We previously found that automated indexing in PubMed and Medline increased the risk of inappropriately excluding human studies when using the human studies filter from the Cochrane Highly Sensitive Search Strategy (HSSS). Following the National Library of Medicine's transition from the Medical Text Indexer-Auto (MTIA) to the Medical Text Indexer-Next Generation (MTIX) in 2024, we wanted to assess whether that was still the case. Using the same approach as our previous study, we reviewed a set of MTIX-indexed records that were excluded by the HSSS. Only 1.1% turned out to be human studies that were inappropriately excluded - a major improvement over not only MTIA but also human indexers in our previous comparison. However, problems remain with automated indexing and search filters. This commentary outlines our findings and our ongoing concerns.

Medical librarians have long observed that medical students who struggle with reading complex textbooks also demonstrate weak information literacy skills. For decades, these have been treated as separate problems requiring separate solutions-offering reading workshops and database training sessions with limited lasting impact. This commentary proposes a reconceptualization: textbook literacy and information literacy are not sequential competencies but rather parallel manifestations of the same underlying cognitive architecture. Drawing on research in medical education, cognitive psychology, and educational neuroscience, this commentary argues that both literacies depend on three interconnected cognitive capacities: metacognitive self-regulation, working memory management, and epistemic sophistication. Understanding this shared foundation has profound implications for how health sciences librarians design interventions, plan instruction, and position themselves as essential contributors to medical education. This commentary presents a framework and outlines practical implications for library instruction, assessment, and collaboration with medical educators.

[This corrects the article DOI: 10.5195/jmla.2026.2244.].

In the fall of 2023, the Journal of the Medical Library Association (JMLA) introduced its policy on generative artificial intelligence (AI) submissions, predicting that these tools would become embedded within the workflows of clinicians, educators, researchers, and students. Those predictions dramatically underestimated AI's impact. On the three-year anniversary of that editorial, we revisit the issue in light of AI's unprecedentedly rapid adoption across knowledge work. We examine the educational challenges AI poses, arguing that excessive cognitive offloading threatens the deep thinking on which learning depends. We then consider the challenges AI creates for researchers, from hallucinated citations to fabricated diseases. We suggest that health sciences librarians, who offer expertise in approaching information systems with discernment, are well suited to be leaders in this space. To help prospective JMLA authors weigh whether and when to use AI, we propose the Compliance, Verifiability, and Disclosure (CVD) Framework. Throughout, we reaffirm our conviction that education and research must remain human-centered endeavors.