AIM@EPIA 2017 : Artificial Intelligence in Medicine
Call For Papers
Every day medicine is facing new challenges: new diseases, cost reductions, new therapeutics, quick decisions and make more with less. Artificial Intelligence (AI) can play an important role in the decision making process, most concretely in the way the data of the patients are collected, treated, processed and presented as well to test and simulate new treatments, scenarios and devices. The big question to be answered is: How Artificial Intelligence can help to overcome these challenges and provide new and efficient solutions to medicine?
Business Intelligence, Data Mining, Sensing, Pervasiveness, Ubiquity and Intelligent Agents in Medicine, can contribute with new artifacts and new knowledge for health professionals. The development of AI systems has been one of the most ambitious and, not surprisingly, controversial themes in medicine. Often, healthcare professionals are septic regarding the use of these technologies, because they are afraid of losing their jobs. However, this concern is not justified. AI no longer aims the substitution of professionals by computer artifacts. AI aims to improve the usability of programs for assisting physicians in figuring out what is wrong with the patients and provide new solutions to help making better decisions. AI systems are intended to support healthcare practitioners in the normal course of their duties, assisting with tasks that rely on the manipulation of data and knowledge. In particular, these systems have for example the capacity to learn, leading to the discovery of new phenomena and the creation of medical knowledge.
This track promotes a forum to discuss and present emergent themes, new projects and ideas about how AI can contribute to the field of Medicine and, most concretely, improve patient conditions. By bringing together researchers from two distinct areas is expected to produce new scientific and technical knowledge in a particular area as is medicine.
Information technology, in general, can help improving human health and longevity. To achieve this goal innovative and intelligent software can be deployed in order to improve medical research, disease prevention, and healthcare service delivery.
The motto of this track is “artificial intelligence improves medicine” and we are inviting the community to share this vision.
Topics of interest
We seek novel, innovative, and exciting work in areas including but not limited to:
Medical methodologies, architectures, environments and systems.
• Agents for information retrieval;
• AI in Medical Education and Clinical Management;
• Wellbeing and lifestyle support;
• Interoperability, Security, Pervasiveness, Ubiquity and Cloud Computing in Medicine;
• Methodological, philosophical, ethical, and social issues of AI in Medicine;
• Pervasive Healthcare Environments;
• Software architectures.
Knowledge engineering and Decision Support Systems:
• AI-based clinical decision making and Clinical Decision Support Systems;
• Automated reasoning, Case-Based Reasoning or Reasoning with medical knowledge;
• Business Intelligence in Health Care;
• Clinical Data Mining;
• Data Streaming;
• Diagnostic assistance;
• Expert, agent-based or knowledge-based systems;
• Genomic data;
• Medical knowledge engineering;
• Pervasive or Real-Time Intelligent Decision Support Systems in Critical Health Care.
Medical Applications and Devices
• Computational intelligence in bio- and clinical medicine;
• Electronic Health Records (eHealth);
• Image recognition and interpretation;
• Intelligent devices and instruments;
• Sensor-based applications;
• Telemedicine and mHealth solutions;
• Ubiquitous devices in the storage, update, and transmission of patient data;
• Usability and acceptability.
AI in Healthcare Information Systems
• Autonomous systems to support independent living;
• Healthcare System Based on Cloud Computing;
• Intelligent Healthcare information systems;
• Pervasive Information Systems;
• Pervasiveness and Security in Clinical Systems;
• Smart homes, hospitals and Intelligent Systems;
• Simulation Computer systems.
Manuel Filipe Santos, Universidade do Minho, Portugal
Carlos Filipe Portela, Universidade do Minho, Portugal
Allan Tucker, Brunel University London, United Kingdom
José Machado, University of Minho, Portugal
António Abelha, University of Minho, Portugal
Pedro Henriques Abreu, University of Coimbra, Portugal
Daniel Castro Silva, University of Porto, Portugal