A custom GPT-based model for the automated analysis and interpretation of antimicrobial susceptibility tests in...

Antimicrobial resistance in Gram-negative bacilli represents a growing clinical challenge that demands innovative tools to optimize antibiogram interpretation and support safe therapeutic decisions.

To develop and validate a custom generative pre-trained transformer (GPT) model for antibiogram interpretation, comparing its performance with an alternative model based on Gemini (Gems).

A retrospective, cross-sectional diagnostic accuracy study was conducted following the STARD-artificial intelligence (AI) guideline. A total of 210 antibiograms were analysed, balanced across controls and five resistance mechanisms (penicillinases, broad-spectrum β-lactamases, extended-spectrum β-lactamases, AmpC and Carba-R). The custom GPT, configured through prompt engineering and retrieval-augmented generation, was evaluated against expert interpretation and compared with Gems. Performance metrics (sensitivity, specificity, precision, F1-score, receiver operating characteristic/precision-recall area under the curve and Cohen's Kappa) were calculated, and errors were classified by clinical impact as overtreatment or undertreatment.

The GPT achieved an accuracy of 95.7% (95% confidence interval: 92.0-98.0) and a Kappa of 0.948, outperforming Gems (91.9% and 0.902, respectively). Both models showed good performance, but GPT displayed greater consistency across metrics, with sensitivities and F1-scores >90% in all classes. Clinically relevant errors were fewer and less severe with GPT (9 versus 17 in Gems), with markedly lower spectrum penalty and treatment failure risk indices.

This study provides proof-of-concept evidence that a custom GPT model can accurately interpret antibiograms of Gram-negative bacilli under controlled retrospective condition. Although these findings are promising, external multicentre validation, expansion to other pathogens and resistance mechanisms and further assessment as a clinical decision-support tool are required before routine clinical implementation.