
Explainability is a key consideration in healthcare, as AI systems are increasingly adopted in healthcare—from aiding medical diagnoses to optimising treatment planning and predicting patient outcomes—explainability is pivotal for trust and effective adoption. Integrating xAI methods enables healthcare practitioners to gain deeper insights into AI decisions, fostering trust and accountability in clinical environments. Despite the advancements, deploying xAI in healthcare involves complex challenges, including aligning model interpretability with clinical accuracy, integrating xAI seamlessly into workflows, and addressing domain-specific data complexities such as medical imaging and real-time monitoring systems. Key hurdles also arise from ethical and regulatory dimensions, such as ensuring fairness, mitigating bias, and maintaining data privacy, all while adhering to stringent compliance standards. Novel xAI approaches, including interactive visualisations and personalised explanation tools, offer promising avenues to improve human-AI collaboration. Evaluation techniques such as cognitive load analysis and usability testing are critical to refining these tools for both medical professionals and patients. This session aims to delve into state-of-the-art xAI methodologies, practical applications, and their implications in healthcare settings. By fostering discussions on innovative xAI strategies, the session seeks to address pivotal challenges, promote patient-centric solutions, and ultimately contribute to a future where AI enhances healthcare efficacy and ethics.
Topics
- Advanced xAI methods for medical imaging interpretation
- Time-series analysis explainability for patient monitoring
- Personalisation of xAI for healthcare professionals and patients
- Counterfactual explanations in treatment recommendation systems
- Ethical considerations in healthcare xAI deployment
- Privacy-preserving explainable AI methods
- Trust evaluation metrics in xAI-driven healthcare applications
- Explainability for federated learning in clinical settings
- Cognitive load assessments in xAI tools
- Explainable AI for multi-modal healthcare data fusion
- Human-centric design in medical AI systems
- Regulatory frameworks and compliance for xAI in healthcare
- SHAP and LIME applications in clinical decision support
- Visual explanation techniques for diagnostic AI systems
- Explainability in AI-driven drug discovery
- Fairness and bias mitigation in healthcare AI models
- Integration of xAI in electronic health record systems
- Explainability for predictive models in disease progression
- Usability studies of xAI in telemedicine applications
- Interactive tools for real-time AI explanation in emergency care




