Fairness of AI in Medical Imaging

TL;DR

Joint FAIMI, BRIDGE, and EPIMI workshop at MICCAI 2026 (Strasbourg, France)
Date: Sunday, September 27, 2026
Location: The Berlin Room, Strasbourg Conference Centre (in person only)
Program chairs: Emma Stanley (Imperial College, UK), Louisa Fay (Stanford University, USA), Christopher Boland (DTU Compute, Denmark), Gelei Xu (University of Notre Dame, USA)
Full joint organizer list will be posted here as soon as it is finalized

Workshop Overview

FAIMI has again been selected to host a workshop at MICCAI 2026. This year, the workshop is being organized jointly with BRIDGE (Regulatory Evaluation) and EPIMI (Ethics & Philosophy).

The joint workshop brings together three connected perspectives needed for trustworthy medical AI:

FAIMI: fairness, bias identification, and bias mitigation in medical imaging AI
BRIDGE: regulatory science and evaluation for emerging AI paradigms
EPIMI: ethical and philosophical analysis of new challenges in medical imaging AI

At present, the organizer list below reflects the FAIMI team coordinating this page and the FAIMI-led workshop activities. The full combined FAIMI + BRIDGE + EPIMI organizer list will be added here once confirmed.

Tentative Schedule

Preliminary programme — session details and timings are subject to change.

Time	Programme
10:30 – 10:35	Opening Remarks
10:35 – 11:15	Keynote 1Algorithmic fairness is harder than you think — Aasa Feragen, DTU Compute
11:15 – 12:05	Oral Session 1Understanding nuanced disparities Intersectional Disentangling of Temporal and Acquisition Bias in Fetal Ultrasound Understanding Sources of Demographic Predictability in Brain MRI via Disentangling Anatomy and Contrast On Evaluating Subgroup Discovery in Medical Image Classification From Fairness Findings to Fairness Claims: An Evidence Classification Scheme for Clinical AI
12:05 – 12:30	Poster Highlight Talks
12:30 – 13:30	Lunch
13:30 – 14:10	Keynote 2Surviving the Real World: Can AI Fairness, Ethics, and Safety Make It to the Bedside? — Florence Doo, University of Maryland
14:10 – 15:00	Oral Session 2Issues in bridging research and deployment Equal Accuracy, Unequal Agreement: Auditing Clinician–AI Agreement Fairness in Lung Nodule CT False Confidence: Automated Labels Confound Fairness Audits in Cervical Spine Segmentation When Oracle Conditioning Misleads Deployment: Conditioning-Availability Bias in Echocardiographic Segmentation Rethinking Explainability for Clinical Trust: A Task-Specific Communicative Layer
15:00 – 16:00	Poster Session / Break
16:00 – 16:50	Oral Session 3Issues with foundation models and federated learning Gradient Erasure and Contributory Injustice in Federated Medical Imaging AI Look What the Probes Dragged In! Real-World Chest X-ray Shortcuts in MedCLIP Subgroup performance analysis of adaptation strategies for chest X-ray foundation models Foundational values for foundation models
16:50 – 17:50	FAIMI / BRIDGE / EPIMI Round Table Discussion
17:50 – 18:00	Awards and Closing

Poster Session

Theme 1: Demographic Fairness & Intersectional Disparities

Contrast-Induced Class Overlap as a Fairness Bottleneck in Dermatological AI: Evidence from HAM10000
When Fairness Transfer Backfires: Dark-Skin Inversion in Dermatology Foundation Models and a Minimal In-Distribution
Subgroup performance analysis of adaptation strategies for chest X-ray foundation models
Beyond Predictive Fairness: Quantifying Attribution Consistency Across Demographic Groups in Diabetic Retinopathy Screening
Understanding Sources of Demographic Predictability in Brain MRI via Disentangling Anatomy and Contrast
An Intersectional Fairness-Aware Framework for Alzheimer’s Disease Detection Using Multimodal Data
Intersectional Disentangling of Temporal and Acquisition Bias in Fetal Ultrasound
Investigating Sex and Ethnicity Bias in Deep Learning-based Echocardiography Image Segmentation

Theme 2: Model Robustness & Deployment Risks

When Oracle Conditioning Misleads Deployment: Conditioning-Availability Bias in Echocardiographic Segmentation
Marginal Coverage Hides a Reduced-Ejection-Fraction Reliability Gap: Severity-Conditional Conformal Intervals on Public Echocardiography
Loss-Conditioned Utility-Fairness Boundary Modeling for Medical Imaging
Multiple Shortcut Pathways in Mammography-Based Breast Cancer Risk Prediction
Look What the Probes Dragged In! Real-World Chest X-ray Shortcuts in MedCLIP
Counterfactual Stress Testing for Image Classification Models
Hallucinations and constraints: Regulating surgical workflow recognition beyond accuracy

Theme 3: Human-AI Interaction & Values

Foundational values for foundation models
Gradient Erasure and Contributory Injustice in Federated Medical Imaging AI
What is AI to Us: Exploring Patient Values in Integrating AI for Epilepsy Management
Futures Before Failures: Design Fictions for Anticipatory Fairness in Surgical AI

Theme 4: Auditing Frameworks

Rethinking Explainability for Clinical Trust: A Task-Specific Communicative Layer
On Evaluating Subgroup Discovery in Medical Image Classification
From Fairness Findings to Fairness Claims: An Evidence Classification Scheme for Clinical AI
Equal Accuracy, Unequal Agreement: Auditing Clinician–AI Agreement Fairness in Lung Nodule CT
False Confidence: Automated Labels Confound Fairness Audits in Cervical Spine Segmentation

Call for Papers

We invite the submission of papers for

FAIMI: The MICCAI 2026 Workshop on Fairness of AI in Medical Imaging.

