Tutorials
July 7, 2026:
Tutorials
(& Doctoral Consortium)
July 8-9, 2026:
AIME Main Conference
July 10, 2026:
Workshops
Tutorial Program - July 7
CRX building
Time/Location | CRX 632 | CRX C407 | CRX C440 | CRX C408 |
|---|---|---|---|---|
Morning | AI for Imaging I
| Responsible AI I
| Evaluation of AI | Advanced Topics I
|
Afternoon | Responsible AI II | Advanced Topics II
|
Topic | Title | Duration |
|---|---|---|
AI for Imaging I | T2. From Radiomics to Dosiomics: Patterns, Tools and Challenges | 4 hours |
Responsible AI I | T4. Responsible AI-Assisted Qualitative Data Analysis in Health Research: A Hands-On CFIR Tutorial | 3.5 hours |
Responsible AI II | T5. CARE-AI: Framework Contextual, Accountable, and Responsible Ethics for Artificial Intelligence in Healthcare | 3.5 hours |
Evaluation of AI | T6. Pragmatic Evaluation of Large Language Models in Healthcare | 4 hours |
Advanced Topics I | T7. Appraise and Stress-Test Clinical AI: Calibration, Dataset Shift, and Post-Deployment Monitoring | 3.5 hours |
Advanced Topics II | T8. From LLMs to DNA: A Practical Guide to Genomic Foundation Models in Healthcare | 3.5 hours |
Note: The following tutorials were cancelled by the tutorial organizers:
- T1 (Interpretable AI and Evolving Knowledge in Medicine: From Probabilistic Reasoning and Graphs to Dynamic, Explainable Clinical Systems), originally in the two “Interpretable AI” slots.
- T3 (Artificial Intelligence Pipelines for Medical Imaging: From Preprocessing to Deep Learning in fMRI and CT), originally in the “AI for Imaging II” slot,
T2. From Radiomics to Dosiomics: patterns, tools and challenges
Roberto Gatta, Unai Pérez Goya, Amaia Gastearena Irigoyen and Paola Jablonska.
Half day.
🔗 Website
T4. Responsible AI-Assisted Qualitative Data Analysis in Health Research: A Hands-On CFIR Tutorial
Zack Van Allen, L. Jayne Beselt, Douglas Archibald, Jerry Maniate and Arun Radhakrishnan.
Half day.
T5. CARE-AI: Framework Contextual, Accountable, and Responsible Ethics for Artificial Intelligence in Healthcare
Lyn Sonnenberg, Jerry Maniate.
Half day.
T6. Pragmatic Evaluation of Large Language Models in Healthcare
Hojjat Salmasian, Abdul Tariq, Ashley Oliver, Dhineshvikram Krishnamurthy and Jim Urick.
Half day.
Large Language Models (LLMs) and LLM-based tools are becoming ubiquitous in healthcare. However, there is a dearth of analyses evaluating the efficacy of these tools. This tutorial has three learning objectives for participants:
(1) gain a rigorous conceptual understanding of how LLMs are built;
(2) become familiar with the various frameworks available in the literature for LLM evaluation
(3) observe three real-world case studies that demonstrate the use of these frameworks (and other open-source tools) to evaluate LLM deployments of increasing complexity.
T7. Appraise and Stress-Test Clinical AI: Calibration, Dataset Shift, and Post-Deployment Monitoring
Fares Alahdab, Venkatesh Thiruganasambandamoorthy
Half day.
Clinical prediction models and machine learning systems are being deployed in health systems, yet many adoption decisions still rely on headline performance metrics and incomplete reporting. This tutorial teaches a practical, method-focused workflow for deciding whether a model is ready for local use and what to monitor after deployment. Using a synthetic cohort and precomputed model outputs, participants will: (1) distinguish discrimination from calibration and quantify calibration error, (2) choose decision thresholds by linking model outputs to clinical consequences, (3) evaluate transportability under dataset shift (prevalence, measurement, and missingness changes), and (4) design a lightweight monitoring plan with explicit review and rollback triggers. The tutorial is hands-on and laptop-based, with a no-code workbook (Excel/Google Sheets) and an optional companion notebook for those who prefer code. Small groups conclude with an “adoption decision memo” (Go / No-Go / Go-with-conditions) plus a monitoring plan, mirroring real institutional governance deliverables. Participants leave with reusable templates for appraisal, decision documentation, and monitoring that can be adapted to new clinical domains.
T8. From LLMs to DNA: A Practical Guide to Genomic Foundation Models in Healthcare
Pablo Arozarena Donelli, Simone Rancati, Giovanna Nicora, Riccardo Bellazzi, Enea Parimbelli and Luigi Portinale.
Half day.
🔗 Website
As genomic sequencing becomes increasingly integrated into clinical practice, the primary challenge is shifting from data generation to functional interpretation. Recent advances in artificial intelligence have introduced genomic foundation models, deep learning architectures trained on large collections of biological sequences that learn contextual representations of DNA. This tutorial provides a practical introduction to genomic foundation models and their emerging applications in medicine and epidemiology. Participants will explore the conceptual transition from natural language processing models to genomic sequence models, focusing on architectural adaptations required for biological data, including long-range dependencies and large genomic contexts. The tutorial will present recent developments in the field through selected case studies, including large-scale models such as Evo2. Participants will learn how these models can support biomedical tasks such as variant effect prediction, genomic representation learning, and pathogen genome analysis. Learning objectives include: (i) understanding the key architectural differences between NLP language models and genomic foundation models, (ii) evaluating their potential applications in clinical genomics and epidemiology, and (iii) exploring practical workflows for inference, adaptation, and evaluation of genomic AI models. The tutorial combines conceptual lectures with practical demonstrations to provide participants with both theoretical foundations and hands-on exposure to emerging genomic AI methods.