ArticleTherapeutic advances in infectious disease
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.
Article in Therapeutic advances in infectious disease. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
Background: General-purpose large language model (LLM)-based systems are increasingly accessible to clinicians and are being explored for applications in clinical microbiology and infectious diseases (ID). However, rapid adoption has outpaced the development of specialty-specific guidance, raising concerns related to safety, reliability, accountability, and antimicrobial stewardship. Objectives: The project aims to develop consensus-based statements, endorsed by Study Group for Artificial Intelligence and Digitalisation of the European Society of Clinical Microbiology and Infectious Diseases (ESGAID), that describe principles, opportunities, and limitations in the interactions of clinical microbiologists and ID specialists with general-purpose LLM-based systems. Secondary objectives are to quantify expert agreement and identify areas of uncertainty and disagreement. Design: The project follows a structured expert consensus design using the RAND/UCLA Appropriateness Method. Methods and analysis: A multidisciplinary panel of 15 experts will be selected through ESGAID using predefined criteria to ensure balanced expertise across clinical microbiology, infectious diseases, ethics, legal aspects, and patient safety. Ten draft statements, each supported by a structured literature review, will be developed by project coordinators. Statements will be evaluated through iterative rounds of anonymous rating on a 1-9 scale, combined with moderated remote discussions. Median scores will classify statements as supported, uncertain, or unsupported. Consensus will be defined as ⩾70% agreement with <15% disagreement during final anonymous voting. Discussion: This protocol provides a transparent and reproducible framework to generate interim, specialty-specific statements on principles, opportunities, and limitations in the interactions of clinical microbiologists and ID specialists with general-purpose LLM-based systems. By combining structured evidence review with expert judgment, the resulting statements aim to delineate guidance on principles for interacting with these systems, highlight the nature of both existing risks and excessive skepticism, and identify research priorities in a rapidly evolving technological and regulatory landscape.
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.