Bridging the Gap: A Literature Review on the Integration of Process Mining and AI Techniques

This presentation examines recent advances in combining process mining with artificial intelligence, focusing on deep learning and explainable AI techniques. Based on a review of 32 studies (2019–2025), it highlights how models such as LSTMs, GRUs, and temporal convolutional networks consistently improve predictive accuracy while enabling greater transparency in decision-making. Applications span healthcare, including patient pathway prediction, and business domains like workflow and e-commerce optimization. Key challenges, such as limited adaptability to real-world dynamics and insufficient expert validation—are also addressed. Attendees will gain insights into future directions for developing comprehensive, dynamic frameworks that extend beyond next-event prediction and enhance operational decision support.

Identify how integrating deep learning and explainable AI techniques enhances predictive monitoring and interpretability in process mining.

Propose future directions for developing adaptive, domain-validated frameworks that extend beyond next-event prediction to improve real-world decision support.

Evaluate current applications of AI–process mining frameworks across healthcare and business domains, including their benefits and limitations.