Call for Papers | IEEE Transactions on Biomedical Engineering (TBME)Special Issue: Digital Twins, Simulation Based AI, and Medical World Models
Published in Bioengineering & Biotechnology
The integration of digital twins, simulation based AI, and medical world models is transforming computational medicine. This Special Issue targets rigorous advances in multimodal data fusion, patient specific modeling, and predictive simulation frameworks that enable precision diagnostics, therapy optimization, and longitudinal health monitoring.
We invite contributions spanning:
🔹 Digital twin architectures and personalization pipelines
🔹 Physics based and mechanistic physiological modeling
🔹 Simulation driven AI (e.g., PINNs, hybrid models, differentiable simulators)
🔹 Multimodal integration (imaging, genomics, physiological, behavioral data)
🔹 Clinical validation, uncertainty quantification, and explainability
🔹 In-silico trials and predictive health systems
🔹 Translational deployment and regulatory considerations
đź“ŚÂ Submission Process
Submit via the TBME portal under: “Special Issue on Digital Twins, Simulation-Based AI, and Medical World Models”
đź“©Â Letter of IntentÂ
Include: title, abstract, authors, affiliations, and scope alignment
Deadline: May 1, 2027
đź—“Â Key Dates
• Full Submission: July 1, 2027
• First Decision: September 1, 2027
• Revision Deadline: November 1, 2027
• Final Decision: January 15, 2028
• Publication: February 15, 2028
đź‘ĄÂ Guest Editors
• Dr. Jieyun Bai (Jinan University)
• Dr. Peter Hunter (University of Auckland)
• Dr. Shuo Li (Case Western Reserve University)
📣 If you are developing frameworks that unify data, models, and clinical insight, this Special Issue provides a targeted venue for high impact dissemination.
✍️ Prepare your manuscript:
https://lnkd.in/gTdrUtxV
📝 Submit your manuscript to TBME:
https://lnkd.in/ejMeAbF5
I am an Associate Professor at Jinan University, a Joint Appointed Professor at the First Affiliated Hospital of Jinan University, and the Chief Scientist for Medical Artificial Intelligence at Guangzhou Lianyin Medical Technology Co., Ltd.
I was a postdoctoral researcher at the Auckland Bioengineering Institute, University of Auckland, working with Prof. Jichao Zhao from 2018 to 2019. I received my Ph.D. in Computer Science and Technology from Harbin Institute of Technology in 2017, under the supervision of Prof. Kuanquan Wang and Prof. Henggui Zhang, and I was also a visiting scholar at the Auckland Bioengineering Institute for one year.
My research focuses on AI-driven medical digital twins, ultrasound-based maternal–fetal monitoring, multimodal biomedical signal processing, and clinical decision support systems. I have published extensively on deep learning–based medical image and signal analysis in leading international conferences, including MICCAI, ISBI, EMBC, BIBM, and CinC, as well as in top-tier journals such as Medical Image Analysis (MIA), IEEE Transactions on Medical Imaging (TMI), IEEE Journal of Biomedical and Health Informatics (JBHI), and PLOS Computational Biology.
My team is dedicated to translational and data-driven medical AI research, leveraging large-scale clinical data collected from multi-center hospital systems. We actively develop open datasets, benchmark challenges, and open-source platforms to support reproducible and clinically relevant AI research.
I am a leading organizer in the international ultrasound AI community and currently serve as an Area Chair for MICCAI and ISBI Challenges. Through these initiatives, I have coordinated global collaborations among academia, hospitals, and industry, and have released large-scale, clinically curated datasets and standardized evaluation platforms to the community.
We are strongly committed to open science and have released more than 10 public datasets and code repositories on platforms such as Kaggle and Zenodo.
My group is currently recruiting postdoctoral fellows, research scientists, and graduate students in medical image analysis, medical digital twins, and AI-enabled maternal and cardiovascular healthcare.
Researchers and clinicians interested in medical AI and translational biomedical engineering are warmly welcome to contact me.
Website: https://jieyunbai.github.io/
