Development and implementation of a unified curriculum in math, statistics, computing, and informatics with financial considerations for medical physics graduate programs

Abstract Background AAPM Report No. 365 recommends that medical physics graduate programs offer courses covering both mathematical and statistical methods (Section 3.1.7) as well as computational methods and medical informatics (Section 3.1.8). While our program seeks to incorporate both of these essential areas into the curriculum, various financial and programmatic constraints have necessitated a more streamlined approach. Accordingly, this work presents a single 2‐semester‐hour course designed to address these topics in an integrated format. Purpose In this paper, we present our efforts in developing a new teaching approach that addresses both AAPM Report No. 365 recommendations in one course. Methods The major challenge of designing this course was the insufficient number of semester hours allocated to teaching both topics. To overcome this obstacle, we implemented a novel approach to homework assignments. Unlike the traditional approach in which students complete homework manually, students in this class were asked to write computer programs to solve most homework questions. These carefully designed assessments not only enhanced students’ understanding of the course materials but also required them to utilize appropriate computational methods. Recognizing that students had varying levels of coding experience from their undergraduate studies, the program instructed them, prior to starting the program, to acquire foundational Python skills through self‐guided learning to prepare for this course. In addition, basic Python programming guidance was provided with each homework assignment to support students with less coding experience. Results The final course covered the following key mathematical concepts: signals and systems, Fourier series and transform, probability, statistical inference, image quality, optimization methods, and an introduction to artificial intelligence with an emphasis on machine learning. To complete the homework assignments, students developed coding skills in data visualization, numerical integration, convolution, continuous‐time/discrete‐time/fast Fourier transforms, random number generator, point estimation, confidence interval, hypothesis testing, linear models, DICOM, the Rose model, conjugate‐gradient descent, iterative methods for solving systems of linear equations, and support vector machines. The course was offered in the Spring 2024 and Spring 2025 semesters and received generally positive evaluations, with some noted challenges per the Course and Teacher Rating reports. Overall, students reported that the course was educationally beneficial; however, some indicated that the coding‐based assignments were demanding. Conclusion We successfully developed and implemented a course that covers mathematical and statistical methods as well as computational methods and medical informatics as recommended in AAPM Report No. 365.