AFFILIAZIONE
Aurora – International Study Center
AUTORE PRINCIPALE
Dr.ssa Ferrara Caterina
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GRUPPO DI LAVORO
AREA TEMATICA
Innovazione di prodotti e servizi dagli incubatori di impresa
ABSTRACT
Communication between non-verbal patients and healthcare professionals represents a significant challenge for patient safety, care effectiveness, and personalization. Traditional solutions based on symbolic languages, digital interfaces, or physical aids often limit the understanding of patients’ cognitive and affective needs.
We propose a prototype developed within an incubator setting that integrates advanced artificial intelligence, non-invasive detection of physiological signals (EEG, vital signs, movement), and an ethical Decision Engine. The ethical component is central to the system: every inference is designed to ensure transparency, privacy protection, and compliance with clinical and bioethical principles, while supporting human supervision without replacing it.
The prototype employs artificial intelligence models based on deep neural networks and supervised/unsupervised learning, trained on anonymized physiological datasets of non-verbal patients. The AI detects complex patterns in physiological and behavioral signals, translating them into clinically interpretable representations for healthcare professionals, always under human supervision and in full respect of ethical and privacy standards.
The primary target population comprises non-verbal patients with autism spectrum disorders, estimated at approximately 590,000 individuals in Italy, including children and adults, based on projections from national and international epidemiological data. Patients with other conditions characterized by language deficits, such as ALS, are excluded from the target population, as dedicated clinical devices already exist for these groups.
The solution is modular and scalable, designed for pilot studies in hospital, rehabilitation, and educational settings, and represents a starting point for the development of advanced cognitive communication tools. This approach aims to improve the accuracy of patient needs assessment, enhance care effectiveness, and facilitate the adoption of innovative devices and services in healthcare, with a strong focus on ethics, safety, practical applicability, and future integration with emerging technologies.
We propose a prototype developed within an incubator setting that integrates advanced artificial intelligence, non-invasive detection of physiological signals (EEG, vital signs, movement), and an ethical Decision Engine. The ethical component is central to the system: every inference is designed to ensure transparency, privacy protection, and compliance with clinical and bioethical principles, while supporting human supervision without replacing it.
The prototype employs artificial intelligence models based on deep neural networks and supervised/unsupervised learning, trained on anonymized physiological datasets of non-verbal patients. The AI detects complex patterns in physiological and behavioral signals, translating them into clinically interpretable representations for healthcare professionals, always under human supervision and in full respect of ethical and privacy standards.
The primary target population comprises non-verbal patients with autism spectrum disorders, estimated at approximately 590,000 individuals in Italy, including children and adults, based on projections from national and international epidemiological data. Patients with other conditions characterized by language deficits, such as ALS, are excluded from the target population, as dedicated clinical devices already exist for these groups.
The solution is modular and scalable, designed for pilot studies in hospital, rehabilitation, and educational settings, and represents a starting point for the development of advanced cognitive communication tools. This approach aims to improve the accuracy of patient needs assessment, enhance care effectiveness, and facilitate the adoption of innovative devices and services in healthcare, with a strong focus on ethics, safety, practical applicability, and future integration with emerging technologies.
