Workshops – AIME 2026 – AIME 2026 Official web site

Days

Hours

Minutes

Seconds

July 7, 2026

Doctoral Consortium

July 7, 2026

Tutorials

July 7, 2026

Welcome Reception

July 8-9, 2026

AIME Main Conference

July 8, 2026

Conference Gala Dinner

July 10, 2026

Workshops

July 7, 2026:

Doctoral Consortium & Tutorials

July 8-9, 2026:

AIME Main Conference

July 10, 2026:

Workshops

Workshop Program - July 10

CRX building

Time/Location	TBD	TBD	TBD	TBD	TBD
Morning	AI in Nursing – Part I	AI in Clinical Subfields I	Privacy-Preserving AI – Part I	Trustworthy and Translational AI in Health – Part I	Practical AI Implementation I
Afternoon	AI in Nursing – Part II	AI in Clinical Subfields II	Privacy-Preserving AI – Part II	Trustworthy and Translational AI in Health – Part II	Practical AI Implementation II

Topic	Title	Duration
AI in Nursing	W1. Fifth International Workshop on Artificial Intelligence in Nursing (AINurse-26)	7 hours
AI in Clinical Subfields I	W2. Artificial Intelligence in Oncology	3.5 hours
AI in Clinical Subfields II	W3. Foundation Models for Public Health and Epidemiology: From Promise to Practice	3.5 hours
Privacy-Preserving AI	W4. MULTIPLi Health – 1st International Workshop on MULTIcentrics and Privacy-Preserving Learning in Healthcare	7 hours
Trustworthy and Translational AI in Health	W5. From Prototype to Practice: Implementing Trustworthy Human – AI Systems in Healthcare	7 hours
Practical AI Implementation I	W6. Clinical AI: Sociotechnical, Cognitive, and Human-AI Teaming Perspectives on Implementing and Evaluating	3.5 hours
Practical AI Implementation II	W7. KG4Health: Leveraging Knowledge Graphs for Robust Healthcare Solutions	3.5 hours

W1. Fifth International Workshop on Artificial Intelligence in Nursing (AINurse-26)

Martin Michalowski, Maxim Topaz, Lisiane Pruinelli, Charlene Ronquillo and Laura-Maria Peltonen.

Full day.

🔗 Website

Artificial intelligence (AI) is increasingly transitioning from methodological innovation to real-world deployment in healthcare systems. Nurses, the largest segment of the global healthcare workforce, are central to this transition as primary users, evaluators, and mediators of AI-enabled clinical systems. While AI has demonstrated potential to improve clinical decision-making, workflow efficiency, and patient outcomes, significant challenges remain related to explainability, workflow integration, governance, ethical oversight, and clinician trust.

AINurse-26, organized by the Nursing and Artificial Intelligence Leadership (NAIL) Collaborative, focuses on a new and emerging frontier in AI research and practice: the implementation and governance of trustworthy AI systems in nursing practice. Building on the success of prior AINurse workshops (AINurse-22 through AINurse-25), this workshop shifts emphasis from developing AI methods to understanding how existing AI research can be responsibly translated into clinical care.

The workshop will bring together AI researchers, nurses, students, and interdisciplinary stakeholders to examine design principles, evaluation strategies, and organizational challenges that determine whether AI systems achieve meaningful clinical and societal impact. Through paper presentations, panels, and structured discussion, AINurse-26 aims to advance a shared research and practice agenda for trustworthy, human-centered AI in nursing.

W2. Artificial Intelligence in Oncology

Ece Uzun and Yves Lussier.

Half day.

🔗 Website

As advances in artificial intelligence (AI) continue to reshape the landscape of healthcare, this workshop aims to explore the transformative potential of AI in the field of cancer research and treatment. The integration of AI technologies presents unprecedented opportunities to enhance early detection, precision medicine, and personalized treatment strategies for cancer patients. This session will delve into the machine learning (ML) based models identifying novel biomarkers, predicting cancer risk as well drug resistance using multi-modal data including EHR, multi-omics and imaging. By bringing together experts from both the AI and cancer research communities, this session aims to foster collaborative efforts, share insights, and pave the way for a future where AI plays a pivotal role in advancing cancer care.

W3. Foundation Models for Public Health and Epidemiology: From Promise to Practice

Daniele Pala and Giovanna Nicora.

Half day.

🔗 Website

Foundation models (e.g., large language models and multimodal generative systems) offer transformative potential for biomedical and clinical tasks. Yet, their application in public health — which involves population-level decision-making, surveillance, and equity-sensitive interventions — remains underdeveloped. Despite many methodological studies, there is limited translation into practical use, low generalizability across populations, and poor interoperability with existing health data systems.

This workshop will convene researchers and practitioners to critically assess the current landscape, identify barriers to real-world impact, and chart a research agenda that connects foundation model capabilities with public health needs. The half-day workshop will include invited talks, contributed short presentations, and structured discussions, concluding with a community-driven roadmap for evaluation frameworks, hybrid modeling (including agent-based integration with generative AI), and interoperable deployment strategies. We aim to foster collaboration between AI methodologists, public health informaticians, and policy makers, strengthening the field’s scientific foundations and translational impact.

Ultimately, the workshop aims to catalyze collaborative initiatives (e.g. shared benchmarks and consensus evaluation guidelines) to be disseminated through the AIME community, fostering scientifically rigorous and operationally meaningful adoption of foundation models in public health.

W4. MULTIPLi Health - 1st International Workshop on MULTIcentrics and Privacy-preserving Learning in Healthcare

Björn Eskofier, Bruno Casella, Christoph Düsing, Ioanna Miliou and Mirko Polato.

Full day.

