Special Session: Harnessing the Power of Artificial Intelligence to Improve Outcomes for Patients with for Long-Term Health Conditions
Long-term health conditions (LTHCs), such as cancer, diabetes, cardiovascular diseases, chronic respiratory conditions, and mental health disorders, pose significant challenges to healthcare systems worldwide. These conditions require continuous monitoring, personalized treatment plans, and proactive interventions to improve patient outcomes and reduce healthcare costs. Artificial Intelligence (AI) has emerged as a transformative tool in addressing these challenges, offering innovative solutions for early diagnosis, predictive analytics, personalized medicine, and remote patient monitoring.
This special session aims to explore the latest advancements in AI-driven technologies and methodologies tailored to improving outcomes for patients with long-term health conditions. It will bring together researchers, clinicians, and industry experts to discuss cutting-edge AI applications, share success stories, and address the ethical, technical, and practical challenges of implementing AI in this domain.
This special session is open to submissions, and the topics of Interest include (not limited to):
- AI-driven predictive models for early detection and risk stratification of long-term conditions.
- Personalized treatment recommendations using machine learning and decision support systems.
- Remote monitoring and wearable technologies for chronic disease management.
- Natural language processing (NLP) for analyzing electronic health records (EHRs) and patient-reported outcomes.
- Ethical considerations, bias mitigation, and trust in AI for long-term healthcare.
- Real-world implementation challenges and success stories of AI in chronic care.
We invite researchers and practitioners to submit original research, case studies, or position papers related to the session theme. Submissions should highlight innovative AI methodologies, practical applications, or policy implications for improving outcomes in long-term health conditions.
Organisers:
- Shang-Ming Zhou, Centre for Health Technology, Faculty of Health, University of Plymouth, shangming.zhou@plymouth.ac.uk
- Neil Vaugan, University of Exeter Medical School
- Xu Wang, Centre for Health Technology, Faculty of Health, University of Plymouth
- Sherif Shazly, Leeds Teaching Hospital
- Ian Overton, School of Medicine, Dentistry and Biomedical Sciences, Queen’s University Belfast
Submission format:
Authors are encouraged to submit original research articles, reviews, or case studies. This Special Session welcomes both full length papers (12 pages plus up to 2 pages of references) and abstracts (up to 5 pages including references). Click here for detailed submission guidelines and templates.
Deadline:
All deadlines, including submission deadline and review timeline are the same as the main conference. Please follow this link to see all the Important Dates.
If you have any questions regarding this Special Session, please contact the organiser.