Mapping the Immediate Trajectory of Retinal Disease Research: A Large Language Model-Assisted Bibliometric Analysis

Technical Summary: Mapping the Immediate Trajectory of Retinal Disease Research

Problem Statement
Retinal diseases, particularly age-related macular degeneration (AMD) and diabetic retinopathy, represent a critical global public health challenge. While the field is undergoing rapid transformation driven by novel pharmacological interventions, genomic medicine, and artificial intelligence (AI), traditional bibliometric methods struggle to capture these immediate shifts. Conventional approaches relying on lifetime citation counts suffer from temporal bias, favoring older publications simply due to their longevity. Furthermore, reliance on broad author-provided keywords or generic Medical Subject Headings (MeSH) often lacks the semantic granularity required to distinguish between foundational algorithmic development and real-world clinical implementation. Consequently, there is a methodological gap in identifying the "immediate trajectory" of high-impact research, potentially delaying the recognition of emerging clinical paradigms by policymakers and funding agencies.

Methodology
To address these limitations, the authors conducted a retrospective, large language model (LLM)-assisted bibliometric analysis comparing influential literature from two consecutive cohorts: papers published in 2023 (Class of 2025) and 2024 (Class of 2026).

• Data Source and Selection: Bibliographic records were retrieved from the OpenAlex database, restricted to the research topic "Retinal Diseases and Treatments" (T10170) and limited to journal articles.
• Citation Metric: To normalize for the age of papers, influence was ranked by an 18-month citation window (citations received within 18 months of publication) rather than lifetime counts. The top 50 papers per cohort were selected using an exact threshold-expansion algorithm.
• LLM-Assisted Screening: An initial pool of 75 papers per cohort (ranked by the 18-month metric) was screened using the Claude Sonnet LLM to verify topical relevance to retinal diseases, ensuring the final cohort of 50 papers per year was strictly on-topic.
• Concept Extraction and Normalization:
  • Extraction: The LLM extracted specific concepts (methods, entities, interventions, biomarkers, imaging modalities) from titles and abstracts, limiting extraction to 20 concepts per paper.
  • Normalization: Extracted strings were deduplicated and clustered using embedding-based similarity (Google Gemini Embedding). Synonymous terms were merged into 874 canonical topics using DeepSeek V4 Pro.
  • Thematic Grouping: Canonical topics were assigned to 89 parent themes via a separate LLM pass.
• Analysis: Frequencies were normalized by cohort size (50 papers). The study calculated frequency differences and fold changes between the 2023 and 2024 cohorts to identify shifts in research emphasis. No inferential statistical tests were applied; the analysis was descriptive.

Key Results
The analysis revealed a distinct shift in research focus from foundational technical development toward clinical application and specific disease management.

• Dominant Themes: "Diabetic retinopathy and diabetic eye disease" remained the dominant parent theme in both cohorts, with a normalized frequency rising from 0.48 (2023) to 0.50 (2024).
• Emerging Topics (2024 vs. 2023):
  • Clinical AI & Grading: "Diabetic retinopathy grading" showed the largest positive shift (+0.10). "Large language models" and "Genome-wide association study" each emerged with a +0.06 increase.
  • Genetics: "Retinal degenerative and inherited diseases" and "Diabetes mellitus and its complications" both rose by +0.06 and +0.04, respectively.
• Declining Topics:
  • Technical Foundations: "Convolutional neural network" (CNN) declined significantly (-0.22), as did "Fundus imaging" (-0.20). Specific architecture terms like "ResNet-50" and "Automated diabetic retinopathy detection" also saw sharp decreases.
  • Therapeutics: "Anti-VEGF therapy" and "Age-related macular degeneration" (as a parent theme) declined by -0.06, reflecting a shift away from the initial landmark trials of complement inhibitors (pegcetacoplan, avacincaptad pegol) that dominated the 2023 cohort.
• Topical Shifts: The 2023 cohort was characterized by complement-inhibition trials and CNN-based detection. The 2024 cohort emphasized diabetic retinopathy prediction/screening, inherited retinal disease gene therapy (e.g., CEP290 trials), and real-world safety reporting for anti-VEGF agents.

Key Contributions

1. Methodological Innovation: The study introduces a bias-resistant framework combining a fixed early-citation window with LLM-driven semantic extraction. This approach effectively isolates immediate research impact and captures nuanced transitions that traditional keyword co-occurrence networks miss.
2. Granular Taxonomy: By normalizing 1,255 raw terms into a two-tier taxonomy (89 parent themes, 874 canonical topics), the study provides a high-resolution map of thematic evolution, distinguishing between generic field labels and specific clinical or technical entities.
3. Empirical Resource: The authors provide a web application (https://pri.pepkio.com) and a public GitHub repository containing the source code and data, enabling interactive exploration of these trends.

Significance and Claims
The paper claims that its findings indicate a maturation of AI in ophthalmology, shifting from "proof-of-concept" diagnostic accuracy on closed datasets to pragmatic clinical deployment focused on disease progression prediction and real-world decision support. The study suggests that the field is moving beyond foundational imaging-based machine learning toward clinical diabetic retinopathy management, genomic studies, and deployed AI tools.

The authors position this work as an objective resource for identifying imminent clinical and technological paradigms. They argue that without such dynamic, bias-resistant frameworks, stakeholders risk relying on outdated trends. The study concludes that while diabetic retinopathy remains central, the trajectory of high-impact research is increasingly defined by the integration of precision medicine (gene therapy), genomic analysis, and clinically deployed AI, rather than the development of foundational algorithmic architectures alone.

Limitations Acknowledged
The authors modestly note that the 18-month window excludes "sleeping beauties" (papers with delayed impact), the reliance on OpenAlex and English-language articles may introduce regional/linguistic bias, and LLM-based extraction, while high-fidelity, remains susceptible to subtle contextual misinterpretations.