Call for Papers - 2nd European Conference on eHealth (ECEH'07)

OFFISOldenburg, Germany, October 11 - 12, 2007
This conference aims to promote research and scientific exchange related to eHealth and to bring together practitioners, scientists, and researchers, as well as those interested in learning more about eHealth.

This conference will discuss innovations in eHealth, especially in the following areas:

  • eHealth portals and web-based platforms
  • Medical communication centres
  • Telemedicine
  • Electronic healthcare records
  • Data security and data protection
  • Business process optimization in health-care
  • Home care
  • Usage of mobile devices in eHealth
  • Data warehouse and data mining technologies in healthcare
  • Standards and exchange formats for eHealth
  • Semantic interoperability
  • Privacy and ethical considerations

In addition, new and innovative topics can be addressed by organized / special sessions within the conference tracks.

Important Dates:
27.02.2007 Proposals for organized sessions
22.04.2007 Paper submission (Extended)
15.05.2007 Notification of acceptance
15.06.2007 Camera-ready due

Paper Submission:
Submitted papers must be original and not used for publication elsewhere. Authors are invited to submit their manuscripts in PDF format. The paper length should not exceed 12 pages. The proceedings will be published in the GI-Edition - Lecture Notes in Informatics (LNI).

More information for submitting a research paper can be found at:
http://www.EUNetHealth.org

Most Popular Now

Philips Foundation 2024 Annual Report: E…

Marking its tenth anniversary, Philips Foundation released its 2024 Annual Report, highlighting a year in which the Philips Foundation helped provide access to quality healthcare for 46.5 million people around...

Giving Doctors an AI-Powered Head Start …

Detection of melanoma and a range of other skin diseases will be faster and more accurate with a new artificial intelligence (AI) powered tool that analyses multiple imaging types simultaneously...

Scientists Argue for More FDA Oversight …

An agile, transparent, and ethics-driven oversight system is needed for the U.S. Food and Drug Administration (FDA) to balance innovation with patient safety when it comes to artificial intelligence-driven medical...

New AI Transforms Radiology with Speed, …

A first-of-its-kind generative AI system, developed in-house at Northwestern Medicine, is revolutionizing radiology - boosting productivity, identifying life-threatening conditions in milliseconds and offering a breakthrough solution to the global radiologist...

AI Agents for Oncology

Clinical decision-making in oncology is challenging and requires the analysis of various data types - from medical imaging and genetic information to patient records and treatment guidelines. To effectively support...

New Research Finds Specific Learning Str…

If data used to train artificial intelligence models for medical applications, such as hospitals across the Greater Toronto Area, differs from the real-world data, it could lead to patient harm...

Start-ups in the Spotlight at MEDICA 202…

17 - 20 November 2025, Düsseldorf, Germany. MEDICA, the leading international trade fair and platform for healthcare innovations, will once again confirm its position as the world's number one hotspot for...

AI Medical Receptionist Modernizing Doct…

A virtual medical receptionist named "Cassie," developed through research at Texas A&M University, is transforming the way patients interact with health care providers. Cassie is a digital-human assistant created by Humanate...

AI Detects Hidden Heart Disease Using Ex…

Mass General Brigham researchers have developed a new AI tool in collaboration with the United States Department of Veterans Affairs (VA) to probe through previously collected CT scans and identify...

AI Tool Set to Transform Characterisatio…

A multinational team of researchers, co-led by the Garvan Institute of Medical Research, has developed and tested a new AI tool to better characterise the diversity of individual cells within...

MHP-Net: A Revolutionary AI Model for Ac…

Liver cancer is the sixth most common cancer globally and a leading cause of cancer-related deaths. Accurate segmentation of liver tumors is a crucial step for the management of the...

Human-AI Collectives Make the Most Accur…

Diagnostic errors are among the most serious problems in everyday medical practice. AI systems - especially large language models (LLMs) like ChatGPT-4, Gemini, or Claude 3 - offer new ways...