12,060 research outputs found
A three-dimensional electronic report of a venous echo color Doppler of the lower limbs: MEVeC®
No full textAldo Innocente Galeandro,1 Pietro Scicchitano,2 Annapaola Zito,2 Cristina Galeandro,2 Michele Gesualdo,2 Francesco Ciciarello,3 Annagrazia Cecere,2 Andrea Marzullo,4 Vincenzo Contursi,5 Annamaria Annicchiarico,1 Marco Matteo Ciccone2 1Department of Science and Technology, University of Bari, Bari, Italy; 2Cardiovascular Diseases Section, Department of Emergency and Organ Transplantation (DETO), University of Bari, Bari, Italy; 3Department of Cardiovascular, Respiratory, Geriatric and Morphologic Sciences of "Umberto I" Polyclinic of Rome, "Sapienza" University, Rome, Italy; 4Department of Emergency and Organ Transplantation (DETO), Pathology Division, Medical School, University of Bari, Bari, Italy; 5Italian Society for Interdisciplinary Primary Care, Bari, Italy Background: The reports of ultrasound evaluation of lower limb veins are difficult to understand by general practitioners (GPs) and physicians who are not specialized. We developed software for a three-dimensional (3D) electronic report of venous hemodynamic mapping (MEVeC®) in order to represent lower limb venous vasculature in a 3D way. The aim of the study is to compare the novel 3D report with the standard report. Methods: Thirty subjects (medical students and GPs) evaluated a standard report and a novel 3D report of the lower limb veins of a prespecified patient. The cases were randomly and blindly taken from an archive of 100 cases. GPs and students answered a questionnaire made up of 13 questions that were structured in order to investigate the readability and comprehension of the two reports. A score ranging from 0 to 10 (0= not understandable; 10= full comprehension) was attributed to each report for each question according to the readability of the venous scheme proposed. Results: The scores from each question of the questionnaire were compared. The 3D report (MEVeC®) obtained higher scores than those from the evaluation of the standard report (P<0.0001). Each question revealed the superiority of the 3D report (MEVeC®) as compared with the standard report of the ultrasound evaluation of lower limbs. When dividing the scores according to percentiles, the 3D report (MEVeC®) still continued to show more readability than the standard report in a statistically significant way (P<0.0001). Conclusion: The new 3D report (MEVeC®) concerning ultrasound evaluation of lower limb veins is more reproducible than the standard report when evaluated by medical physicians not specialized in the evaluation of the vein tree of lower limbs. Keywords: vein 3D report, ultrasound evaluation, lower limb veins, standard repor
Human-AI Collaboration in Academic Writing: towards a Synergy Model and A Case to Include AI as a Co-Author
No full textAs generative AI systems such as ChatGPT and Gemini 2.5 become increasingly integrated into academic workflows, the question of their legitimacy, limitations, and potential in scholarly writing has become urgent. This paper presents a reflexive case study of a sustained collaboration between a domain expert in consciousness studies and Gemini 2.5, culminating in the co-authorship of a peer-reviewed research article. By analyzing exactly 37,440 words of recorded interactions, we identify patterns of synergy, including recursive refinement, conceptual amplification, and accelerated manuscript development. We argue that when guided by a knowledgeable human author, AI can act as a cognitive partner rather than a passive tool—amplifying scholarly creativity and improving efficiency without compromising academic rigor. The case supports a '1+1=3' synergy model for co-authorship, in which human steering and AI fluency converge to produce novel insights and polished output faster and more effectively than either could achieve alone. The findings advocate for a paradigm shift from prohibitive policies to the responsible, expert-guided integration of AI in academic research and writing, grounded in transparency and accountability, and present arguments for why the AI tool should be listed as a co-author despite current injunctions against such practice
La Flebologia Emodinamica
No full textLo studio emodinamico flebologico degli arti inferiori non può prescindere da un approccio biomeccanico e successivamente posturologico del soggetto flebopatico
Meaningful human control: actionable properties for AI system development
