AI4H:B²E 2019 - IEEE International Special Track on Artificial Intelligence for Healthcare: from black box to explainable models
Dal 05/06/2019 ore 09.00 al 07/06/2019 ore 17.00
@ IMIBIC - Hospital Universitario Reina Sofía
Av. Menéndez Pidal, s/n
Il Cnr-Icar è tra gli organizzatori dello “special track”: AI4H:B²E 2019 – IEEE International Special Track on Artificial Intelligence for Healthcare from black box to explainable models (http://www.ai4hb2e.icar.cnr.it/). L’appuntamento mira a riunire ricercatori provenienti da università, industria, governo e centri medici per presentare lo stato dell’arte e discutere le ultime progressi nel settore emergente dell’uso delle tecniche di Intelligenza Artificiale (AI) e Soft Computing (SC) nei campi della medicina, della biologia, della sanità e del benessere. L’ evento è associato alla conferenza “32th IEEE CBMS International Symposium on Computer-Based Medical Systems” (http://www.cbms2019.org/) che si terrà presso l’Instituto Maimónides de Investigación Biomédica de Córdoba, Spain dal 5 al 7 Giugno 2019.
The special track on “Artificial Intelligence for Healthcare: from black box to explainable models” - AI4H:B2E 2019 - aims at bringing together researchers from academia, industry, government and medical centers in order to present the state of the art and discuss the latest advances in the emerging area of the use of Artificial Intelligence (AI) and Soft Computing (SC) techniques in the fields of medicine, biology, healthcare and wellbeing. In general, in recent years, methods based on AI and SC have proved to be extremely useful in a wide variety of areas, and are becoming more and more widespread, in some cases a sort of a “de facto” standard. Currently, many of the algorithms on offer are often black box in nature (defined as a system which can be viewed in terms of its inputs and outputs without any knowledge of its internal workings). This may not be an issue for certain practical AI solutions in healthcare, yet in other systems it may indeed be a serious limitation. This holds true when a clear explanation should be provided to a user about the reasons why a solution is proposed by an AI-based system. In fact, if the predictive models are not transparent and explainable, we lose the trust of experts such as healthcare practitioners. Moreover, without access to the knowledge of how an algorithm works we cannot truly understand the underlying meaning of the output.
Given the above general framework, AI4H:B2E is expected to cover the whole range of methodological and practical aspects related to the use of AI and SC in Healthcare:
- we request papers that explore methods to combine state-of-the-art data analytics for exploiting the huge data resources available, while ensuring that these systems are explainable to domain experts. This will result in systems that not only generate new insights but are also more fully trusted.
- we also request papers that describe more generally the successful application of AI and SC methodologies to issues as machine learning, deep learning, knowledge discovery, decision support, regression, forecasting, optimization and feature selection in the healthcare, biology, medicine and wellbeing domains.
Paper Submission: 14 Gennaio 2019
Notification of acceptance: 1 Marzo 2019
Camera-ready due: 15 Marzo 2019
Author registration at the conference: 15 Marzo 2019
AI4H:B2E 2019: 5 – 7 Giugno 2019
Elizabeth Black, Senior Lecturer. Department of Informatics at King’s College London
Ivanoe De Falco, Senior Researcher Cnr-Icar. Italy
Francesco Gargiulo, Researcher Cnr-Icar. Italy
Giovanna Sannino, Researcher Cnr-Icar. Italy
Stephen Swift, Senior Lecturer. Brunel University. London
Allan Tucker, Senior Lecturer. Brunel University. London
Giuseppe De Pietro, Direttore dell’Istituto di Calcolo e Reti ad Alte prestazioni, Cnr-Icar. Italy
Brunel University London
Cnr - Istituto di calcolo e reti ad alte prestazioni
Area della ricerca Via Pietro Castellino, 111
Napoli - 80131
Modalità di accesso: a pagamento