Re: HC@AIxIA: AI&Health Seminar Series (2024) - APRIL 17

[Gentle Reminder]

dear all,

please note that today's seminar is going to start in a few minutes. 🙏👨‍🏫

join here:
https://unimib.webex.com/unimib/j.php?MTID=m8e381e4f5342f30bd5467906320a5783

ciao
f


On Mon, Apr 15, 2024 at 5:09 PM Francesco Calimeri <fcalimeri@gmail.com>
wrote:

> Dear Madam/Sir,
>
> This is to officially announce the FOURTH seminar of the "AI & Health"
> series as hosted by HC@AIxIA, i.e., the "Artificial Intelligence for
> Healthcare" working group of the Italian Association for Artificial
> Intelligence. *Save the date: 17 March.*
>
> We hope you will attend and participate in the discussion on the relevant
> topics that will be presented and by our speakers.
> *Feel free to share this with those potentially interested.*
> Please find some details below, and a poster attached. All directions for
> participating are available at https://aixia.it/gruppi/hc/.
>
> *== Are you interested in Joining the group? ==*
> Please head to https://aixia.it/en/gruppi/hc/ fo find out how. Do not
> hesitate to contact us at hc-aixia@googlegroups.com for any information
> or clarification.
>
>
> Thank you for your interest in the AI & Health seminar series and the
> HC@AIxIA working group, and see you soon!
>
> Sincerely,
> Francesco Calimeri, Mauro Dragoni, Fabio Stella
> (coordinators of the HC@AIxIA working group)
>
>
>
> *== April 2024 seminar ==*
> *Link to participate: *
> https://unimib.webex.com/unimib/j.php?MTID=m8e381e4f5342f30bd5467906320a5783
>
> *2024 APRIL 17 - 4:30PM CET*
> *Aldo Marzullo and Saverio D'Amico*
> IRCSS Humanitas Research Hospital (Rozzano, Milan, Italy)
>
> *Title*: Health digital twins. Artificial Intelligence to support
> clinical decision making in hematology
>
> *Abstract*: Rare diseases are life-threatening or chronically
> debilitating diseases which affect fewer than 5 in every 10000 people in
> the EU. Most rare diseases lack effective treatments representing an
> enormous unmet medical need. The major challenge is to understand
> rare-disease mechanisms better and ensure that research and innovation are
> effectively translated into new diagnostics and treatments. Personalized or
> precision medicine combines established clinical-pathological parameters
> with advanced profiling to create innovative diagnostic, prognostic, and
> therapeutic strategies. This approach is relevant in the context of rare
> hematologic diseases, where additional information from transcriptomics
> (and other omic features), as well as from digitized images, may improve
> the clinical decision-making process and the choice of optimal therapy or
> treatment. Health digital twins are virtual representations of patients
> generated from historical multimodal patient data, such as clinical,
> genomics, physiology, images, treatment, outcomes, physics, quality of life
> (QOL) and wearables. They can improve diagnosis and prognosis, predict
> treatment in a specific patient population and create virtual scenarios to
> support clinical decision-making. Health digital twins implement
> data-driven Artificial Intelligence (AI) and Machine Learning (ML) methods,
> trained on patients' longitudinal data, to build robust predictive models
> that integrate multiple information to address unmet clinical needs.
> AI-based models integrate multi-layer information and simulate the behavior
> and prognosis of the disease in the individual patient, allowing a detailed
> understanding of the disease and treatment effects, and defining the
> patient’s individual risk. The impact of health digital twins can be
> evaluated in several areas: 1) improve patient outcomes by using specific
> patient information to identify the most appropriate treatment; 2) reduce
> healthcare costs with more targeted and effective therapies; 3) accelerate
> clinical and pharmaceutical research; 4) deal with ethical and privacy
> issues, detaching the link between people and the value of data. Despite
> innovative AI technology being extensively applied to different medical
> fields, health digital twins represent a novel and innovative approach that
> will pave the way for effective personalized medicine. The exploitation of
> this technology will enable the creation of high performance predictive
> models, supporting clinical research and decision making.
>
> *Short Bio*:  Saverio D’Amico is Senior Data Scientist at the AI Center
> of the Humanitas Research Hospital institute in Milan. Always passionate
> about innovation and technology, Saverio graduated in biomedical
> engineering at the Polytechnic of Milan. With a background in artificial
> intelligence, consolidated thanks to the experience gained in business
> consultancy, he contributed to the development of several strategic
> innovation projects in the AI area. At Humanitas, he deals with Generative
> AI and synthetic data generation and is mainly active on the European
> projects GenoMed4All and Synthema, whose objective is the use of advanced
> and innovative technologies for personalized medicine, with particular
> attention to explainable AI processes. Since 2023 he has been CEO and CTO
> of Train, a spin-out of Humanitas specialized in the development of
> Generative AI technologies in the healthcare sector such as Synthetic Data
> and Digital Twin.
> *Short Bio*: Aldo Marzullo holds a double Ph.D. from the University of
> Calabria and the University Claude Bernard Lyon 1. Specializing in machine
> learning and graph theory, his work ranges from medical image processing to
> brain connectivity analysis and generative AI. He is currently senior data
> scientist at the AI Center of the Humanitas Research Hospital Institute in
> Milan, Italy.
>
>
>
> [image: HC@AIxIA - Seminars AI & Health 2024 - Locandina 04.png]
>