Flex Conference (Physical / Digital)

International Conference on AI-driven Data Science and Machine Learning Applications - (ICADSL-26)

20th - 21st October 2026 , Toronto - Canada

Registration Options

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Call For Papers

The (ICADSL-26) emphasizes interdisciplinary collaboration by bringing together experts from diverse fields. It encourages research that integrates multiple perspectives to address complex global challenges.

Key areas such as Artificial Intelligence,Data Science,Machine Learning are explored to promote cross-domain knowledge exchange and collaborative innovation.

Authors are invited to submit papers addressing, but not limited to, the following areas:

  • AI applications in healthcare settings
  • Machine learning for patient data analysis
  • Predictive analytics for clinical outcomes
  • Data-driven decision making in medicine
  • Ethical implications of AI in healthcare
  • AI in personalized treatment plans
  • Natural language processing for clinical data
  • Real-time analytics in patient monitoring
  • Machine learning for disease diagnosis
  • Data integration challenges in healthcare
  • Impact of AI on healthcare efficiency
  • AI-driven tools for medical research
  • Healthcare applications of deep learning
  • Patient privacy and data security issues
  • AI in telemedicine and remote care
  • Collaborative AI systems in healthcare
  • Future directions in AI and healthcare
  • Machine learning for health disparities research
  • AI-enhanced patient engagement strategies
  • Data ethics in AI applications

Assessment

All submissions will undergo peer review to ensure quality and interdisciplinary relevance. Accepted papers will be presented and considered for publication in journals and conference proceedings.

Registration

Registering for the conference provides access to keynote sessions, technical presentations, and networking opportunities with global experts.

Publication

Publishing through the conference enhances the visibility of your research and connects your work with a broader academic audience.