AI is increasingly shaping modern healthcare through advances in medical imaging, signal processing, predictive analytics, and personalised medicine. However, translating these advances into resource-constrained settings such as low- and middle-income countries, rural regions, and under-resourced health systems raises critical challenges related to data scarcity, heterogeneous data quality, limited computational infrastructure, workforce constraints, and heightened ethical and safety concerns. This special session focuses on the design, evaluation, and deployment of Trustworthy AI for healthcare, emphasising methods that are explainable, reliable, fair, privacy-preserving, and clinically meaningful under real-world constraints. Aligned with the AIiH conference themes, the session invites contributions spanning AI-aided medical imaging and signal processing, predictive and proactive healthcare, precision and personalised medicine, digital pathology and neurology, mental health, and healthcare workflow optimisation. Particular emphasis is placed on approaches that incorporate explainability, uncertainty awareness, robustness, and human-centred design to foster clinician trust and support safe decision-making. The session also welcomes work on ethical AI, patient data privacy, federated and decentralised learning, and scalable deployment strategies that enable sustainable adoption in constrained environments. Methodological advances, multi-modal learning frameworks, large-scale cohort analyses, and real-world case studies demonstrating clinical impact are especially encouraged. By bringing together interdisciplinary perspectives from AI, medicine, and health systems research, this special session aims to advance practical, responsible, and deployable AI solutions that can meaningfully improve healthcare delivery and outcomes in resource-constrained settings.
The focus of this special session is on the following topics:
- Trustworthy, Explainable, and Ethical AI for Healthcare
- Predictive Analytics and Proactive Care in Low-Resource Settings
- AI-Aided Medical Imaging and Signal Processing under Data Constraints
- Precision Medicine and Digital Twinning with Limited Clinical Resources
- Deployable AI Systems for Healthcare Workflows and Mental Health
Organisers:
- Dr. Hassan Aqeel Khan, Aston University, UK h.khan54@aston.ac.uk
- Dr. Muhammad Naseer Bajwa, National University of Sciences and Technology, Pakistan naseer.bajwa@seecs.edu.pk
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.