Computational Regulation of Medical Devices in PSOA RuleML

Almpani, Sofia; Stefaneas, Petros; Boley, Harold; Mitsikas, Theodoros; Frangos, Panayiotis

doi:10.1007/978-3-319-99906-7_13

Cited by 5 publications

(5 citation statements)

References 6 publications

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“…This article describes a novel attempt to formalize, in a computational manner, the exoskeleton-related parts of the European Directives. This study extends previous works concerning the formalization of MDD [16][17][18]. It focalizes specifically in the case of exoskeletons as a type of medical device and incorporates the relevant requirements from the MD, regarding their safety and marketability.…”

Section: Introductionmentioning

confidence: 76%

“…Furthermore, the syntax of the language is easily read and interpreted, facilitating the interdisciplinary understanding from nontechnical experts. PSOA RuleML has also been used for legal rules formalization in other use cases, such as Port Clearance Rules [36], Air Traffic Control Regulations [37,38], and MDD [16][17][18], providing evidence that PSOA RuleML is well suited to express real-world legal texts.…”

Section: Formalization For Exosce Rulesmentioning

confidence: 99%

“…In the first part of formalization, the 22 rules are expressed with a three-level deep description of medical device characteristics described for abbreviation with (informal) three-symbol categories [17,18]. The relational conclusion argument ?m becomes the OID (Object IDentifier) of the class :MedicalDevice of a frame with :kind, :use, and :specificCasek slots.…”

Section: Formalization For Exosce Rulesmentioning

confidence: 99%

See 2 more Smart Citations

ExosCE: A legal-based computational system for compliance with exoskeletons’ CE marking

Almpani

Mitsikas

Stefaneas

et al. 2020

Paladyn, Journal of Behavioral Robotics

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Wearable robots are devices intended to improve the quality of users’ life by augmenting, assisting, or substituting human functions. Exoskeletons are one of the most widespread types of wearable robots, currently used extensively in medical applications (and also for industrial, assistive, or military purposes), thus governed by regulations for medical devices and their conformity assessment. On top of that, manufacturers must also specify if their exoskeletons can be categorized as machines and, therefore, additionally apply a number of requirements mandated from machinery regulations. This work focuses on capturing both the abovementioned requirements enacted by the Medical Devices Directive 2017/745 and the Machinery Directive 2006/42 into a single framework. It formalizes into Rules the Conformity Assessment procedures regarding the marketability of exoskeletons indicated by the CE marking (“Conformité Européene”). These Rules, expressed in the Positional-Slotted Object-Applicative (PSOA) RuleML code, were complemented by representative Facts based on real-life cases of commercialized exoskeletons. Additional Exoskeletons Facts can be included by users from other forms (such as MS Excel) and translated into the PSOA RuleML code through the provided Python script. The open-source Exoskeletons’ CE mark (ExosCE) Rules KB was tested by querying in the open-source PSOATransRun system. The ExosCE Rules prototype can assist in the compliance process of stakeholders and in the registration of exoskeletons with a CE mark.

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Section: Introductionmentioning

confidence: 76%

Section: Formalization For Exosce Rulesmentioning

confidence: 99%

Section: Formalization For Exosce Rulesmentioning

confidence: 99%

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ExosCE: A legal-based computational system for compliance with exoskeletons’ CE marking

Almpani

Mitsikas

Stefaneas

et al. 2020

Paladyn, Journal of Behavioral Robotics

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“…The studies that attempt to fuse commonsense knowledge into Argumentation Theory consider commonsense knowledge as: commonly accepted knowledge on which they use preferences (Čyras 2016), text analytics (Moens 2016; Zhang et al . 2017), Event Calculus (Almpani & Stefaneas 2017), and even knowledge from external Web Knowledge Graphs (Kobbe et al . 2019).…”

Section: Discussionmentioning

confidence: 99%

Argumentation and explainable artificial intelligence: a survey

Vassiliades

Bassiliades

Patkos

2021

The Knowledge Engineering Review

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Argumentation and eXplainable Artificial Intelligence (XAI) are closely related, as in the recent years, Argumentation has been used for providing Explainability to AI. Argumentation can show step by step how an AI System reaches a decision; it can provide reasoning over uncertainty and can find solutions when conflicting information is faced. In this survey, we elaborate over the topics of Argumentation and XAI combined, by reviewing all the important methods and studies, as well as implementations that use Argumentation to provide Explainability in AI. More specifically, we show how Argumentation can enable Explainability for solving various types of problems in decision-making, justification of an opinion, and dialogues. Subsequently, we elaborate on how Argumentation can help in constructing explainable systems in various applications domains, such as in Medical Informatics, Law, the Semantic Web, Security, Robotics, and some general purpose systems. Finally, we present approaches that combine Machine Learning and Argumentation Theory, toward more interpretable predictive models.

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“…To create a computational prototype of this use case, ethical reasoning was integrated into the logic-based PSOA RuleML programming language (Boley, 2015) to illustrate how this ethical thinking can be formalized. PSOA RuleML has also been used for the formalization of legal procedures in similar cases, such as Exoskeletons (i.e., a type of WR) (Almpani et al, 2020) and Medical Devices (Almpani et al, 2019(Almpani et al, , 2018b(Almpani et al, , 2018a providing evidence that PSOA RuleML is well-suited to express robotic-related decision-making procedures.…”

Section: Implementation In Data Privacy Of Wearable Robotsmentioning

confidence: 99%

Formalization of Ethical Decision Making

Almpani

Stefaneas

Frangos

2023

International Journal of Extreme Automation and Connectivity in Healthcare

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As automation in robotics and artificial intelligence is increasing, we will need to automate a growing amount of ethical decision making. However, ethical decision-making raises novel challenges for designers, engineers, ethicists, and policymakers, who will have to explore new ways to realize this task. For example, engineers building wearable robots should take into consideration privacy aspects and their different context-based scenarios when programming the decision-making procedures. This in turn requires ethical input in order to respect norms concerning privacy and informed consent. The presented work focuses on the development and formalization of models that aim at ensuring a correct ethical behavior of artificial intelligent agents, in a provable way, extending and implementing a logic-based proving calculus. This leads to a formal theoretical framework of moral competence that could be implemented in artificial intelligent systems in order to best formalize certain parameters of ethical decision-making to ensure safety and justified trust.

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Computational Regulation of Medical Devices in PSOA RuleML

Cited by 5 publications

References 6 publications

ExosCE: A legal-based computational system for compliance with exoskeletons’ CE marking

ExosCE: A legal-based computational system for compliance with exoskeletons’ CE marking

Argumentation and explainable artificial intelligence: a survey

Formalization of Ethical Decision Making

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