The project enables a highly precise modus operandi through the “sustainable” use of artificial intelligence and data, in compliance with the regulations that govern these applications and their ethical implications, these too binding.

Specifically, it is an advanced oncological diagnosis system based on AI/ML and Digital Twin (Digital Healthcare Identity) technologies delivered through a Cloud platform. It is designed to serve local oncology networks organized into HUB and Spoke models and to support medical personnel in identifying treatment options, potential complications, and the best follow-up strategy based on available local services.

In fact, OncologIA’s reference framework includes an ecosystem that leverages AI-driven technology at various levels: from predictive algorithms and algorithms which provide decision-support in clinical and logistical settings, to the establishment of Digital Healthcare Identities (IDS).

An advanced digital model of real patients

OncologIA is testing a “Digital Healthcare Identity” model of real patients, connected through specific sensors and devices. Through a simulation process, this model gives doctors and/or caregivers a near-real time “view” of the patient, creating a digital twin of the individual so as provide medical personnel with a comprehensive and integrated clinical overview of their patient’s health status, thus facilitating the identification of personalized, and therefore potentially more effective, therapies.

BENEFITS

AI-driven systems, developed throughout time to enhance medical perceptive capability with more in-depth biological data, have today become true tools to support healthcare professionals. In fact, AI-driven technology provides real and constant support to all healthcare personnel, serving as a “second pair of eyes” in the smart cultural integration between humans and machines, based on the understanding that the human cognitive system is regardless always more “intelligent” than the artificial one.

Thanks to these new and advanced predictive tools and support systems for clinical decisions, doctors can delegate data calculations and operations to machines, allowing them to focus their time and energy on interpreting complex phenomena and exploring potential solutions.

Healthcare professionals can thus take advantage of the enormous potential of predictive AI systems in the prognostic field through the availability of Big Data and machine learning, while continuing to guide, supervise, and monitor patients using their own intelligence and typically human qualities like abstraction, intuition, flexibility and empathy to exercise a conservative and constructively critical approach.