SCOPE
Artificial intelligence is moving from proof-of-concept studies to everyday use in clinical practice and in the management of health services. This shift raises questions that accuracy alone does not answer: whether a model can be trusted by the clinician or the manager who has to act on it, whether its risk estimates hold up across hospitals and populations, and whether the efficiency gains it promises can be measured.
The workshop brings together researchers working on neural, machine learning and hybrid methods for health systems, at both the clinical and the organizational level. It welcomes contributions on predictive models built on clinical data, biosignals and medical images, on the measurement and improvement of hospital performance, and on the explainability, calibration and validation that make these models usable in real decisions. Methodological papers, applications on real-world data and critical evaluations, including negative results, are equally welcome.
The aim is to give the community working on AI in healthcare a recognizable place within WIRN, where neural network research meets the constraints of health systems.
TOPICS
Topics include, but are not limited to:
- Neural and machine learning models for clinical risk prediction and decision support
- Deep learning on biosignals and medical images
- Explainable and interpretable AI for clinical and managerial decisions
- Risk stratification, calibration and threshold selection in clinical models
- Hospital efficiency, performance analytics and resource allocation with AI
- Hybrid methods combining machine learning, optimization and efficiency analysis
- Federated and privacy-preserving learning on health data
- Foundation models and large language models in healthcare
- AI and IoT for remote monitoring and connected care
- Machine learning for systematic reviews and evidence synthesis
- Validation, generalization and bias of health AI on real-world data
- Governance, evaluation and adoption of AI in health organizations
PROGRAM CO-CHAIRS
- Agostino Marengo, Department of Agricultural Sciences, Food, Natural Resources, and Engineering (DAFNE), University of Foggia, Italy – agostino.marengo@unifg.it
- Vito Santamato, Department of Agricultural Sciences, Food, Natural Resources, and Engineering (DAFNE), University of Foggia, Italy – vito.santamato@unifg.it
- Anna Esposito, Department of Psychology, Università della Campania “Luigi Vanvitelli”, Italy – anna.esposito@unicampania.it
- Lerina Aversano, Department of Agricultural Sciences, Food, Natural Resources, and Engineering (DAFNE), University of Foggia, Italy – lerina.aversano@unifg.it
- Antonio Perna, Department of Psychology, Università della Campania “Luigi Vanvitelli”, Italy – antonioperna@unicampania.it
IMPORTANT DATES
- Extended Abstract Submission Deadline: January 31, 2027
- Notification of Acceptance: February 28, 2027
- Camera-ready Deadline: April 30, 2027
- Workshop Dates: June 9–11, 2027
PUBLICATION AND PAGE LIMITATIONS
- Contributions will be published on the SPRINGER “Smart Innovation Systems and Technologies” (see Springer Guidelines for formatting guidelines and templates). The minimum paper length is 15 pages.
- Manuscripts should be submitted through [Submission LINK TBA] by selecting “Artificial Intelligence for Health Systems: Prediction, Efficiency and Explainability” from the dropdown menu.
REGISTRATION FEE
Registration must be done through bank transfer and receipts must be exhibited at the conference.
Details can be found at
https://siren.science/registration.html