🔗 Website

The increasing availability of heterogeneous healthcare data across institutions creates unprecedented opportunities for data-driven medicine, while simultaneously raising critical challenges related to privacy, governance, and data sharing. As a result, many high-value healthcare datasets remain isolated across institutions, limiting the development, validation, and generalizability of Artificial Intelligence (AI) models for clinical and biomedical applications, despite multicentric studies being widely regarded as the gold standard for robust and generalizable medical evidence. Multicentric and privacy-preserving learning paradigms, such as Federated and Distributed Learning, offer a promising path forward by enabling collaborative model training and analysis without requiring the exchange of raw and possibly sensitive patient data.

The MULTIPLi Health workshop addresses the methodological, technical, and practical challenges associated with building trustworthy AI systems under these constraints. It focuses on learning from multicentric and heterogeneous healthcare data while ensuring compliance with privacy regulations. The workshop aims to examine recent advances as well as unresolved issues related to scalability, robustness, evaluation, and deployment in real-world clinical environments.

By aligning with AIME’s focus on medical decision support and clinically meaningful evaluation, MULTIPLi Health provides a forum for advancing privacy-aware AI methodologies that are essential for the next generation of collaborative healthcare research.

W5. From Prototype to Practice: Implementing Trustworthy Human – AI Systems in Healthcare

Mirella Veras, Émilie Valiquette, Fateme Rajabiyazdi, Arnaud Mbadjeu Hondjeu, Camilo E. Valderrama, Mathieu LeBreton, Hamid Mansoor, Kara Kitts, Ouwen Huang.

Full day.

🔗 Website

Health systems around the world are rapidly moving from experimenting with AI to deploying it in real clinical and operational settings. Yet turning a promising prototype into a trusted, sustainable, and human‑centered solution remains one of the hardest challenges in healthcare AI.

This highly interactive, hands-on workshop is designed for researchers, clinicians, implementers, and health system leaders who want to bridge that gap. Participants will work alongside a multidisciplinary team of experts spanning clinical practice, data science, privacy, human‑centered design, and health system implementation. Through a series of practical exercises and collaborative sessions, the workshop will guide attendees through the end‑to‑end journey of implementing a trustworthy “digital human” or human–AI system in a real-world healthcare context. The session will combine:

A hands-on data workshop, grounding theoretical concepts in practical examples and implementation realities
Privacy and legal considerations, with a focus on governance, consent, and risk management when deploying digital humans and AI systems in healthcare environments
Codesign in action, where participants collaboratively design a project team and implementation approach that integrates clinical, technical, and organizational perspectives
Concrete guidance on getting started, including how to identify the right use case, scope a feasible first deployment, and assemble a strong multidisciplinary team
Collaborative working sessions, encouraging peer learning, shared problem‑solving, and cross‑disciplinary dialogue

Whether you are just beginning to explore AI deployment or are already scaling solutions across your organization, this workshop will provide practical frameworks, shared language, and real-world insights for moving from experimentation to impact. Participants will leave with a clearer understanding of what it takes – technically, ethically, and organizationally – to implement digital human systems that clinicians trust, patients accept, and organizations can sustain.

W6. Clinical AI: Sociotechnical, Cognitive, and Human-AI Teaming Perspectives on Implementing and Evaluating

Jorie Butler, Peter Taber, Andre Kushniruk, Elizabeth Borycki, Jen Van Tiem and Michael Matheny.

Half day.

The rapid proliferation of clinical AI tools, including predictive models, large language model-enabled documentation systems, and conversational agents, has outpaced empirical understanding of how these tools are implemented, evaluated, and sustained in real-world healthcare settings. Safe and effective AI deployment requires attention to sociotechnical context, cognitive processes, workflow integration, organizational dynamics, and equity.

This workshop builds on our AIME 2024 and 2025 sessions by advancing from “why sociotechnical approaches matter” to “how to rigorously design, evaluate, and monitor clinical AI in practice.” We will integrate perspectives from implementation science, cognitive psychology, human factors, anthropology, and post-deployment model surveillance. Particular attention will be given to clinician-AI teaming, trust calibration, distributed cognition, workflow reconfiguration, and monitoring of unintended consequences.

Through invited talks, peer-reviewed paper presentations, and interactive discussion, the workshop will create a focused venue for sharing empirical research, frameworks, and evaluation methods for clinical AI implementation.

W7. First International Workshop on Knowledge Graphs for Health (KG4Health)

Enayat Rajabi and Somayeh Kafaie.

Half day.

🔗 Website

The KG4Health workshop addresses the critical intersection of Knowledge Graphs (KGs) and Artificial Intelligence in the biomedical and healthcare domain. While modern AI models (i.e., Generative AI) have shown immense potential in processing healthcare data, clinical text, and biomedical information issues regarding factual accuracy, explainability, and data siloization persist. Knowledge Graphs provide a structured, interpretable foundation that can ground AI models in established medical truths. We welcome computer scientists, medical informaticians, and clinicians to submit their late-breaking results, demos, or position statements via OpenReview to join this high-intensity, half-day dialogue on the future of evidence-based, explainable medical AI. We invite submissions on themes including, but not limited to:

KG-LLM Integration: Methods for embedding KGs into LLMs to enhance factual grounding and reasoning in medical applications.
Neuro-symbolic AI: Combining LLMs with KGs (e.g., Retrieval-Augmented Generation – RAG) for medical QA.
KG Construction & Enrichment: Automated extraction of medical entities/relations from clinical notes and literature.
Interoperability: Mapping KGs to standard ontologies (SNOMED-CT, UMLS, FHIR).
Explainable AI (XAI): Using graph paths to provide the “why” behind AI-driven clinical predictions.
Medical Applications of KG: Drug repurposing, rare disease diagnosis, and personalized treatment pathways.