JMIR Publications

ABSTRACT

Clinician educators occupy a demanding role at the intersection of clinical practice, teaching, and scholarship. Given their teaching roles they are expected to contribute in particular to medical education scholarship, yet many have little protected time for one of the first few steps of scholarship: the literature review. Further they also struggle to find support to help refine their academic writing. Artificial intelligence (AI) tools have emerged as practical supports for exactly these two activities: conducting early-stage literature reviews and improving scholarly writing. A barrier, however, is that clinician educators are often unaware of which tools are genuinely suited to academic work, and popular general-purpose search assistants are easily mistaken for rigorous scholarly tools. This viewpoint offers a curated perspective on AI platforms, based on the experience of the author group, that can be useful for medical education research, including Consensus, Elicit, Scite, Litmaps, Research Rabbit, Paperpal, Jenni-AI and Claude. While this is not an empiric study or a comprehensive review of AI-research platforms or an exhaustive list, the viewpoint aims to introduce clinician educators to a few tools and highlight how these tools can retrieve and synthesize information. We also address some ethical considerations, including hallucinated content, citation inaccuracies, transparency in disclosure, and subscription costs. Rather than advocating for any single tool, we encourage clinician educators to adopt a deliberate, multi-platform approach, matching tools to each stage of the scholarly process, supported by institutional governance and training.