Expanding the medical physicist curricular and professional programme to include Artificial Intelligence

Purpose: To provide a guideline curriculum related to Artificial Intelligence (AI), for the education and training of European Medical Physicists (MPs). Materials and methods: The proposed curriculum consists of two levels: Basic (introducing MPs to the pillars of knowledge, development and applications of AI, in the context of medical imaging and radiation therapy) and Advanced. Both are common to the subspecialties (diagnostic and interventional radiology, nuclear medicine, and radiation oncology). The learning outcomes of the training are presented as knowledge, skills and competences (KSC approach). Results: For the Basic section, KSCs were stratified in four subsections: (1) Medical imaging analysis and AI Basics; (2) Implementation of AI applications in clinical practice; (3) Big data and enterprise imaging, and (4) Quality, Regulatory and Ethical Issues of AI processes. For the Advanced section instead, a common block was proposed to be further elaborated by each subspecialty core curriculum. The learning outcomes were also translated into a syllabus of a more traditional format, including practical applications. Conclusions: This AI curriculum is the first attempt to create a guideline expanding the current educational framework for Medical Physicists in Europe. It should be considered as a document to top the sub-specialties' curriculums and adapted by national training and regulatory bodies. The proposed educational program can be implemented via the European School of Medical Physics Expert (ESMPE) course modules and - to some extent - also by the national competent EFOMP organizations, to reach widely the medical physicist community in Europe.Peer reviewe

2022 SOARS Conference Program

Program for the 2022 Showcase of Osprey Advancements in Research and Scholarship (SOARS)

States’ Rights to Protect Gun-Owning Patients from Politicized Physician Speech

The article examines the U.S. states\u27 obligations to protect patients\u27 best interests and evaluates physicians\u27 free speech rights in the patient-physician relationship

Autonomous Weapons Systems and the Moral Equality of Combatants

To many, the idea of autonomous weapons systems (AWS) killing human beings is grotesque. Yet critics have had difficulty explaining why it should make a significant moral difference if a human combatant is killed by an AWS as opposed to being killed by a human combatant. The purpose of this paper is to explore the roots of various deontological concerns with AWS and to consider whether these concerns are distinct from any concerns that also apply to long- distance, human-guided weaponry. We suggest that at least one major driver of the intuitive moral aversion to lethal AWS is that their use disrespects their human targets by violating the martial contract between human combatants. On our understanding of this doctrine, service personnel cede a right not to be directly targeted with lethal violence to other human agents alone. Artificial agents, of which AWS are one example, cannot understand the value of human life. A human combatant cannot transfer his privileges of targeting enemy combatants to a robot. Therefore, the human duty-holder who deploys AWS breaches the martial contract between human combatants and disrespects the targeted combatants. We consider whether this novel deontological objection to AWS forms the foundation of several other popular yet imperfect deontological objections to AWS

Spartan Daily, November 2, 1994

Volume 103, Issue 44https://scholarworks.sjsu.edu/spartandaily/8614/thumbnail.jp

Predictive Analytics in the Criminal Justice System: Media Depictions and Framing

Artificial intelligence and algorithms are increasingly becoming commonplace in crime-fighting efforts. For instance, predictive policing uses software to predetermine criminals and areas where crime is most likely to happen. Risk assessment software are employed in sentence determination and other courtroom decisions, and they are also being applied towards prison overpopulation by assessing which inmates can be released. Public opinion on the use of predictive software is divided: many police and state officials support it, crediting it with lowering crime rates and improving public safety. Others, however, have questioned its effectiveness, citing civil liberties concerns as well as the possibility of perpetuating systemic discrimination. According to the Prison Policy Initiative, over 2.3 million Americans were incarcerated in 2017 [1]. Of this population, 60 per cent were made up of people of color. African-American men are disproportionately targeted by the U.S. judicial system; they are more likely to be stopped and frisked by police, as well as receive stiffer sentences than white men for the same crimes [2]. In light of these facts, using algorithms and predictive methods to make decisions-especially ones that may affect the freedom of individuals-requires further study. Investigating the increasingly intertwined relationship between technology and human liberties can help develop a better understanding of how artificial intelligence can help make lives more efficient and the judicial system more transparent. The news media plays a significant role in shaping opinions on controversial issues. Articles and reports on predictive policing not only inform the public, but they also influence how people perceive the use of artificial intelligence in law enforcement, and ultimately how we, as citizens, want to be policed. This study evaluates the role of news media in shaping public opinion on two fronts: (a) the use of predictive analytics in the justice system, and (b) the integration of artificial intelligence in everyday life. Working with a corpus of articles from major journalistic outlets, we apply a qualitative methodology based on grounded theory to identify the key frames that govern media representation of predictive policing. This study makes the following contributions: - A survey of current predictive policing techniques, including hot spot analysis, regression methods, near-repeat, and spatiotemporal analysis - Application of grounded theory methods to a qualitative analysis of a corpus of 51 online articles on the U.S. criminal justice system\u27s use of predictive software and algorithms - Identification of two frames most commonly adopted by elite journalists writing for national news outlets Two dominant frames were identified from a corpus of 51 articles: fear of the future and fear of the past. The first frame elaborates on the potential consequences of implementing predictive algorithms in policing efforts, using specific examples to emphasize the difficulty of removing bias from software systems and the likelihood of perpetuating racial discrimination. The second frame argues that using data effectively can help combat rising crime rates, especially in metropolitan areas like Chicago and New York City. It bolsters its claim by attributing the ability of using predictive analytics to forecast crime as well as national threats before they happen - it focuses on preventing crime as opposed to combating it

Yale Medicine : Alumni Bulletin of the School of Medicine, Autumn 2009

This is the Autumn 2009 issue of Yale Medicine: alumni bulletin of the School of Medicine, v. 44, no. 1. Prepared in cooperation with the alumni and development offices at the School of Medicine. Earlier volumes are called Yale School of Medicine alumni bulletins, dating from v.1 (1953) through v.13 (1965).https://elischolar.library.yale.edu/yale_med_alumni_newsletters/1026/thumbnail.jp