Bernhard Kainz Appointed as Senior Area Editor of IEEE TRANSACTIONS ON MEDICAL IMAGING
Bernhard Kainz (Senior Member, IEEE) has been appointed as Senior Area Editor for IEEE TRANSACTIONS ON MEDICAL IMAGING (TMI), the premier scientific journal in the field of medical image analysis. In his new role, Professor Kainz will be responsible for the initial screening and triage of submissions, determining which manuscripts are forwarded to Associate Editors and Reviewers for further assessment. This is a critical position at a time when the journal is experiencing a tremendous surge in submissions – far exceeding the capacity of the current review system.
As the leading publication for original contributions on medical imaging, TMI welcomes manuscripts covering a broad spectrum of topics. These include imaging of body structure, morphology and function, cell and molecular imaging, and all forms of microscopy. The journal encourages innovative work on imaging modalities such as ultrasound, x-rays, magnetic resonance, radionuclides, microwaves, and optical methods. Contributions that advance novel acquisition techniques, medical image processing and analysis, visualization, pattern recognition, machine learning, and related methods are particularly sought after. Research that bridges the sciences of medicine, biology, and imaging – emphasizing the integration of instrumentation, hardware, software, mathematics, physics, biology, and medicine – is at the heart of TMI’s mission.
For more information on the editorial board and to explore opportunities for collaboration, please visit the TMI Editorial Board page.
In response to the recent increase in manuscript submissions, TMI is actively seeking motivated reviewers with a proven track record. We encourage qualified experts to join our team of reviewers by completing the following form:
Introducing the AI for TMI (AI4TMI) Initiative
In tandem with this appointment, TMI is excited to launch the AI for TMI (AI4TMI) initiative. Announced on February 6, 2025, this forward-looking program aims to harness artificial intelligence to enhance both the quality and efficiency of the peer review process. Key goals include:
- Optimizing Editorial Workflow: Implementing AI tools to assist in manuscript triage, ensuring accurate matching with handling editors and reviewers.
- Performance Analytics: Utilizing machine learning algorithms to evaluate the performance of associate editors and reviewers, thereby enhancing accountability.
- Supporting Decision-Making: Experimenting with large multimodal foundation models to support editorial decisions while upholding the indispensable role of human judgment.
- Ensuring Data Security: Prioritizing data privacy and security in all AI applications and maintaining compliance with best practices.
- Fostering Community Engagement: Collaborating with authors, reviewers, and the broader scientific community to share insights, outcomes, and best practices in AI integration.