBC |
Recommended Prerequisites
At least one of the following:
- Familiarity with fundamental medical imaging techniques and an interest in AI applications.
- Understanding of medical diagnostics and clinical data interpretation.
- A solid background in Python programming, linear algebra, probability, and numerical algorithms is recommended.
References and Resources
- [1] Litjens, Geert, et al. “A survey on deep learning in medical image analysis.” Medical image analysis 42 (2017): 60-88.
- [2] Shen, Dinggang, Guorong Wu, and Heung-Il Suk. “Deep learning in medical image analysis.” Annual review of biomedical engineering 19.1 (2017): 221-248.
- [3] Chen, Xuxin, et al. “Recent advances and clinical applications of deep learning in medical image analysis.” Medical image analysis 79 (2022): 102444.
- [4] Tajbakhsh, Nima, et al. “Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation.” Medical image analysis 63 (2020):101693.
- [5] Wang, Ge, Jong Chul Ye, and Bruno De Man. “Deep learning for tomographic image reconstruction.” Nature machine intelligence 2.12 (2020): 737-748.
Registration
Registration takes place via BIGS CPS Portal or by contacting Prof. Dr. Shadi Albarqouni.
Adea Nesturi
PhD Candidate
Adea is currently a PhD candidate under the supervision of Prof. Dr. Shadi Albarqouni. She earned her master’s degree in Health Data Science from the University of St Andrews, Scotland, in 2024, where her research focused on the application of deep learning techniques for cancer detection. During her master’s program, she also contributed to the BraTS 2021 challenge, working on the segmentation of brain tumors (gliomas), which provided her with valuable experience in medical image analysis and collaborative research in international competitions.Adea’s research interests are centered on harnessing the power of deep learning to solve real-world medical challenges. She is particularly passionate about developing innovative technologies and AI-driven solutions that can directly improve patient care, enhance diagnostic accuracy, and optimize healthcare outcomes. Through her work at the Albarqouni Lab, she aims to bridge the gap between cutting-edge computational methods and practical applications in clinical settings, advancing the field of medical AI while addressing critical healthcare needs.
David D. Gaviria
PhD Candidate
David is currently a PhD candidate supervised by Prof. Dr. Shadi Albarqouni. He earned his master’s degree in AI from FIB, which is a joint program of UPC, UB, and URV. In 2019, he achieved the top spot in the SIIM-ISIC competition for skin lesion classification and has published his work at VISAPP 2023. David has a keen interest in utilizing technology for the betterment of society and intends to make contributions to AI in the field of medicine through his work at Albarqouni Lab.
Jiajun Zeng
PhD Candidate
Jiajun Zeng is a PhD candidate at the University of Bonn, supervised by Prof. Shadi Albarqouni. He holds an MSc in Biomedical Engineering from Shenzhen University. During his master’s studies, he focused on AI for ultrasound image computing under the supervision of Assoc. Prof. Ruobing Huang and Prof. Dong Ni. His research interests span AI for healthcare, particularly in enhancing radiologists’ workflows through anatomical structure segmentation, disease diagnosis, and treatment support — with a focus on scenarios involving limited annotations and scarce computational resources. Currently, he is exploring efficient vision-language models and multimodal large language models for applications in the healthcare domain.
Pranav Kirti
Visiting Researcher & Fulbright Scholar
Pranav is a visiting researcher from the USA on a Fulbright Study/Research scholarship. He recently earned his bachelor’s degree in Data Science from Washington University in St. Louis, and he hopes to pursue a career in academic medicine. His project is titled Multimodal, Explainable Deep Learning for Rapid and Affordable Diagnosis of Retinal Diseases in LMICs.
