Cancer immunotherapy and artificial intelligence: Building a robust educational framework for future physicians 2965
Abstract Description Background Rapid advancements in cancer immunotherapy have transformed oncology, offering new treatments across malignancies. The complexity of this field necessitates targeted education to prepare future healthcare professionals, with programs bridging foundational science, research, and clinical practice. Methods Medical educators developed a cancer immunotherapy elective for students, covering immune checkpoint inhibitors, T-cell therapy, cancer vaccines, immune-related adverse events, and the underlying role of Artificial Intelligence (AI). The curriculum included a primer, quizzes, discussions, and final presentations. To assess the course and gather student feedback, a survey was conducted at the end of the course. Results This elective provided students with essential skills in cancer immunotherapy, enhancing their capacity to manage immune-related adverse events. Feedback indicated increased understanding and interest, with students valuing collaboration and mentorship. Surveys are ongoing to assess perspectives on AI integration. Conclusions This elective represents progress in medical education, equipping future physicians for modern immuno-oncology treatment. Integrating AI into medical education provides students with opportunities to remain up to date with advancements in cancer immunotherapy, highlighting its critical role in driving innovation and improving patient care. Topic Categories Immunology Education and Communication (EDU)
Authors
- Jennifer T. Grier (ORCID: https://orcid.org/0000-0002-9331-0280)
- Gert Mayer (ORCID: https://orcid.org/0000-0002-5039-543X)
- Jessica Ann Chacon (ORCID: https://orcid.org/0000-0002-3657-7528)
Institutions
- Texas Tech University (US)
- University of South Carolina (US)
- Texas Tech University Health Sciences Center (US)
Publication Details
- Journal
- The Journal of Immunology
- Published
- 2025-11-01
- DOI
- https://doi.org/10.1093/jimmun/vkaf283.819
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00