ArticleScientific reports2025
Pre-trained ChatGPT for report generation in automated microbial identification and antibiotic susceptibility testing systems.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Performance Evaluation of GPT-5, Grok 4, and DeepSeek R1 in Interpreting Complete Blood Count Reports for Hematologic Diseases: Retrospective Comparative Study.Journal of medical Internet research · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automated microbial identification and antibiotic susceptibility testing systems (automated ID/AST systems) are widely used in clinical microbiology, yet ensuring standardized and accurate report generation remains challenging. Large language models, such as ChatGPT, offer potential for improving report consistency and objectivity through structured prompt engineering. This study evaluates the effectiveness of ChatGPT in generating standardized microbiology reports for automated ID/AST systems, compared to clinical microbiologists (CM). ChatGPT was provided with structured prompts based on clinical & laboratory standards institute (CLSI) guidelines to generate automated ID/AST systems reports. A prompt engineering framework was developed to enhance AI-generated reports. Performance was assessed across five dimensions: accuracy, relevance, objectivity, completeness, and clarity. Eight clinical cases were analyzed, comparing reports from three groups: CM, ChatGPT before prompt training (ChatGPT_BT), and ChatGPT after prompt training (ChatGPT_AT). The ChatGPT_BT group demonstrated higher relevance and completeness compared to the CM' group (p < 0.0001 and p < 0.0001). After training, the ChatGPT_AT group produced reports with significantly improved quality across all five dimensions (p < 0.001, p < 0.0001, p < 0.0001, p < 0.0001 and p < 0.0001). Moreover, the ChatGPT_AT group showed notable improvements in relevance, objectivity, completeness, and clarity compared to the ChatGPT_BT (p < 0.001, p < 0.0001, p < 0.0001 and p < 0.05), with no significant difference in accuracy (p ≥ 0.05). ChatGPT, when guided by a structured prompt engineering process, shows significant potential in assisting CM by enhancing the objectivity, clarity, completeness, relevance, and accuracy of automated ID/AST systems reports.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.