International Conference on AI-driven Data Science in Healthcare
(ICIADSH-26)

This track focuses on the integration of artificial intelligence techniques in the analysis of medical images. It aims to explore novel algorithms and methodologies that enhance diagnostic accuracy and efficiency in radiology and pathology.

This session will delve into the use of predictive analytics to forecast patient outcomes and optimize treatment plans. Researchers are invited to present innovative models that leverage big data to improve clinical decision-making.

This track emphasizes the development of machine learning systems that assist healthcare professionals in making informed clinical decisions. Contributions should highlight the effectiveness and reliability of these systems in real-world applications.

This session explores the role of data science in tailoring medical treatments to individual patient profiles. Papers should discuss methodologies that utilize genomic, phenotypic, and lifestyle data to enhance therapeutic outcomes.

This track aims to showcase advancements in AI technologies that facilitate the drug discovery process. Participants are encouraged to present case studies that illustrate the impact of AI on reducing time and costs in pharmaceutical research.

This session addresses the complexities and challenges associated with managing and analyzing big data in healthcare settings. Contributions should focus on innovative solutions that enhance data interoperability, security, and usability.

This track highlights the intersection of digital health technologies and artificial intelligence. Researchers are invited to discuss how AI can enhance telemedicine, mobile health applications, and patient engagement platforms.

This session will cover a range of data science techniques applied to biomedical research. Papers should present novel approaches to data analysis that contribute to advancements in understanding diseases and treatment efficacy.

This track focuses on the application of AI in diagnostic processes across various medical fields. Contributions should highlight innovative diagnostic tools and their impact on patient care and outcomes.

This session will explore how AI can be utilized to extract meaningful insights from electronic health records. Researchers are encouraged to present methodologies that improve patient care through data-driven decision support.

This track examines the role of AI technologies in real-time patient monitoring and management. Contributions should focus on systems that enhance patient safety and improve chronic disease management through continuous data analysis.