GCAIH 2027 – Scientific Sessions
Congress Theme: Artificial Intelligence in Healthcare: Applications and Future Perspectives
Session 1: Artificial Intelligence in Clinical Decision-Making and Patient Care
Description:
This session explores how artificial intelligence is supporting clinical decision-making, patient assessment, and care planning across different medical specialties. It will examine AI-assisted clinical workflows, decision-support systems, predictive tools, and the relationship between algorithmic recommendations and professional clinical judgement. Contributions addressing clinical validation, practical implementation, and the impact of AI on patient care are encouraged.
Sub-tracks:
- AI-Based Clinical Decision Support Systems
- Intelligent Patient Assessment and Risk Prediction
- AI-Assisted Treatment Planning and Clinical Workflows
- Human–AI Collaboration in Clinical Practice
Session 2: AI in Medical Imaging, Radiology and Diagnostic Sciences
Description:
This session focuses on the application of artificial intelligence in medical imaging and diagnostic interpretation. It will explore machine learning and deep learning approaches used in image analysis, disease detection, diagnostic accuracy, and clinical imaging workflows. Research examining model performance, clinical validation, interoperability, and the limitations of AI-assisted diagnostics is particularly relevant.
Sub-tracks:
- AI in Radiology and Medical Image Analysis
- Deep Learning in Imaging-Based Disease Detection
- AI Applications in Pathology and Laboratory Diagnostics
- Clinical Validation of AI-Assisted Diagnostic Technologies
Session 3: Predictive Analytics, Machine Learning and Precision Medicine
Description:
This session examines the role of machine learning, predictive modelling, and advanced analytics in understanding disease patterns and supporting personalised healthcare. It will address the use of clinical, genomic, demographic, and population-level data to predict health risks, identify disease progression, and inform individualised treatment strategies.
Sub-tracks:
- Machine Learning for Disease Prediction and Risk Stratification
- Predictive Modelling for Clinical Outcomes
- AI in Genomics and Personalised Medicine
- Data-Driven Approaches to Precision Healthcare
Session 4: AI in Surgery, Robotics and Image-Guided Interventions
Description:
This session explores emerging applications of artificial intelligence in surgical practice, robotic-assisted procedures, and image-guided interventions. It will consider intelligent surgical systems, computer-assisted planning, intraoperative decision support, and technologies designed to improve procedural precision and workflow efficiency. Contributions addressing clinical evaluation, safety, and the practical integration of AI-enabled surgical technologies are welcome.
Sub-tracks:
- AI-Assisted Surgical Planning and Navigation
- Robotic Surgery and Intelligent Surgical Systems
- Computer Vision in Intraoperative Monitoring
- AI in Image-Guided and Minimally Invasive Interventions
Session 5: Digital Health, Telemedicine and Remote Patient Monitoring
Description:
This session addresses the integration of artificial intelligence into digital healthcare environments, including telemedicine, connected care platforms, wearable technologies, and remote monitoring systems. It will explore how intelligent digital tools can support continuous health assessment, patient engagement, and care coordination across clinical and community settings.
Sub-tracks:
- AI-Enabled Telemedicine and Virtual Healthcare
- Wearable Devices and Intelligent Health Monitoring
- Remote Patient Monitoring and Predictive Alerts
- Digital Therapeutics and AI-Supported Patient Engagement
Session 6: Generative AI, Large Language Models and Healthcare Applications
Description:
This session examines the emerging role of generative artificial intelligence and large language models in healthcare research, clinical documentation, medical education, and professional communication. It will explore opportunities for responsible deployment while addressing accuracy, hallucinations, data protection, explainability, and the need for appropriate human oversight in healthcare applications.
