Clinical Scorecard: Evaluating Large Language Models for Detecting Internal Inconsistencies in Ophthalmic Manuscripts
At a Glance
Category
Detail
Condition
Clinical topic addressed by the source article: Evaluating Large Language Models for Detecting Internal Inconsistencies in Ophthalmic Manuscripts
Key Mechanisms
The study assessed detection of percentage/count discrepancies, sample-size/subgrouping discrepancies, and statistical reporting discrepancies.
Target Population
Forty randomly selected published ophthalmic articles from journals with an impact factor greater than 3.0; 30 had one prespecified inconsistency inserted without otherwise modifying the manuscript, and 10 were left unchanged.
Care Setting
Evaluation of LLMs as consistency checkers for published ophthalmic manuscripts; the authors discuss possible use as an additional quality-control layer before submission.
Key Highlights
Across 120 evaluations of manuscripts with planted errors, models detected 98 (81.7%); detection varied by model.
GPT-5.6 detected 29/30 planted errors (96.7%), Claude Sonnet 5 detected 27/30 (90.0%), Gemini 3.1 Pro detected 23/30 (76.7%), and GPT-5.5 detected 19/30 (63.3%).
Detection was highest for sample-size/subgrouping inconsistencies (90.0%), followed by percentage/count discrepancies (82.5%) and statistical reporting inconsistencies (72.5%).
All 98 detected planted errors were localised correctly; explanations and corrections were correct for 97/98 (99.0%). Complete end-to-end performance occurred in 97/120 assessments (80.8%).
Of 355 additional error flags, 337 (94.9%) were adjudicated as genuine; 158 unique pre-existing inconsistencies remained after duplicate detections were collapsed.
Guideline-Based Recommendations
Diagnosis
The study evaluated LLM detection of planted internal inconsistencies in published ophthalmic manuscripts; it did not assess clinical diagnosis.
Management
The authors support considering LLMs as an additional manuscript quality-control layer before submission.
The authors describe LLM use as complementary to, rather than a substitute for, conventional review.
Monitoring & Follow-up
The study assessed detection, precise localisation, explanation, and proposed correction.
The authors identify systematic detection, rather than interpretation after detection, as the chief limitation.
Risks
Models overlooked some planted errors, despite identifying other genuine inconsistencies in the same manuscript.
Three of 118 flags in unmodified manuscripts (2.5%) were adjudicated as non-genuine.
Patient & Prescribing Data
Not applicable: this was a manuscript-evaluation study, not a patient study.
No treatments or prescribing data were evaluated.
Clinical Best Practices
The authors support considering LLM assessment as an additional manuscript-screening layer, complementary to conventional review.
In this study, additional flags were manually adjudicated as genuine or non-genuine.
Planted-error detection was incomplete across all evaluated models.
