In just over two years, papers on large language models (LLMs) in medicine have accumulated nearly fifteen thousand citations, creating an academic canon that is already shaping funding decisions, regulatory conversations, and clinical experimentation. This study dissects the 100 most-cited LLM-in-medicine papers to show who is driving the field, which applications dominate attention, and where the evidence remains dangerously thin. What emerges is a picture of rapid intellectual consolidation—paired with a widening gap between technical promise and clinical reality.
The article by Huang et al. is not a clinical trial, nor does it attempt to measure patient outcomes. Instead, it is a bibliometric analysis of the 100 most-cited publications on LLMs in medicine, designed to map influence rather than effectiveness. By examining citation counts, publication venues, countries of origin, institutional affiliations, and thematic clusters, the authors aim to identify which ideas and research directions are shaping the field’s shared reference points.
Using the Web of Science database, the authors searched for publications between November 2022 and February 2025 that referenced large language models, generative AI systems, or well-known model families such as GPT and ChatGPT. After screening and adjudication by multiple reviewers, the top 100 papers were selected purely on the basis of citation counts. Analytical tools including CiteSpace and bibliometrix were then used to explore citation dynamics, keyword clustering, and collaboration networks. The full study is available via its DOI:
https://doi.org/10.1177/20552076251365059
The results highlight the extraordinary speed with which the field has coalesced. Nearly all of the top-cited papers were published in 2023, yet together they amassed more than 14,800 citations by early 2025. This concentration reflects both intense interest and a structural feature of fast-moving fields: early publications often become default references, regardless of whether their claims have been validated in real-world settings.
The content of these influential papers is revealing. Most original studies focus on performance benchmarking—evaluating how well LLMs answer medical questions, pass licensing-style exams, summarize clinical text, or interpret radiology reports. Reviews synthesize these early findings and speculate on future applications. What is largely absent are prospective trials, longitudinal studies, or rigorous evaluations of how LLMs change clinician workflows, patient outcomes, or safety profiles once deployed in practice.
Geographically, the canon is dominated by the United States, which accounts for more than half of the top-cited papers. Elite academic centers, particularly Stanford and other major U.S. institutions, appear repeatedly. This concentration raises questions about generalizability, as the clinical environments, regulatory frameworks, and data resources reflected in these studies may not represent global healthcare realities.
Journal placement also plays a critical role. Digital health and medical informatics journals contribute a large share of the volume, while high-impact clinical journals publish fewer but disproportionately influential papers. This combination shapes both the technical direction of research and its perceived legitimacy within mainstream medicine.
Perhaps the most important contribution of the study lies in its discussion of what is missing. Despite frequent references to ethics, equity, and privacy, actionable frameworks for bias mitigation, informed consent, liability, and governance are comparatively underdeveloped in the highly cited literature. Workflow integration and clinician burden—factors that historically determine whether health IT succeeds or fails—receive far less empirical attention than model accuracy or response quality.
The authors interpret these patterns as evidence of a growing bench-to-bedside gap. LLMs are being evaluated primarily as technical artifacts, rather than as socio-technical interventions embedded in complex clinical systems. Without stronger emphasis on implementation science, prospective evaluation, and patient-centered outcomes, there is a risk that deployment will outpace evidence.
For clinicians and health system leaders, the implication is clear: citation volume should not be mistaken for clinical readiness. High-profile LLM studies offer valuable proof of concept, but local validation, governance structures, and human oversight remain essential. For researchers, the next phase of impact will likely come not from additional benchmarks, but from rigorous trials that measure safety, equity, workflow effects, and real-world clinical value.
Viewed in this light, the study serves as both a map and a warning. It shows where the intellectual energy of medical AI is currently concentrated, while underscoring how much work remains before LLMs can be responsibly integrated into everyday care. The future influence of the field may depend less on how well models perform in isolation, and more on how carefully they are tested, governed, and aligned with the realities of clinical practice.
