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SUMMARY:Joint ICTP-IAEA Workshop on the Clinical Application of Imaging-Ba
 sed Artificial Intelligence Systems for Medical Physicists | (smr 4246)
DTSTART;VALUE=DATE-TIME:20261116T060000Z
DTEND;VALUE=DATE-TIME:20261120T200000Z
DTSTAMP;VALUE=DATE-TIME:20260914T231636Z
UID:indico-event-11172@ictp.it
DESCRIPTION:\n	An ICTP-IAEA meeting\n	The workshop is designed for medical
  physicists interested in expanding their knowledge and skills in the clin
 ical implementation of imaging-based AI systems. By combining theoretical 
 principles with hands-on practical examples\, it offers a contemporary ove
 rview and practical tools for safe and effective implementation.\n\n	 \n\
 n	The deployment of technologies based on artificial intelligence (AI) in 
 medical physics and in the medical use of ionizing radiation - namely radi
 ation oncology\, diagnostic imaging and nuclear medicine - is rapidly incr
 easing. Medical physicists are expected to play a key role in ensuring saf
 e and effective clinical implementation of AI based tools.\n\n	This compre
 hensive workshop will provide participants with a theoretical foundation i
 n the most important areas of imaging-based AI systems relevant to medical
  physics. The workshop will introduce medical physicists to examples of im
 aging-based AI systems\, along with relevent aspects of their clinical imp
 lementation\, including technical\, ethical\, and legal challenges related
  to the implementation process. These aspects should be carefully consider
 ed to ensure that quality of patient care and patient safety are not compr
 omised.\n\n	Hands-on training focused on implementation\, risk mitigation\
 , and quality assurance of imaging-based AI systems will be provided to st
 rengthen participants’ practical skills. \n\n	\n	 \n\n	Topics:\n	 \n\
 n	The Workshop is aimed at providing medical physicists with relevant know
 ledge to support departments in deploying and using imaging-based AI tools
 \, including:\n\n	 \n\n		Insight into current imaging-based AI tools in r
 adiation medicine (diagnostic radiology\, nuclear medicine and radiotherap
 y imaging workflows)\;\n	\n		Theoretical principles of AI from the user’
 s perspective: foundational statistical concepts\, statistical modelling\,
  machine learning\, deep learning\, radiomics\, data management (including
  feature selection and engineering)\, model training and validation\;\n	\n
 		Roles and responsibilities of clinically qualified medical physicists in
  imaging-based AI clinical applications\;\n	\n		Requirements for education
  and training\;\n	\n		Implementation considerations: identification of  n
 eeds\, market research\, (pre)selection\, installation\, acceptance and co
 mmissioning\, introduction to clinical settings\, quality assurance\, clin
 ical evaluation and decommissioning\; \n	\n		Regulatory and ethical aspec
 ts\;\n	\n		Hands-on training.\n\n	Lecturers:\n		Andre Dekker\, Maastricht 
 University\, Medical Center and Maastro Clinic\, Kingdom of the Netherland
 s\n		Serafina Di Gioia\, ICTP\, Italy\n		Giulia Giovannini\, ATS Liguria\
 , AREA 2\, Italy\n		Ashish Kumar Jha\, Tata Memorial Hospital\, Mumbai\, I
 ndia\n		 \n\n\n	 \n\n	Prerequisites:\n\n	The target audience are early a
 nd mid-career clinically qualified medical physicists (CQMPs\, as per IAEA
  Publication Human Health Series No. 25) from United Nations\, UNESCO or I
 AEA Member States holding a postgraduate-level university degree in medica
 l physics and working in hospitals in either in radiotherapy\, nuclear med
 icine or diagnostic radiology. Although the workshop is not intended for d
 evelopers of AI-based tools\, basic computational programming\, data manag
 ement and fundamental statistics skills are considered as assets.\n\n	 \n
 \n	Grants: A limited number of grants are available to support the attenda
 nce of selected participants\, with priority given to participants from de
 veloping countries. There is no registration fee.\n\n	 \n\n//indico.ictp.
 it/event/11172/
LOCATION:ICTP Kastler Lecture Hall (AGH)
URL://indico.ictp.it/event/11172/
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