Special Issue on Machine Learning in Medical Device Technology
Journal of Medical Devices
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This Special Issue of the ASME Journal of Medical Devices will be a collection of original research that highlights the use of ML techniques for the design, development, and implementation of novel medical devices and technologies spanning the translational spectrum. Manuscripts submitted for this Special Issue should be at the intersection of medical devices and ML, where conventional imaging-related, algorithmic and other “software only” approaches are considered to be outside the scope of this issue. All avenues of experimental research from proof-of-concept demonstration to exhaustive assessment of new devices are encouraged. Due to the emerging nature of the topic, shorter communications or preliminary studies will also be considered. A small number of review articles providing a comprehensive introduction to the topic will be considered following prior discussion with the handling editors.
Topic Areas
THE SCOPE OF THIS ISSUE INCLUDES BUT IS NOT LIMITED TO:- Integration of ML in medical device design such as parametrization of device design
- ML-assisted optimization of therapeutic parameters
- ML-enabled medical devices for applications such diagnostic assistance, imaging, and health monitoring
- ML for wearable devices
- ML for drug delivery technology and devices
- ML for predicting therapeutic response or adverse effects of medical devices
Submission Instructions
Papers should be submitted electronically to the journal through the ASME Journal Tool. If you already have an account, log in as an author and select Submit Paper. If you do not have an account, you can create one here.Once at the Paper Submittal page, select the Journal of Medical Devices and then under the Special Issue field, select Special Issue on Machine Learning in Medical Device Technology.
Papers received after the deadline or papers not selected for the Special Issue may be accepted for publication in a regular issue.
Guest Editors
Dr. Zhenpeng Qin, Associate Professor, Mechanical Engineering, The University of Texas at Dallas, USA, (zhenpeng.qin@utdallas.edu)
Dr. Govind Srimathveeravalli, Assistant Professor, Mechanical Engineering, University of Massachusetts Amherst, USA (govind@umass.edu)
Dr. Boris Rubinsky, Professor, Mechanical Engineering, University of California Berkeley, USA (rubinsky@berkeley.edu)