The introduction of machine learning development paradigm into the automotive software industry has made necessary to update the applicable quality evaluation standards such as Automotive SPICE®. As a result the Automotive SPICE® community timely tackled this challenge with the introduction of the version 4.0 containing a first baseline of process requirements for machine learning engineering. The paper provides a succinct critical analysis of related ASPICE® new content with particular reference to the current state of the art of machine learning development practices. The outcome of this paper aims at being an input for the forthcoming improvement initiatives within the Automotive SPICE working groups.
The rapid integration of Artificial Intelligence (AI) into software systems across many domains, including critical ones such as healthcare, transportation, and public administration, has intensified the need for robust, reliable, and transparent quality evaluation methods. While numerous standards and regulatory initiatives have emerged and many more are being worked on, the current landscape remains fragmented with varying scopes, definitions, and enforcement mechanisms. This paper provides a structured, yet not exhaustive, overview of current international standards, regulatory instruments, and soft-law guidelines relevant to AI-based software quality evaluation. The purpose of this paper is to provide a starting point for practitioners and researchers for understanding the status, as well as the short-coming evolution, of the standards addressing AI-based technologies and for orienting their efforts to contribute to fill the existing lacks and weaknesses in the standard corpus.
Healthcare systems around the world are under pressure to adopt advanced technologies, yet innovation is often stalled by rigid regulatory frameworks, complex procurement procedures, and risk-averse cultures. In contrast, societal crises, such as a pandemic and wartime, catalyze rapid user-driven development and deployment of technologies such as drones, telehealth platforms, and situational awareness systems, demonstrating that even high-risk domains can innovate and rapidly deploy new technologies under extreme conditions. This paper explores what healthcare can learn from these innovation models of social crises, focusing on how agility, minimal viable products (MVPs) and close end-user collaboration can coexist with safety and ethical oversight. By examining real-world cases and drawing parallels with healthcare, we propose actionable strategies to accelerate responsible innovation in healthcare.
Triple-negative breast cancer (TNBC) is an aggressive subtype with poor prognosis and limited treatments, for which accurate pre-operative prediction is essential for guiding therapy. While multiparametric MRI is highly sensitive, its use in multi-center AI workflows is hampered by inter-scanner variability. This study explores Federated Learning with radiomic features from DCE-MRI, and assesses the role of image standardization in improving TNBC classification performance. Data were split across 5 virtual clients to simulate hospitals, each training locally within a federated MLP framework. Results show that image standardization markedly improves TNBC classification, highlighting the role of preprocessing in federated AI pipelines.
The SENTIRE—DPI-uAI project aims to develop an intelligent active hearing protection device for workers in high-noise industrial environments. Combining Internet of Things (IoT) technologies, Machine Learning algorithms, and edge-cloud architectures, SENTIRE enables selective noise filtering to preserve safety-relevant sounds while suppressing harmful ones. At the core of the system are noise-canceling headphones connected via BLE to a wearable device that integrates a DSP module for acoustic signal pre-processing and an embedded CPU for contextual analysis and sound classification. Edge communication is handled via MQTT protocol, while a serverless infrastructure enables interaction with the smart factory. The system demonstrates robust latency and classification performance, confirming the feasibility of selective audio filtering and sub-meter localization accuracy in industrial scenarios [5, 6].
