“In a sense, artificial intelligence will be the ultimate tool because it will help us build all possible tools.” — Dr. K. Eric Drexler, Future of Humanity Institute, University of Oxford
Artificial Intelligence (AI) can be broadly defined as the capability of machines to mimic human intelligence. This includes learning from data and making decisions based on that information. This field has evolved rapidly into a technology that transform nearly every aspect of our lives. In recent years, AI has significantly revolutionized the healthcare industry. This transformation is attributed to the ability of AI to manage, process, and evaluate big amounts of healthcare data that are available. These tasks often surpass human capacity.
AI is reshaping healthcare by enhancing diagnostics, personalizing treatments, and improving operational efficiency. It leverages machine learning and cognitive technologies to process vast amounts of complex medical data, aiding in early disease detection, precision medicine, and resource optimization.
Applications include diagnostic imaging, where AI identifies abnormalities with high accuracy, and personalized treatment plans tailored to individual genetic and lifestyle factors. Additionally, AI-driven predictive analytics anticipates health risks, enabling proactive care, while AI-powered tools streamline drug discovery, reduce costs, and accelerate clinical trials.
A landmark moment in the integration of AI in healthcare came in 2018 when the U.S. Food and Drug Administration (FDA) approved the first AI-powered device for detecting diabetic retinopathy. This device uses advanced algorithms to analyze retinal images and identify signs of the disease, enabling early detection and treatment without requiring a specialist. Additionally, the rapid growth of AI in healthcare is reflected in the steady rise in research activity and the increasing number of scientific publications over the past decade.
AI in healthcare holds transformative potential, but significant challenges must be addressed to fully realize its benefits. One of the primary issues is the need for transparency and explainability, as healthcare professionals must understand not only individual AI predictions but also the broader framework of the system. This includes knowledge of the AI’s capabilities, limitations, and subjective decision thresholds, similar to how clinicians assess the diagnostic tendencies of their human colleagues. Additionally, the onboarding process plays a crucial role in fostering trust and effective use of AI. Practitioners seek clarity on the source of ground truth used to train the AI, the diversity of the training data, and its capacity to handle edge cases, especially in high-stakes scenarios like cancer diagnosis. Without this information, clinicians may be hesitant to fully rely on AI support. To overcome these challenges, human-AI collaboration should be designed to emphasize interactive onboarding and competence articulation, where the AI’s capabilities and design objectives are made transparent. Addressing these challenges through transparent design, user-friendly onboarding, and human-centered AI development will enable the creation of reliable, explainable, and patient-centered AI solutions.
These goals are aligned with initiatives like the HORIZON Europe COMFORTage project, which showcases its potential in applying these technologies to real-world challenges.
COMFORTage aims to develop holistic and integrated healthcare models to foster personalized dementia and frailty prevention and interventions promoting individuals’ physical and mental health. COMFORTage will facilitate care services providers to design and deploy personalized, integrated care prevention and intervention measures against dementia and frailty towards significantly and evidently improving the individuals’ wellbeing and Quality of Life. In addition, it will provide means and tools for the empowerment and enhancement of all stakeholders’ health and digital literacy to further reduce health inequalities in modern communities and to support healthy and active age living. In that context, the project’s framework will be validated and evaluated in thirteen (13) pilot studies involving diverse stakeholders at eight (8) EU member states and will be empowered by a unique combination and integration of (i) Medical/clinical innovations (ii) Cutting edge AI innovations for trusted, accurate, secure and personalized clinical decision making, (iii) DIHs to facilitate and promote research activities in the health and wellbeing domain and (iv) social innovations for promoting innovative views and co-creating new or improved solutions for assistance and improvement of social integration and interaction.
ITML’s role
ITML will design and implement a Training recommender tool, offering personalized learning experiences to patients based on different cognitive states. The module is going to be integrated as part of the COMFORTage serious games with the aim to provide recommendations for disease prevention in patients. The tool aims to enhance patients’ interactions within learning systems and improve their engagement and learning efficiency.
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