On July 10, 2025, the AISSLab team led by Professor Mugahed A. Al-antari from Sejong University’s Department of Artificial Intelligence and Data Science successfully held the 2025 Expert Invitation Seminar, “Explainable Hybrid AI Workflow for MRI-based Lumbar Stenosis Prediction,” at the Daeyang AI Center.
The event, organized by Professor Al-antari and AISSLab, welcomed Professor Jamil Hussain, AISSLab researchers, and department students. The two-hour seminar featured a welcoming speech, a presentation on AISSLab’s latest research, a special invited lecture, an in-depth Q&A and discussion session, gift presentations, and a group luncheon.
A highlight of the seminar was the keynote by Professor Inbo Han, current chair of the Basic Science Research Society of the Korean Neurosurgical Society and former editor-in-chief of the Q1 international journal Neurospine. In his lecture, “Medical Perspective on Lumbar Spinal Diseases & AI Collaboration,” Professor Han emphasized the growing efforts in the medical field to use AI for enhanced spinal disease diagnostics particularly for pinpointing pain-inducing discs among the many in the spine. He noted the increasing adoption of natural language processing (NLP) to quantify symptoms and pain, as well as the expanding usefulness of AI in all stages of spinal disease management: from preoperative planning and surgical precision to postoperative recovery monitoring.
Professor Han further highlighted that lumbar spinal stenosis the core research focus of AISSLab is a critical issue in spinal medicine. He encouraged AISSLab to continue pushing to apply AI not only to stenosis but also across all facets of spinal disease research, expressing hope for more collaboration in the future.
Reflecting on the seminar’s significance, Professor Al-antari stated, “Practical collaboration between AI experts at Sejong University and medical professionals is a key factor in the advancement of medical AI. Such partnerships deepen disease understanding, enhance data quality, and narrow the clinical-technical divide. We are committed to developing explainable VLLM solutions for complex diseases and driving medical innovation.”
This seminar underscored AISSLab’s commitment to leveraging AI for high-impact medical breakthroughs and fostering meaningful collaborations between academia and healthcare. Please visit the office news [Link]
