Description
In this class, we present a broad overview of the field of medical software. You will learn from Yale professors and a series of industry experts who connect the course concepts to their real world applications.
We begin by discussing medical device regulatory structures, data privacy and cybersecurity regulations, and key support technologies such quality management systems and risk management. We then take a detailed look at the medical software life cycle, starting with identifying user needs, mapping these to system requirements and then the process of software design, coding, testing and validation. We continue by exploring issues related to the use of machine learning techniques in medical applications from both a software engineering and a regulatory perspective. We conclude with a discussion of the business and management issues in this area focusing on the impact on digital health on modern healthcare and issues related to starting new ventures.
This is a class aimed at either third- or fourth-year undergraduates in computer science, biomedical engineering, and related fields, and to junior software engineers currently either working in, or interested in moving to, the medical device industry.
For more detailed information about the course, please refer to the course project page maintained by the instructor here.
Course Takeaways
- How medical devices and software are regulated around the world. How medical software is designed, developed, and implemented in the real world.
Meet the Instructors
I have twenty five plus years of experience in medical image analysis, machine learning, and software development. I have been involved in a variety of imaging projects ranging from cardiac image analysis, image-guided epilepsy neurosurgery, image-guided prostate biopsy, development of methods for real-time fMRI, vascular image analysis and general neuroimaging analysis. We have used both model-based approaches (biomechanical and physiological models) and more data-driven statistical/machine learning approaches. These projects spanned most of the imaging modalities (MRI, CT, Ultrasound, PET, SPECT, Optical), body parts (brain, head, heart, vasculature, prostate, abdomen, hindlimbs) and a variety of species.
In addition to algorithm research, I have been heavily involved in the development of medical image analysis software. My software work (which is directly linked to the image analysis research) has focused on the creation of tools for image analysis both at Yale and as a consultant for industry. My early work (1990s) used C++/Motif/OpenInventor on Silicon Graphics workstations. Later I used C++/Tcl/VTK as part of the creation of the original Yale BioImage Suite software package. More recently, I have focused on the creation of web-based tools using a combination of JS and C++ (via WebAssembly) to create server-less tools that can be run in a browser. Some of the C++ algorithms are also made available for use in Python and MATLAB scripts.
In addition to actual software development, I teach a class on Medical Software at Yale which formed the basis for our recently released textbook “Introduction to Medical Software: Foundations for Digital Health, Devices and Diagnostics” that was just published by Cambridge University Press (Summer 2022) and a Coursera online class titled “Introduction to Medical Software” that was released in October of 2021 and which currently has more than 26000 enrolled students from all over the world. I also direct the Yale Certificate Program in Medical Software and Medical AI. Finally, I am a member of a number of technical standards committees at the Association for the Advancement of Medical Instrumentation (AAMI) on software and artificial intelligence.
Full Biography
