Artificial Intelligence and the Medical Physicist: Welcome to the Machine

Abstract

: Artificial intelligence (AI) is a branch of computer science dedicated to giving machines or computers the ability to perform human-like cognitive functions, such as learning, problem-solving, and decision making. Since it is showing superior performance than well-trained human beings in many areas, such as image classification, object detection, speech recognition, and decision-making, AI is expected to change profoundly every area of science, including healthcare and the clinical application of physics to healthcare, referred to as medical physics. As a result, the Italian Association of Medical Physics (AIFM) has created the “AI for Medical Physics” (AI4MP) group with the aims of coordinating the efforts, facilitating the communication, and sharing of the knowledge on AI of the medical physicists (MPs) in Italy. The purpose of this review is to summarize the main applications of AI in medical physics, describe the skills of the MPs in research and clinical applications of AI, and define the major challenges of AI in healthcare.

A Roadmap towards Breast Cancer Therapies Supported by Explainable Artificial Intelligence

“…(1) l . The last step looks at the covariance matrix of clusters centroids, weighted by their cardinality, thus reading S (1) = µ (1) (H (1)T H (1) )µ (1)T , whose PCA leads to the new iteration by using Q (2) . We will follow the iteration cycle until the Frobenius norm ||Q (α) − Q (α+1) | | F < ε, with ε a small preset parameter equal to 0.001 in the Rdimtools package when it is not specified by the user, as in our case motivated by our use of scaled features.…”

“…The definition of patients' profiles according to an automated and data-driven procedure represents a cornerstone of personalized medicine [1,2]. This practice is already well suited to address a patient towards a diagnosis and treatment, once medical hypotheses are broadcasted through multimodal clinical data [3][4][5][6][7][8].…”

Dose Estimation by Geant4-Based Simulations for Cone-Beam CT Applications: A Systematic Review

“…The work of each neuron is entwined in a distributed manner to collectively learn from the input in order to optimize its final output [136,137]. In particular, convolutional neural network (CNN) architectures have witnessed rapid growth in medical physics [138,139]. Although CNNs were primarily used in the field of image pattern recognition [140], they can be also applied to accurately determine a dose distribution from an approximated simulated input dose [141][142][143][144][145].…”

Artificial Intelligence in Digital Pathology: What Is the Future? Part 1: From the Digital Slide Onwards

“…All this has had an important impact on the organization of work from one side and on the training of the figures involved in the activities on the other, having to prepare them to make the necessary changes to adapt them to the ever-changing job description and interactions with the tools (optics/mechatronics/informatics) in ever-more rapid obsolescence and gradually being more and more able to integrate with eHealth and mHealth [ 1 , 2 , 3 , 4 , 5 , 6 ].…”

“…This second revolution , if we leave out the era of robotic telepathology (which does not seem to have had a great impact in pathological diagnostics), had two important moments that we can call (a) the revolution of digital pathology in eHealth [ 5 ] with the possibility of accessing from the personal computer to PACS servers through virtual microscopy and (b) the revolution of digital pathology in mHealth [ 1 ] with the possibility of accessing the same servers from smartphones and tablets through a virtual microscope. As it has been highlighted by M Avanzo et al in the review [ 6 ], nowadays, AI shows (1) the potentiality to access and correlate large amount of data and (2) direct prospective in the world of diagnostics.…”