20 miliardi di euro l’anno: i costi per il cancro in Italia – Prevenzione attiva, la vera arma vincente
Designing and structuring an AI-based platform, especially for the interception of cancer driver conditions, is a multidisciplinary effort requiring inputs from healthcare professionals, data scientists, software developers, UX designers, and legal and regulatory experts. The data collection and management layer is responsible for acquiring, validating, storing, and managing data that the AI models will train on, and analyze with a data management system to securely store and efficiently query these data. The AI/ML model development and training layer is focused on the feature selection, model selection, and training of the AI models and a system for training these models. We develop the model deployment and the serving layer to distribute the trained models to a system where they can analyze new data and make predictions via an API. The interface layer will be designed in such a way that it will be easy for healthcare providers to input data, request analyses, and interpret results.
EFFECTS ON SOLUTION
The HELIXAFE AI-based platform for the interception of cancer driver conditions enhances decision-making. Healthcare providers can leverage data-driven insights to make better decisions regarding diagnosis and treatment. The platform provides probabilities for different outcomes, helping physicians in their decision-making process. It can automate various aspects of healthcare, from data analysis to predictive modeling. These speeds up processes, decrease human errors, and allow healthcare professionals to focus more on patient care, rather than on administrative tasks. The HELIXAFE AI platform assists in developing personalized treatment plans with chemo-preventive agents and helps identify patterns and correlations that might be impossible for humans to detect. This can lead to new insights and advancements in cancer prevention and in preventive care.
