Tutorials

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:

Tutorials
(& Doctoral Consortium)

July 8-9, 2026:

AIME Main Conference

July 10, 2026:

Workshops

Tutorial Program - July 7

CRX building

Time/Location	TBD	TBD	TBD	TBD	TBD
Morning	Interpretable AI – Part I	AI for Imaging I	Responsible AI I	Evaluation of AI	Advanced Topics I
Afternoon	Interpretable AI – Part II		Responsible AI II		Advanced Topics II

Topic	Title	Duration
Interpretable AI	T1. Interpretable AI and Evolving Knowledge in Medicine: From Probabilistic Reasoning and Graphs to Dynamic, Explainable Clinical Systems	7 hours
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: T3 (Artificial Intelligence Pipelines for Medical Imaging: From Preprocessing to Deep Learning in fMRI and CT), originally in the “AI for Imaging II” slot, was cancelled by the tutorial organizers.

T1. Interpretable AI and Evolving Knowledge in Medicine: From Probabilistic Reasoning and Graphs to Dynamic, Explainable Clinical Systems

Amit Kumar and Nidhi Malik.

Full day.

The tutorial provides the participants with a clear way of understanding how to get to self-evolving knowledge representations based on interpretable AI, focusing on medicine and healthcare: how AI systems represent knowledge, how AI systems deal with uncertainty, and how AI systems revise as new evidence and guidelines come along. This is intended to facilitate the clinical decision support, medical knowledge bases, and evidence-based practice through the availability of ideas in probabilistic inference, graph-based intelligence, and dynamic knowledge representation and connect them to medical applications. We will take our step through developing what AI in medicine actually implies, beginning with a simple case, and building up with the notions of probability and uncertainty. Then we will discuss graphs, and knowledge graphs and indicate how these relate to medical systems, such as SNOMED and ICD. Lastly, we will discuss medical knowledge as dynamic, and the ways in which AI can be made more interpretable and flexible in clinical environments by the creation of time-sensitive and geometry-informed models.

T2. From Radiomics to Dosiomics: patterns, tools and challenges

Roberto Gatta, Unai Pérez Goya, Amaia Gastearena Irigoyen and Paola Jablonska.

Half day.

The need for diagnostic and prognostic predictive models based on radiological and nuclear medicine imaging provided the fertile ground for the emergence of the field known as Radiomics. Radiomics has evolved while continuously addressing several critical aspects, including: (a) the protocols adopted in radiology for storing medical images, (b) the signal preprocessing , (c) the research on machine learning techniques to improve performance, (d) issues related to model validation, reproducibility, and replicability of results. In addition, Radiomics is also expanding beyond traditional radiology departments, moving toward technologically related domains such as radiation therapy. Here, the integration of Radiomics with Theragnomics appears to provide a solid foundation for the emergence of a new discipline: Dosiomics. This tutorial is an introduction to the main concepts and challenges behind radiomics and dosomics, with a hands-on example.

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.

Large language models are rapidly entering health research workflows and understanding what it means to utilize AI for qualitative analysis (such as when these tools help, where they fail, and how to use them responsibly) has become important. This half-day hands-on tutorial introduces a human-in-the-loop workflow for AI-assisted qualitative data analysis using the Consolidated Framework for Implementation Research (CFIR). Participants will (1) apply CFIR to structure deductive coding of de-identified transcripts, (2) compare manual coding with outputs from free and higher-capability large language models, (3) refine prompts to improve transparency and reproducibility, (4) adjudicate disagreements and recognize predictable failure modes such as missed codes, over-assignment, and shallow rationales, and (5) identify ethical safeguards for privacy, bias, documentation, and human verification. Through live demonstrations and guided exercises, participants will leave with a reusable prompt scaffold, a practical framework for when AI helps or harms, and an ethics checklist they can adapt to their own projects.

T5. CARE-AI: Framework Contextual, Accountable, and Responsible Ethics for Artificial Intelligence in Healthcare

Lyn Sonnenberg, Jerry Maniate.

Half day.

Artificial intelligence (AI) is rapidly transforming health professions education, research, and care. While enthusiasm is high, many educators and leaders struggle with how to implement AI responsibly and equitably. Existing ethical frameworks often remain abstract and difficult to apply in the day-to-day realities of academic medicine. To address this challenge, we developed and validated the Health CARE-AI Framework, a set of nine principles for responsible AI in health contexts, alongside a practical implementation toolkit that supports their integration into teaching, clinical decision-making, research oversight, and administrative leadership. This tutorial provides participants with a structured opportunity to engage with these principles, explore case based applications, and consider how to adapt the implementation guide and toolkit to their own institutional and professional settings. By the end of this session, participants will be able to: Describe the nine CARE-AI principles and their underlying rationale; Apply these principles to analyze AI-related scenarios in health professions education, research, and care; Identify ethical and equity considerations in their own contexts where AI is being introduced or scaled; Adapt components of the CARE-AI Toolkit to support responsible AI integration within their professional roles and institutions.

T6. Appraise and Stress-Test Clinical AI: Calibration, Dataset Shift, and Post-Deployment Monitoring

Fares Alahdab, Randi Foraker and Anwar Chahal.

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.

T7. 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.

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.

T8. 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.