Submit via OpenReview

Over the past several years, research on fairness, equity, and accountability in the context of machine learning has extensively demonstrated ethical risks in the deployment of machine learning systems in critical infrastructure, such as medical imaging. The FAIMI workshop aims to encourage and emphasize research on and discussion of fairness of AI within the medical imaging domain. We therefore invite the submission of papers, which will be selected for oral or poster presentation at the workshop. Topics include but are not limited to:

Assessment of bias in ML applications for medical imaging
Healthcare inequalities and/or the role of ML in addressing these
Discussion of definitions of fairness in medical contexts
Discussion of applicability of fairness in medical contexts
Bias mitigation strategies for ML/medical imaging
Ethics of fairness for medical imaging and medicine
Legal/regulatory considerations of fairness
Causality and fairness in medical imaging
The interplay of algorithmic and dataset bias

The workshop proceedings will be published in the MICCAI workshops volumes of the Springer Lecture Notes Computer Science (LNCS) series. Papers should be anonymized and at most 8 pages plus at most 2 extra pages of references using the LNCS format. The review process is conducted in a double-blind manner, following MICCAI standards. Submissions are made via OpenReview.

Following the MICCAI paper submission guidelines, the submission of additional supplementary material is possible.

NEW Supplementary materials are limited to multimedia content (e.g., videos) as warranted by the technical application (e.g. robotics, surgery, ...). These files should not display any proofs, analysis, or additional results, and should not show any identification markers either. Violation of this guideline will lead to desk rejection. PDF files may not be submitted as supplementary materials in 2026 unless authors are citing a paper that has not yet been published. In such a case, authors are required to submit an anonymized version of the cited paper.

All supplementary material must be self-contained and zipped into a single file. Only the following formats are allowed: avi, mp4, wmv. We encourage authors to submit videos using an MP4 codec such as DivX contained in an AVI. A README text file must be included with each video specifying the exact codec used and a URL where the codec can be downloaded.

While the reviewers will have access to such supplementary material, they are under no obligation to review it, and the paper itself must contain all necessary information and illustrations for review purposes.

Dates

All times are 23:59 Pacific Time

Full Paper Deadline: July 6, 2026

Notification of Acceptance: July 31, 2026

Camera-ready Version: August 15, 2026

Workshop: Sunday, September 27, 2026, The Berlin Room, Strasbourg Conference Centre

FAIMI-BRIDGE-EPIMI Workshop Award

A FAIMI-BRIDGE-EPIMI Workshop Award will be presented at the workshop, generously sponsored by Heartflow.

FAIMI Horizon Award

The FAIMI Horizon Award aims to provide financial support and highlight one talented early-career researcher attending the conference for the first time, with a special focus on individuals from diverse and underserved backgrounds.

The award offers a full scholarship to attend the FAIMI Workshop, creating a valuable opportunity to present research, engage with the global community, and build lasting professional networks.

Eligibility Criteria:

To be eligible for the FAIMI Horizon Award, the first author of the submitted paper must meet all of the following:

The candidate must be a first-time attendee of the MICCAI conference.
The candidate must be an early-career researcher, defined as a current student (undergraduate, master’s, PhD, or medical student), within two years of completing their terminal degree (i.e., MICCAI Young Scientists), or within four years of completing their terminal degree (i.e., Early-Career Scientists).
The candidate should identify with one or more diverse or underserved backgrounds, such as being from an underrepresented gender or racial/ethnic group, having differing abilities, coming from a low- or lower-middle-income country, or residing in a country historically underrepresented at MICCAI. Candidates with other forms of social, cultural, or economic disadvantage are also encouraged to apply.

Please note: The MICCAI Registration and Travel Grants are intended to support in-person participation in the conference. If a selected grantee is unable to attend in person, the award will be offered to the next eligible candidate.

Current FAIMI Organizers

Aasa Feragen, DTU Compute, Technical University of Denmark
Andrew King, King’s College London
Ben Glocker, Imperial College London
Enzo Ferrante, CONICET, Universidad Nacional del Litoral
Eike Petersen, Fraunhofer Institute for Digital Medicine MEVIS
Esther Puyol-Antón, HeartFlow and King’s College London
Melanie Ganz-Benjaminsen, University of Copenhagen & Neurobiology Research Unit, Rigshospitalet
Veronika Cheplygina, IT University Copenhagen
Louisa Fay, Stanford University
Emma Stanley, Imperial College London
Gelei Xu, University of Notre Dame
Christopher Thomas Boland, DTU Compute, Technical University of Denmark
Tareen Dawood, DTU Compute, Technical University of Denmark

Contact

Please direct any inquiries related to the workshop or this website to faimi-organizers@googlegroups.com.