AI for Health: Closing the Loop from Research to Applications

Organisations and industries of all disciplines are reaping the benefits of artificial intelligence and machine learning techniques to improve their decision-making process. The health industry, in particular, being a data-intensive field, has a lot of AI applications such as analysing Gene sequencing data or medical images for diagnostic decision making, analysing electronic health records for improving patient treatment process, and recommendation of personalised treatments based on heterogeneous data. Multitude of machine learning, and data mining methods are applied in this domain from statistics, regression and clustering to neural networks, and network science.

Healthcare data present a unique set of challenges, including high dimensionality, class imbalance, low numbers of samples, and limited interpretability. Healthcare data are captured in different formats, including numeric, textual reports, signals and images, and sourced from different systems and providers. Integrating and analysing such complex dataset to deliver a meaningful outcome to the end-user in a challenging task that requires a sound understanding of the domain knowledge coming from ontologies, annotation repositories, and domain experts. This requires close collaboration between researchers and end-users of AI applications. Moreover, trust and interpretability are crucial when bringing the research outcomes into practice.

This workshop aims to bring AI researchers and health industry domain experts together to present and discuss the latest research in AI for health, and the challenges and opportunities in translating AI research to shape the future of healthcare applications. It will attract healthcare practitioners who have access to interesting data sources and looking for expertise and methodologies to leverage AI techniques effectively. Special attention will be devoted to building end-user trust through interpretability and transparency of AI, ethics and novel technologies such as blockchain.

Call for papers

We invite high-quality contributions related to following list of topics and themes from researchers and industry practitioners.

List of Topics:

• Applications of artificial intelligence for health care

• Health informatics

• Data-driven precision medicine

• Semantic web for health analytics applications

• Software architectures for health data management and analytics

• Navigating ethics for AI in health

• Visual analytics for health data

• Blockchain in health care

• Using synthetic data for AI in health

Submission Details

Authors can submit full papers (8-10 pages) for the paper presentation session or 4 page short papers for ‘Rapid fire’ poster presentation session.

Authors submit for the ‘Rapid fire’ poster presentation session will be conducting 5-minute poster presentations back-to-back. They are timed exactly, and the speakers are moved at the end of their reserved time. The poster presentation session can be present preliminary and ongoing research work with vision for the future of AI in healthcare.

Please aim at 8-10 pages for paper session and 4 page short papers for Rapide fire session in the IEEE two-column style.

(templates at https://www.ieee.org/conferences/publishing/templates.html).

All accepted submissions will be published in the conference workshop proceedings. The Proceedings will be published by the IEEE Computer Society Press and be available through IEEE Xplore and the IEEE Digital Library. One author from each accepted submission is required to register as a delegate and present at the workshop.

Important Dates (Updated)

Paper submission deadline: Extended till 22 August 2021 (11:55 pm AoE)

Paper acceptance notification: 13 September 2021

Camera-ready paper due: 27 September 2021

Author registration: 27 September 2021

Workshop sessions: 25 October 2021 4 pm (Brisbane, Australia Day/Time (UTC/GMT +10 hours)

Workshop Link: Register HERE to recieve the link.

Organizers