Conference Agenda

Brief Description and Outline:

Foundation models are rapidly reshaping medical AI, enabling large‑scale representation learning across imaging, clinical text, biosignals, and multimodal health data. While recent advances demonstrate impressive performance across a wide range of downstream tasks, significant challenges remain in translating medical foundation models from pretraining to reliable clinical impact. These challenges include data heterogeneity, domain shift, limited annotation, evaluation biases, robustness, interpretability, and regulatory constraints. To set the stage, the workshop will begin with a 45‑minute presentation by the organizers, showcasing ongoing collaborative efforts between Helmholtz Munich and German Cancer Research Center (DKFZ) toward the development, evaluation, and adaptation of medical foundation models. This session will highlight joint projects across multimodal learning, scalable pretraining, domain adaptation, and clinically aligned benchmarking, with the aim of fostering cross‑community exchange and new collaborations. The workshop will also feature two invited talks by leading experts Ewa Szczurek (Helmholtz Munich) and Fabian Isensee (DKFZ), providing perspectives on scalable pretraining strategies and pathways to clinical translation. In addition, we will make a call for 300‑word extended abstracts, from which a subset will be selected for short oral presentations, while remaining contributions will be presented during a poster session. To maximize interaction and visibility, poster presenters will have the opportunity to give one‑minute lightning pitches. The program includes a combined 45‑minute session of short talks and poster pitches, followed by a dedicated 45‑minute poster session to encourage in‑depth discussion and networking. The workshop will conclude with closing remarks and awards, recognizing outstanding contributions and strengthening community building around medical foundation models. 

Workshop Program Outline

• 00:00 – 00:10 Welcome Remarks (10 min): Brief introduction, goals, and overview of the workshop.

• 00:10 – 00:55 Talk: Projects on Medical Foundation Models by Workshop Organizers (45 min) Format: ~40 min presentation + 5 min Q&A Deep dive into the organizers’ ongoing projects, covering multimodal pretraining, domain adaptation, evaluation pipelines, and translational challenges.

• 00:55 – 01:25 Invited Talk #1 (30 min) Format: 25 min talk + 5 min Q&A

• 1:25 – 2:10 Short Talks + 1‑Min Poster Pitches (45 min): Short oral presentations: ~4–6 selected abstracts (5–7 min each. Poster lightning pitches: 1 minute per poster

• 2:10 – 2:25 Coffee Break (15 min)

• 2:25 – 3:10 Poster Session (45 min): Dedicated walk‑through and discussion with all presenters.

• 3:10 – 3:40 Invited Talk #2 (30 min) Format: 25 min talk + 5 min Q&A (This replaces the previous panel discussion slot.)

• 03:40 – 3:55 Closing Remarks + Awards (15 min): Summary, acknowledgments, and recognition of outstanding contributions.

Goals:

This workshop aims to bring together researchers and practitioners working on foundation models for medicine, with a focus on the full lifecycle of model development and deployment: large-scale pretraining, adaptation and fine-tuning, evaluation and benchmarking, and real-world clinical applications. We seek contributions that advance methodological foundations as well as practical insights into deploying and validating medical foundation models in realistic settings. MedFM @ HAICON26 aims to provide a focused yet inclusive forum for advancing medical foundation models and for shaping a shared research agenda that bridges methodological innovation and clinical impact.

Presenters Experience:

Ewa Szczurek: Co-director of Helmholtz Munich’s Institute of AI for Health; AI researcher in probabilistic and generative models for computational medicine.

Fabian Isensee: Senior Scientist at German Cancer Research Center; creator of nnU-Net, advancing AI for medical imaging and segmentation.

Target Audience:

Our target audience includes PhD students, postdoctoral researchers, and scientists working in AI for medicine or general AI, as well as clinicians with a basic to intermediate understanding of AI who are interested in how foundation models can be translated into real clinical practice. The workshop is designed for participants who are familiar with fundamental machine learning concepts and who want to deepen their knowledge of large‑scale models, multimodal learning, evaluation, and clinical deployment.

Keywords:

Medical Foundation models; self‑supervised learning; multimodal learning; imaging AI; generalization; benchmarking; evaluation; clinical translation.