No full textHow can humans remain in control of artificial intelligence (AI)-based systems designed to perform tasks autonomously? Such systems are increasingly ubiquitous, creating benefits - but also undesirable situations where moral responsibility for their actions cannot be properly attributed to any particular person or group. The concept of meaningful human control has been proposed to address responsibility gaps and mitigate them by establishing conditions that enable a proper attribution of responsibility for humans; however, clear requirements for researchers, designers, and engineers are yet inexistent, making the development of AI-based systems that remain under meaningful human control challenging. In this paper, we address the gap between philosophical theory and engineering practice by identifying, through an iterative process of abductive thinking, four actionable properties for AI-based systems under meaningful human control, which we discuss making use of two applications scenarios: automated vehicles and AI-based hiring. First, a system in which humans and AI algorithms interact should have an explicitly defined domain of morally loaded situations within which the system ought to operate. Second, humans and AI agents within the system should have appropriate and mutually compatible representations. Third, responsibility attributed to a human should be commensurate with that human’s ability and authority to control the system. Fourth, there should be explicit links between the actions of the AI agents and actions of humans who are aware of their moral responsibility. We argue that these four properties will support practically minded professionals to take concrete steps toward designing and engineering for AI systems that facilitate meaningful human control.Interactive IntelligenceDesign AestheticsCyber SecurityHuman-Robot InteractionEthics & Philosophy of TechnologyHuman Information Communication DesignWeb Information System
A Two-Dimensional Explanation Framework to Classify AI as Incomprehensible, Interpretable, or Understandable
No full textBecause of recent and rapid developments in Artificial Intelligence (AI), humans and AI-systems increasingly work together in human-agent teams. However, in order to effectively leverage the capabilities of both, AI-systems need to be understandable to their human teammates. The branch of eXplainable AI (XAI) aspires to make AI-systems more understandable to humans, potentially improving human-agent teamwork. Unfortunately, XAI literature suffers from a lack of agreement regarding the definitions of and relations between the four key XAI-concepts: transparency, interpretability, explainability, and understandability. Inspired by both XAI and social sciences literature, we present a two-dimensional framework that defines and relates these concepts in a concise and coherent way, yielding a classification of three types of AI-systems: incomprehensible, interpretable, and understandable. We also discuss how the established relationships can be used to guide future research into XAI, and how the framework could be used during the development of AI-systems as part of human-AI teams.Accepted author manuscriptInteractive Intelligenc
Inquadramento delle reti regionali VLNDEF e TAMDEF (Antartide) nel sistema di riferimento globale
No full textL’obiettivo del presente lavoro è di definire le strategie più opportune per l’inquadramento delle reti antartiche di stazioni permanenti ed episodiche in un sistema di riferimento globale. Lo studio è stato focalizzato in particolare sulla rete antartica di stazioni permanenti e sulle sottoreti regionali VLNDEF e TAMDEF, allo scopo di ottenere informazioni circa la cinematica superficiale della Terra Vittoria (Antartide). L’approccio scelto per l’inquadramento è stato sviluppato in tre fasi: inquadramento delle stazioni permanenti presenti nella regione oggetto di studio, connessione dei siti delle due reti regionali alle stazioni permanenti precedentemente inquadrate, elaborazione di tutte le informazioni a livello di equazioni normali. Per l’analisi dei dati è stato utilizzato il Bernese GPS software v5.0. Per l’elaborazione dei dati sono stati utilizzati parametri e modelli standard, eseguendo per alcuni di essi dei test per verificare quanto essi incidessero sull’analisi del dato e quali fossero le scelte migliori per la soluzione finale. L’analisi delle serie storiche ha evidenziato quali stazioni permanenti fossero affidabili per la connessione delle reti regionali. L’analisi spettrale delle stesse ha messo in rilievo la presenza di segnali periodici che influenzano la determinazione delle velocità, in particolar modo per la componente verticale. I risultati ottenuti mostrano un andamento ben determinato per quanto riguarda le velocità planimetriche, mentre, sono ancora incerte, in termini di spostamento verticale assoluto, a seconda del sistema di riferimento utilizzato, ITRF2000 o ITRF2005. Questo lascia ancora irrisolto il problema dell’interpretazione dei risultati da utilizzare come condizioni al contorno per i modelli geodinamici delle zone oggetto di studio