Sub-tracks:
- Large Language Models in Clinical and Healthcare Settings
- Generative AI for Medical Documentation and Reporting
- AI-Assisted Medical Education and Knowledge Support
- Evaluation, Reliability and Human Oversight of Generative AI
Session 7: AI in Drug Discovery, Pharmaceutical Research and Biomedical Innovation
Description:
This session focuses on the use of artificial intelligence across pharmaceutical research, drug discovery, and biomedical innovation. It will explore computational approaches to identifying potential therapeutic targets, predicting molecular properties, supporting drug development, and analysing biomedical datasets. Research addressing translational applications, model validation, and the integration of AI into pharmaceutical workflows is encouraged.
Sub-tracks:
- AI-Driven Drug Discovery and Target Identification
- Computational Chemistry and Molecular Modelling
- Machine Learning in Drug Development and Repurposing
- AI Applications in Biomedical and Translational Research
Session 8: AI in Public Health, Epidemiology and Population Health Management
Description:
This session examines how artificial intelligence and data analytics can contribute to public health research, disease surveillance, epidemiological modelling, and population health planning. It will consider the use of predictive systems and large-scale health datasets to identify emerging health trends, assess population-level risks, and support evidence-informed public health interventions.
Sub-tracks:
- AI in Disease Surveillance and Outbreak Prediction
- Epidemiological Modelling and Health Risk Forecasting
- Population Health Analytics and Health Equity
- AI-Supported Public Health Planning and Intervention Strategies
Session 9: AI in Hospital Management, Healthcare Operations and Resource Optimisation
Description:
This session explores the application of artificial intelligence in healthcare administration, hospital operations, and institutional decision-making. It will examine intelligent resource allocation, patient flow management, operational forecasting, workflow automation, and data-driven approaches to improving healthcare service delivery. Contributions may address implementation challenges, organisational readiness, and measurable operational outcomes.
Sub-tracks:
- AI in Hospital Administration and Operational Management
- Intelligent Resource Allocation and Workforce Planning
- Predictive Analytics for Patient Flow and Service Demand
- Workflow Automation and Healthcare Process Optimisation
Session 10: Responsible AI, Healthcare Ethics, Data Privacy and Regulatory Governance
Description:
This session addresses the ethical, legal, regulatory, and governance considerations associated with artificial intelligence in healthcare. It will examine patient privacy, informed consent, algorithmic bias, transparency, accountability, clinical safety, and regulatory oversight. The session aims to encourage discussion on the principles and institutional practices required for responsible and trustworthy AI adoption.
Sub-tracks:
- Ethical Principles and Responsible AI in Healthcare
- Patient Data Privacy, Security and Informed Consent
- Algorithmic Bias, Fairness and Explainability
- Regulatory Frameworks, Accountability and AI Governance
Session 11: AI Research, Clinical Validation and Evidence-Based Implementation
Description:
This session focuses on the scientific evaluation and real-world implementation of artificial intelligence technologies in healthcare. It will explore research methodologies, clinical validation, model performance assessment, reproducibility, implementation science, and post-deployment monitoring. Contributions examining the gap between AI development and practical healthcare adoption are particularly encouraged.
Sub-tracks:
- Clinical Evaluation and Validation of AI Models
- Research Methodologies and Performance Assessment
- Implementation Science and Real-World AI Deployment
- Reproducibility, Model Monitoring and Post-Deployment Evaluation
Session 12: Emerging Technologies, Future Directions and Interdisciplinary Healthcare Innovation
Description:
This forward-looking session explores emerging technologies and interdisciplinary developments that may shape the future of artificial intelligence in healthcare. It will consider advances in intelligent healthcare systems, multimodal AI, digital twins, federated learning, next-generation clinical technologies, and collaborative innovation. Contributions examining future research priorities, technological opportunities, and pathways for responsible innovation are welcome.
Sub-tracks:
- Multimodal AI and Next-Generation Healthcare Technologies
- Digital Twins and Intelligent Healthcare Simulation
- Federated Learning and Collaborative Health Data Research
- Future Research Directions and Interdisciplinary Innovation
The scientific programme of GCAIH 2027 is supported by an academic organising team committed to interdisciplinary engagement and scientific exchange. Explore the Organising Committee Members contributing to the congress.
