Introduction

Artificial intelligence and machine learning have shown great potential in transforming bioinformatics, biomedicine, and healthcare informatics, including clinical prediction, medical imaging, patient health records, biomedical signal processing, precision medicine, drug discovery, and healthcare decision support. However, healthcare AI systems are often deployed in dynamic, heterogeneous, high-stakes, and privacy-sensitive environments, where data distributions, patient populations, clinical workflows, medical devices, and disease patterns may change over time.

These challenges raise critical needs for learning systems that are adaptable, reliable, and responsible. Adaptable learning enables healthcare AI models to evolve under distribution shifts, new clinical environments, and changing patient populations. Reliable learning ensures robustness, uncertainty awareness, calibration, and trustworthy performance under noisy, incomplete, biased, or out-of-distribution biomedical data. Responsible learning promotes fairness, explainability, privacy preservation, safety, and ethical deployment in healthcare applications.

The proposed workshop aims to provide an informal and vibrant forum for researchers and industry practitioners to share recent advances, practical experiences, benchmark resources, and open challenges in developing trustworthy learning systems for bioinformatics, biomedicine, and healthcare informatics. The workshop is expected to attract researchers from machine learning, data mining, biomedical informatics, clinical AI, medical imaging, bioinformatics, public health, and responsible AI.

Call for papers

Important Dates

The following are the proposed important dates for the workshop. All deadlines are due 11:59 pm Pacific Time.

• Due date for workshop papers submission: Sept. 27, 2026

• Notification of paper acceptance to authors: Oct. 18, 2026

• Camera-ready deadline for accepted papers: Nov. 8, 2026

• Workshop dates: Dec. 1-4, 2026

Topics of Interest 

We encourage submissions in various degrees of progress, such as new results, visions, techniques, innovative application papers, and progress reports under the topics that include, but are not limited to, the following broad categories:

Adaptable Learning for Healthcare

• Domain adaptation and domain generalization for biomedical and healthcare applications

• Transfer learning, few-shot learning, and meta-learning for clinical prediction

• Continual, lifelong, online, and incremental learning in healthcare

• Test-time adaptation for medical imaging and biomedical data analysis

• Learning under temporal shifts in electronic health records and patient health records

• Adaptive learning from heterogeneous and multimodal healthcare data

• Personalized and patient-specific learning for diagnosis, prognosis, and treatment recommendations

• Federated and collaborative learning across healthcare institutions

• Adaptive large language models and foundation models for healthcare

• Learning with rejection, abstention, and selective prediction in clinical applications

Reliable Learning for Healthcare

• Robust machine learning for bioinformatics, biomedicine, and healthcare informatics

• Out-of-distribution detection and open-world learning for healthcare AI

• Uncertainty quantification and confidence estimation in biomedical applications

• Calibration and risk-aware prediction for clinical decision support

• Reliable medical imaging analysis and medical signal processing

• Robust learning with noisy, missing, imbalanced, or biased healthcare data

• Data quality assessment and reliable preprocessing for biomedical data

• Model monitoring, failure detection, and error analysis in clinical deployment

• Reliable multimodal learning from EHRs, images, signals, genomics, and clinical notes

• Benchmark datasets and evaluation protocols for reliable healthcare AI

Responsible Learning for Healthcare

• Fairness-aware machine learning for healthcare and biomedicine

• Bias detection and mitigation in biomedical and clinical AI systems

• Explainable and interpretable AI for clinical decision support

• Privacy-preserving learning and secure health data analytics

• Ethical and human-centered AI for healthcare applications

• Responsible development and deployment of healthcare foundation models

• Trustworthy large language models for clinical and biomedical applications

• Causal learning and counterfactual reasoning for healthcare decision-making

• AI safety, accountability, and governance in clinical environments

• Equity-aware healthcare analytics and responsible public health AI

Submission Guidelines

Please submit a full-length paper (up to 8 pages in IEEE 2-column format) through the online submission system (you can download the format instruction here: http://www.ieee.org/conferences_events/conferences/publishing/templates.html). The paper submission is double blind. Please don’t include any authors and/or affiliations in the paper.

Electronic submissions (in PDF or Postscript format) are required. Selected participants will be asked to submit their revised papers in a format to be specified at the time of acceptance.

Submit your paper online: 

https://wi-lab.com/cyberchair/2026/bibm26/scripts/submit.php?subarea=S46&undisplay_detail=1&wh=/cyberchair/2026/bibm26/scripts/ws_submit.php

Accepted papers will be posted on the workshop website AND included in the official IEEE BIBM 2026 main conference proceedings.

Attendence

For each accepted paper, at least one author must attend the conference and present the paper either in person or online via Zoom.

Keynote Speakers

TBA

Workshop Organizers