BIBM 2026 Workshop Translational AI

Full online workshop at IEEE BIBM 2026

The 2nd International Workshop on Translational AI for Real-World Healthcare and Biomedicine

A focused forum for AI methods, systems, evaluation frameworks, and applied studies that bridge biomedical research with practical healthcare impact.

Important Dates Explore Topics
01 / Introduction

Turning promising medical AI into dependable real-world practice.

Artificial intelligence has become a key driver of innovation in healthcare and biomedicine, enabling advances across disease diagnosis, risk prediction, treatment planning, and population health analysis.

Despite rapid progress in machine learning and data-driven modeling, many proposed methods remain difficult to translate into clinical and biomedical practice. Translational AI addresses this gap through robust data integration, meaningful evaluation on real-world datasets, and alignment with clinical and biomedical use cases.

The workshop brings together machine learning, biomedical informatics, clinical research, and digital health communities to discuss practical AI solutions for healthcare and biomedical decision making.

02 / Research Topics

Areas covered by the workshop

Translational AI methods for real-world healthcare and biomedical applications
Machine learning and data-driven models for electronic health records and clinical data
Multimodal data integration across clinical, imaging, omics, and sensor data
AI methods for medical imaging, biomedical signal processing, and time-series analysis
Foundation and large-scale models for healthcare and biomedicine in practical settings
Knowledge-enhanced and data-driven approaches for healthcare analytics
Clinical decision support systems and AI-assisted healthcare workflows
Model evaluation, validation, and benchmarking using real-world healthcare data
Generalization, robustness, and adaptation of AI models across sites and populations
AI systems for disease risk prediction, prognosis, and patient stratification
Personalized and population-level analytics for healthcare and public health
Deployment-oriented AI, including monitoring, maintenance, and model updating
Privacy-preserving, federated, and collaborative learning for healthcare data
Explainability, reliability, and uncertainty modeling in medical AI systems
Ethical, regulatory, and practical considerations in translational healthcare AI
03 / Timeline

Important dates

  1. Due date for full workshop papers submission
  2. Notification of paper acceptance to authors
  3. Camera-ready of accepted papers
  4. Workshops
04 / Leadership

Program Chairs

Dr. Sibo Qiao

Tiangong University, China

siboqiao@tiangong.edu.cn Google Scholar

Associate Professor with the School of Software, Tiangong University, Tianjin, China. His work spans biomedical informatics, intelligent systems, and applied AI, with publications in venues including IEEE TITS, IEEE IoTJ, IEEE JBHI, IEEE TCE, IEEE TMC, IEEE TAI, IEEE TII, and IEEE TNSM.

Dr. Mohammad Shabaz

Marwadi University, India

md.shabaz@marwadieducation.edu.in Google Scholar

Assistant Professor (Senior Scale) at Marwadi University, India. He works on computer science applications in interdisciplinary domains, with major contributions in healthcare AI, extensive editorial service, patents, and highly cited research output.

05 / Committee

Program Committee Members

Shuqiang Wang University of Chinese Academy of Sciences, China
Shudong Wang China University of Petroleum, China
Shanchen Pang China University of Petroleum, China
Joel J. P. C. Rodrigues Federal University of Piaui, Brazil
Shalli Rani Chitkara University Institute of Engineering and Technology, India