From May 7 to 9, 2026, the historic city of Kraków, Poland became the epicenter of a critical conversation: how can artificial intelligence move from promising code into tools that genuinely support clinical care — safely, meaningfully, and accountably?
The AIMed 2026 Conference, organized jointly by the Polish Institute for Evidence Based Medicine (PIEBM), the Big Data Institute at the University of Oxford, McMaster University’s Department of Medicine, and the Interdisciplinary Health Data Center at Jagiellonian University Medical College, brought together clinicians, researchers, data scientists, regulators, and industry leaders under the dual themes of “Rebooting EBM: Path Towards EB-AIM (Evidence Based Artificial Intelligence Medicine)” and “Clinical AI in Practice.”
A Three-Day Structure for Depth and Breadth
The conference program was deliberately structured across three distinct days to cover the full lifecycle of AI in medicine:
Day 1 — Research & Innovation Day featured presentations of original scientific research on AI applications in healthcare. Accepted abstracts were published in a Book of Abstracts produced in collaboration with BMJ Digital Health & AI, with the highest-rated submissions selected for oral presentations.
Day 2 — State-of-the-Art Day was dedicated to keynote lectures from invited experts, presenting the current state of knowledge and future directions for AI in clinical settings.
Day 3 — Education Day shifted to hands-on workshops and educational sessions, designed to equip attendees with practical knowledge for implementing AI tools in their own institutions.
World-Class Keynote Speakers
AIMed 2026 secured an impressive lineup of keynote speakers spanning computer science, clinical research, and regulatory leadership:
- Professor Marta Kwiatkowska (University of Oxford) — a leading figure in formal verification and AI safety, bringing expertise on trustworthy machine learning systems
- Professor Piotr Sankowski (University of Warsaw) — offering perspectives on algorithmic foundations and their clinical applicability
- Professor Andreas Maier (Pattern Recognition Lab, FAU Erlangen–Nürnberg) — sharing insights from decades of medical image analysis and pattern recognition research
- Emer Cooke (Executive Director, European Medicines Agency) — providing the regulatory perspective on AI in medicines development and evaluation
- Professor Gary Collins (University of Birmingham) — contributing expertise on clinical prediction models and evidence-based methodology
Themes That Defined the Conversation
Across all three days, several core themes emerged from the sessions and discussions:
Foundation Models and Multimodal AI. As large language models continue to evolve, the conference examined how foundation models trained on diverse data types — text, imaging, genomics, and clinical notes — could transform diagnostic accuracy and clinical decision support.
Transparency, Scalability, and Testing Infrastructure. A recurring concern was the gap between promising AI research and deployable clinical tools. Sessions focused on building robust testing infrastructure, ensuring model transparency, and scaling algorithms from the lab to the bedside without sacrificing safety.
AI Regulation in Healthcare. With the EU AI Act shaping the regulatory landscape, Emer Cooke’s keynote and follow-up panels addressed how frameworks like the European Health Data Space intersect with AI regulation, and what compliance means for developers deploying clinical AI in Europe.
Ethics and Patient Perspectives. The conference dedicated significant attention to the ethical dimensions of AI in medicine — from algorithmic bias to patient consent, data privacy, and the importance of involving patient organizations in the design and deployment of AI tools.
More Than a Conference: The AIMed Initiative
AIMed is not a one-off event but a year-round international platform for collaboration. The initiative hosts workshops, tutorials, and networking activities that connect academia, clinical practice, industry, and regulators. The 2026 conference served as the anchor point for this ongoing effort, with satellite events including the 11th McMaster International Review Conference of Internal Medicine (MIRCIM) and the Young Talents in Internal Medicine World Finals 2026 running alongside.
Bridging the Implementation Gap
The central challenge that AIMed 2026 sought to address is one the healthcare AI community knows well: the gap between the promise of AI and its real-world clinical impact. While AI models continue to achieve impressive results in controlled research settings, the journey from a published paper to a tool that a clinician trusts, uses daily, and integrates into patient care pathways remains fraught with obstacles.
By bringing together the full ecosystem — researchers building the models, clinicians who would use them, regulators overseeing their safety, and patients whose lives they affect — AIMed 2026 made a meaningful contribution to closing that gap. The question is no longer whether AI will transform medicine, but how we ensure it does so responsibly.