Using Generative AI in Research
No full textThe slides accompany a workshop that is intended for graduate students to learn more about generative AI in the context of the research lifecycle. This work is licensed under a Creative Commons license so that others may share and adapt the content for other purposes as long as appropriate credit is provided to the author of the work. To access the Google slides, click here: https://bit.ly/Library_AI_Research
Learning Objectives
At the end of the session participants will be able to:
Demonstrate a basic understanding of how AI tools work
Differentiate between grounded and ungrounded AI tools
Identify key considerations for grad students/researchers
Identify ways AI tools can be used to support the phases of the research lifecycle
Identify main areas of concern with using AI tools
Outline the steps and potential resources for evaluating and citing AI outpu
The AI Author in Litigation
No full textMany scholars have posited whether a computer possessing Artificial Intelligence (AI) could be considered an author as defined per the Copyright Act of 1976. What was once a thought experiment is now becoming reality. To date, scholarship has focused primarily been on whether an AI meets the requirements of authorship from a purely objective legal framework or whether an AI could be an author based on the doctrines of incentives, independent creation, and creativity.
However, a burden inherent in the rights and liabilities of authorship is the ability to be held liable if that author’s expressive work is infringing on another’s. A cause of action is meaningless if a copyright owner cannot enforce it by suing the infringer or if the infringer is judgement-proof. Thus, when contemplating whether an emancipated AI—or any non-human—can be an author under the Copyright Act, part of that examination should be whether the AI which created the work can sue or be sued for infringement.
This article considers issues from the theoretical, like civil procedure and remedies, to the practical, such as legal representation and discovery. How is an AI served with a lawsuit? What would be an adequate, enforceable remedy for an AI’s infringement? Is an AI even bound by our laws? Additional questions—and procedural barriers—are raised when considering other roles an AI might play in an infringement action: as a witness, a co-party, or even a plaintiff seeking to protect its own creative expression.
This morass of legal headaches goes beyond any doctrinal issues regarding authorship, and provide ample reason to keep legal authorship in the hands of humans or entities controlled by humans—at least until legal procedure catches up to technological realities and possibilities for litigation that AI parties present
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The AI Author in Litigation
No full textMany scholars have posited whether a computer possessing Artificial Intelligence (AI) could be considered an author as defined per the Copyright Act of 1976. What was once a thought experiment is now becoming reality. To date, scholarship has focused primarily been on whether an AI meets the requirements of authorship from a purely objective legal framework or whether an AI could be an author based on the doctrines of incentives, independent creation, and creativity.
However, a burden inherent in the rights and liabilities of authorship is the ability to be held liable if that author’s expressive work is infringing on another’s. A cause of action is meaningless if a copyright owner cannot enforce it by suing the infringer or if the infringer is judgement-proof. Thus, when contemplating whether an emancipated AI—or any non-human—can be an author under the Copyright Act, part of that examination should be whether the AI which created the work can sue or be sued for infringement.
This article considers issues from the theoretical, like civil procedure and remedies, to the practical, such as legal representation and discovery. How is an AI served with a lawsuit? What would be an adequate, enforceable remedy for an AI’s infringement? Is an AI even bound by our laws? Additional questions—and procedural barriers—are raised when considering other roles an AI might play in an infringement action: as a witness, a co-party, or even a plaintiff seeking to protect its own creative expression.
This morass of legal headaches goes beyond any doctrinal issues regarding authorship, and provide ample reason to keep legal authorship in the hands of humans or entities controlled by humans—at least until legal procedure catches up to technological realities and possibilities for litigation that AI parties present
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