Taking AI risks seriously: a new assessment model for the AI Act

The EU Artificial Intelligence Act (AIA) defines four risk categories: unacceptable, high, limited, and minimal. However, as these categories statically depend on broad fields of application of AI, the risk magnitude may be wrongly estimated, and the AIA may not be enforced effectively. This problem is particularly challenging when it comes to regulating general-purpose AI (GPAI), which has versatile and often unpredictable applications. Recent amendments to the compromise text, though introducing context-specific assessments, remain insufficient. To address this, we propose applying the risk categories to specific AI scenarios, rather than solely to fields of application, using a risk assessment model that integrates the AIA with the risk approach arising from the Intergovernmental Panel on Climate Change (IPCC) and related literature. This integrated model enables the estimation of AI risk magnitude by considering the interaction between (a) risk determinants, (b) individual drivers of determinants, and (c) multiple risk types. We illustrate this model using large language models (LLMs) as an example

Soluble ST2 levels and left ventricular structure and function in patients with metabolic syndrome

Background: A biomarker that is of great interest in relation to adverse cardiovascular events is soluble ST2 (sST2), a member of the interleukin family. Considering that metabolic syndrome (MetS) is accompanied by a proinflammatory state, we aimed to assess the relationship between sST2 and left ventricular (LV) structure and function in patients with MetS. Methods: A multicentric, cross-sectional study was conducted on180 MetS subjects with normal LV ejection fraction as determined by echocardiography. LV hypertrophy (LVH) was defined as an LV mass index greater than the gender-specific upper limit of normal as determined by echocardiography. LV diastolic dysfunction (DD) was assessed by pulse-wave and tissue Doppler imaging. sST2 was measured by using a quantitative monoclonal ELISA assay. Results: LV mass index (β=0.337, P<0 .001, linear regression) was independently associated with sST2 concentrations. Increased sST2 was associated with an increased likelihood of LVH [Exp (B)=2.20, P=0.048, logistic regression] and increased systolic blood pressure [Exp (B)=1.02, P=0.05, logistic regression]. Comparing mean sST2 concentrations (adjusted for age, body mass index, gender) between different LV remodeling patterns, we found the greatest sST2 level in the group with concentric hypertrophy. There were no differences in sST2 concentration between groups with and without LV DD. Conclusions: Increased sST2 concentration in patients with MetS was associated with a greater likelihood of exhibiting LVH. Our results suggest that inflammation could be one of the principal triggering mechanisms for LV remodeling in MetS

How to evaluate the risks of Artificial Intelligence: a proportionality-based, risk model for the AI Act

The EU proposal for the Artificial Intelligence Act (AIA) defines four risk categories: unacceptable, high, limited, and minimal. However, as these categories statically depend on broad fields of application of AI systems (AIs), the risk magnitude may be wrongly estimated, and the AIA may not be enforced effectively. Our suggestion is to apply the four categories to the risk scenarios of each AIs, rather than solely to its field of application. We address this model flaw by integrating the AIA with the framework arising from the Intergovernmental Panel on Climate Change (IPCC) reports and related literature. This makes possible addressing AI risk considering the interaction between (a) risk determinants, (b) individual drivers of determinants, and (c) multiple risk types. Then we integrate the proposed model with a proportionality-based balance among values considered by the AIA’s risk analysis. The resulting semi-quantitative approach identifies a more efficient way to implement the AIA and addresses the regulatory issue of general-purpose AI (GPAI)

Syncope and sudden death from the emergency physician's perspective: is there room for new biomarkers?

Syncope is a transient loss of consciousness due to temporary global cerebral hypoperfusion characterized by rapid onset, short duration, and spontaneous complete recovery. Syncope represents 1-2% of emergency department (ED) visits and is coupled with a high risk for mortality, prolonged hospital admission, and immediate false diagnosis. Many patients who present to the ED with aspecific symptoms are mainly hospitalized because of diagnostic uncertainty. It is always very important to immediately distinguish syncope of cardiac and non-cardiac origins. Cardiac syncope has higher risk for mortality especially for sudden cardiac death, while non-cardiac one shows risk of repeated events of syncope with poor quality of life. Sudden cardiac death is defined as rapid and unexpected natural death due to cardiac etiology. Researchers from the GREAT Network hypothesized to evaluate some novel biomarkers in order to test acute cardiac condition that can suggest the presence of heart structural diseases, heart failure, and electrical disorders. The primary objective of this study is to test the diagnostic performance from patient history, clinical judgment, and novel biomarkers in the diagnosis of cardiac syncope in patients admitted to the ED. The trial is designed as a prospective international multicenter observational study accounting for 730 patients aged over 40 admitted to the ED with syncope within the last 12 h. A multimarker approach combining markers of different origin and mode of relapse, should add diagnostic information to correctly identify the cardiac conditions and to therefore be pertinent in the early diagnosis of cardiac syncope and in the prediction of cardiac events including sudden death. Future data should be needed to confirm the hypothesis presented here

Pseudo-Filone di Bisanzio, Le sette meraviglie del mondo. Introduzione, testo critico, traduzione, note esegetiche e testuali; con la traduzione latina di Lukas Holste

Il Περὶ ἑπτὰ θεαμάτων è l’unico trattato sulle sette meraviglie che ci sia giunto dall’antichità. Il solo testimone che ne tramanda il testo – il Pal. Gr. 398, celebre testimone appartenente alla cosiddetta "collezione filosofica" – lo attribuisce all’ingenere ellenistico Filone di Bisanzio, attivo fra la metà e la fine del III sec. a.C. Tale attribuzione ha accresciuto, fin dal XVII sec., fama e autorità del trattatello. Tutto indica, tuttavia, che l’autore debba essere considerato tardo-antico, se non addirittura proto-bizantino. Questo volume offre la prima edizione critica del Περὶ ἑπτὰ θεαμάτων, corredata di traduzione, e preceduta da un’ampia introduzione che esplora la tradizione letteraria di cui l’autore si è nutrito, nonché le sorti testuali, il genere, la lingua e lo stile del trattatello, per arrivare a un’ipotesi di datazione. A ciascuna delle meraviglie descritte dall’autore sono inoltre dedicati approfondimenti di carattere storico e letterario, utili a far emergere le peculiarità della prospettiva adottata dallo Pseudo-Filone, e – in alcuni casi – a individuarne le probabili o sicure fonti. A corredo e giustificazione del testo è offerta una discussione dei passi più problematici sotto il profilo esegetico e critico-testuale. Conclude il volume la traduzione latina, sinora inedita, di Lukas Holste (1596-1661)

Taking AI Risks Seriously: a Proposal for the AI Act

The EU proposal for the Artificial Intelligence Act (AIA) defines four risk categories: unacceptable, high, limited, and minimal. However, as these categories statically depend on broad fields of application of AI, the risk magnitude may be wrongly estimated, and the AIA may not be enforced effectively. This problem is particularly challenging when it comes to regulating general-purpose AI (GPAI), which has versatile and often unpredictable applications. Recent amendments to the compromise text, though introducing context-specific assessments, remain insufficient. To address this, we propose applying the risk categories to specific AI scenarios, rather than solely to fields of application, using a risk assessment model that integrates the AIA with the risk approach arising from the Intergovernmental Panel on Climate Change (IPCC) and related literature. This model enables the estimation of the magnitude of AI risk by considering the interaction between (a) risk determinants, (b) individual drivers of determinants, and (c) multiple risk types. We use large language models (LLMs) as an example