Exploring Machine Learning and Explainable AI in Medical Data Analysis

Organisers

Length

2 hours

Description

The processing of medical data, including images, text, and time series, is an area of research that is rapidly expanding and evolving, with numerous exciting developments and applications emerging in recent years. The utilisation of these various types of data is fundamental in healthcare, playing a critical role in diagnosing, monitoring, and treating a wide array of diseases and conditions. Machine Learning (ML) techniques, such as Deep Learning (DL), have demonstrated their effectiveness in analysing medical data, particularly images such as X-rays, CT scans, and MRI scans, leading to improved diagnosis, treatment, and patient outcomes. The integration of ML and DL in healthcare represents a significant advancement in processing and comprehending complex data, uncovering patterns and insights at a scale and speed that would be unattainable by humans alone. This development creates new opportunities for early disease detection, precision medicine, and predictive health management. However, the effectiveness of these technologies depends on their explainability. Explainable Artificial Intelligence (XAI) plays a crucial role in ensuring transparency in AI decision-making, thereby empowering healthcare professionals to confidently utilize insights derived from AI.

This special session will bring together experts from both Machine Learning and medical fields to delve into the latest research findings, challenges, and opportunities in this dynamic field. The session will also include works on XAI methods and their integration into AI systems for medical analysis, fostering trust, accountability, and the promotion of responsible, patient-centric AI applications in healthcare.

Category/Keywords:

• Medical Data Processing

• Machine Learning (ML) in Healthcare

• Deep Learning (DL) in Medical Imaging

• Explainable Artificial Intelligence (XAI)

• XAI for Healthcare

• Integrating ML/DL in Healthcare

• Patient-Centric AI Applications

• Healthcare Data Analytics

• Predictive Health Management

• Synthetic Medical Image Generation

• Precision Medicine

• Early Disease Detection

• Medical Image Analysis

• Time Series Analysis in Healthcare

• AI Transparency

• Trust in medical AI Systems

• Responsible AI in Healthcare

• AI Ethics in Medicine

• Clinical Decision Support Systems

• Challenges in ML/DL for Healthcare

• Opportunities in Medical AI Research

Submission Instructions

Submission to the workshop follows the same procedures as for main conference papers. This workshop supports both ordinary and short paper submissions.  please select the specific Special Session name you are interested in the "additional questions" part of the submission.

Please note that submission dates are the same as per the main conference schedule.