Artificial intelligence and Machine learning in digital health


22 Sept.			09:30 - 10:30



YOUNGINNOVATION		HEALTH & NANOMEDICINE
THE STATE OF RESEARCH COMMUNICATED BY YOUNG RESEARCHERS
TT.IX - Technical Multi-Track with Parallel SYMPOSIA
Artificial intelligence and Machine learning in digital health
Co-organized with University Magna Graecia of Catanzaro and Sapienza University of Rome Chair: Laura BONZANO, University of Genoa
The evolution of digital technologies is opening new frontiers in healthcare, thanks to the integration of Artificial Intelligence (AI) and Machine Learning. These powerful technologies are tackling complex medical challenges and transforming the healthcare field into a new era of more accurate diagnoses, personalized therapies, and proactive health management. From early disease detection to individual risk prediction, these technologies are proving to be fundamental tools for enhancing the precision and effectiveness of treatments. In detail, AI is revolutionizing the collection and analysis of clinical data, enabling the identification of hidden patterns and correlations that would escape human observation. On the other hand, Machine Learning is refining predictions of medical outcomes and facilitating continuous patient monitoring through digital devices and wearables. The link between AI, Machine Learning, and Digital Health not only promises improvements in timely diagnoses and prognosis, but also offers innovative solutions for managing large volumes of medical data, discovering new drugs, and personalized therapies. However, this revolution is not without challenges, such as ethical dilemmas related to patient data usage and the implementation of decision-making algorithms in healthcare. Balancing technological innovation with privacy and medical responsibility is a crucial theme that requires attention.
*The symposium is part of the Special Event SE.I*
TT.IX.F.1 SE.I.9.1	Introductive Keynote Alessia BRAMANTI University of Salerno Application of artificial intelligence and machine learing in cardiovascular diseases
TT.IX.F.2 SE.I.9.2	Monica BIGGIO - CV University of Genoa Disentangling Blink Reflexes in Multiple Sclerosis with machine learning techniques
TT.IX.F.3 SE.I.9.3	Giuseppe Felice RUSSO - CV University of Salerno Innovation in cardiology: telemedicine and artificial intelligence to manage heart failure
TT.IX.F.4 SE.I.9.4	Luigi CHIRICOSTA IRCCS Messina Big data and omics: bioinformatics to support personalized medicine
TT.IX.F.5 SE.I.9.5	Paulina Anna WOJTYLO University of Perugia Development of the novel indolic modulators of the aryl hydrocarbon receptor using machine learning