AI in healthcare is transforming the field by improving diagnostics, aiding in medical imaging analysis, personalizing treatment, and supporting clinical decision-making. It enables faster and more accurate analysis of medical data, enhances drug discovery, and assists in robot-assisted surgeries. AI also contributes to predictive analytics, virtual assistants, wearable devices, and clinical decision support. However, it is important to remember that AI is a tool to support healthcare professionals rather than replace them, and ethical considerations and data privacy are crucial in its implementation.
This track is devoted to discussions and exchange of ideas on questions like:
- Explainability and Interpretability: How can AI algorithms be made transparent and understandable to healthcare providers and patients?
- Data Quality and Integration: How can diverse healthcare data sources be integrated while ensuring data quality and interoperability?
- Ethical and Legal Considerations: What ethical and legal frameworks should be established to address privacy, consent, bias, and responsible AI use?
- Validation and Clinical Implementation: How can AI algorithms be rigorously tested and integrated into clinical workflows?
- Robustness and Reliability: How can AI systems be made robust, reliable, and adaptable to changing patient populations and data quality?
- Human-AI Collaboration: How can AI systems effectively collaborate with healthcare professionals?
- Long-term Impact and Cost-effectiveness: What is the long-term impact and cost-effectiveness of AI in healthcare?
- Regulatory and Policy Frameworks: What regulatory and policy frameworks are needed for the development and deployment of AI in healthcare? These research questions drive efforts to address technical, ethical, legal, and societal challenges to maximize the benefits of AI in healthcare.
Note that this text was mostly generated using ChatGPT.
Please submit your contributions via EquinOCS
Track Organizers 🔗
|University of Lübeck, DE