A brief analysis of the application of AI in medical physics

In recent years, the application of artificial intelligence (AI) and machine learning (ML) algorithms in biomedicine has continued to grow. This growth is most evident in areas related to radiation applications and medical physics, including the publication of special issues with sections on medical physics. This growth has inadvertently led to inconsistent reporting of AI/ML research results in the literature, confusing the interpretation of their results, and eroding trust in their potential impact.

Assessing MR Artifacts

As clinical magnetic resonance (MR) imaging increases in popularity and sophistication, it becomes increasingly difficult to gain a deep understanding of the physics underlying the ever-changing technology. This is especially true for practicing radiologists, whose primary responsibility is to interpret clinical images without necessarily understanding the complex equations that describe the underlying physics. However, the physics of magnetic resonance imaging play a role in clinical practice. important role as it determines image quality, and suboptimal image quality may hinder accurate diagnosis. This article provides an image-based explanation of the physics of common MR imaging artifacts and provides simple solutions for fixing each type of artifact.

Details solutions emerging from the latest technological advances that radiologists may not yet be familiar with. The types of artifacts discussed include those produced by voluntary and involuntary patient motion, magnetic susceptibility, magnetic field inhomogeneities, gradient nonlinearities, standing waves, aliasing, chemical shifts, and signal truncation. With increased awareness and understanding of these artifacts, radiologists will be better able to modify MR imaging protocols to optimize clinical image quality, thereby increasing diagnostic confidence.

Role in Radiation Oncology

Medical physics has a long tradition of modeling biological effects in radiation oncology. High-impact examples include the quantification of dose-volume effects based on clinical data, relevant to daily radiation treatment planning and optimization, and the adaptation and use of fractionation models aimed at converting physical doses into biologically equivalent doses to tumors.

Medical physicists possess the basic physical skills to establish mathematical descriptions of biological or clinical problems and have the ability to simplify complex relationships to the greatest extent possible. In addition, medical physics training in basic mathematics, statistics, biology, and clinical aspects allows medical physicists to interact relatively easily with the professionals needed for successful interdisciplinary teams to solve modeling problems. Machine learning and artificial intelligence-based models derived from data can be useful, but require an appropriate level of understanding and extensive validation to provide sufficient confidence for clinical use.

The role of medical physicists is not just to implement artificial intelligence, but also to act as facilitators of data collection and data farming, to establish and manage advanced data sharing platforms and to contribute to new innovations such as umbrella protocols and basket trials. Methods

Conclusion

In AI/ML applications in the field of medical physics, we need to clearly state and justify the problem of using these algorithms and emphasize the innovative nature of the method. We need to briefly describe how data is divided into subsets for training, validation, and independent testing of AI/ML algorithms. Next, we need to summarize the results and statistical indicators that quantify the performance of AI/ML algorithms

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