ArticleBMC infectious diseases2025
Recommended antibiotic treatment agreement between infectious diseases specialists and ChatGPT
Article in BMC infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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
13 citing papers in PubMed.
- Recent advancements in artificial intelligence applications for the mitigation of antimicrobial resistance: challenges and opportunities.JAC-antimicrobial resistance · 2026Review
- Therapeutic Decisions in Inflammatory Bowel Disease: ChatGPT-4.0 Compared to Medical Expertise.JGH open : an open access journal of gastroenterology and hepatology · 2026Article
- Large Language Models in Infectious Diseases: A Systemic Review.Research square · 2026Article
- Benchmark evaluation of deepseek AI models in antibacterial clinical decision-making for infectious diseases.BMC medical informatics and decision making · 2026Article
- The Future of Antibiotics and Artificial Intelligence: Some Thoughts from Discovery to Bedside.Infectious diseases and therapy · 2026Review
- A Theoretical Framework forInfection and drug resistance · 2026Article
- Observational
- Mapping applications and evaluations of LLM-enabled AI chatbots for health purposes: a scoping review.Frontiers in public health · 2026Article
- Large language models as information providers for appropriate antimicrobial use: computational text analysis and expert-rated comparison of ChatGPT, Claude and Gemini.BMJ health & care informatics · 2025Article
- Role of artificial intelligence in ICU therapeutic decision-making for severe infections.Current opinion in critical care · 2025Review
- Potential of ChatGPT in youth mental health emergency triage: Comparative analysis with clinicians.PCN reports : psychiatry and clinical neurosciences · 2025Article
- Chatbot Underperformance in Biology and Image-Based Questions in Medical Education.Journal of CME · 2025Article
- European Society of Clinical Microbiology and Infectious Diseases Study Group for Artificial Intelligence and Digitalisation consensus statements on key aspects in the interaction of clinical microbiologists and infectious disease specialists with general-purpose large language model-based systems: project protocol.Therapeutic advances in infectious diseaseArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAntimicrobial resistance is a global threat to public health. Chat Generative Pre-trained Transformer (ChatGPT
methodsUsing data from microbiological isolates recovered in our center, we fabricated 100 cases of patients with different infections. Each case included age, infectious syndrome, isolated organism and complete antibiogram. Considering a precise set of instructions, the cases were introduced into ChatGPT
resultsRegarding the recommended antibiotic prescription, agreement between ID specialists and ChatGPT
conclusionWe found poor agreement between ID specialists and ChatGPT
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.