ArticleClinical infectious diseases : an official publication of the Infectious Diseases Society of America2024
Black Box Warning: Large Language Models and the Future of Infectious Diseases Consultation.
Article in Clinical infectious diseases : an official publication of the Infectious Diseases Society of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 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
63 citing papers in PubMed.
- Article
- Recent advancements in artificial intelligence applications for the mitigation of antimicrobial resistance: challenges and opportunities.JAC-antimicrobial resistance · 2026Review
- Current Landscape of Generative AI Use as a Search Engine Among Resident Physicians: Cross-Sectional Study.JMIR AI · 2026Article
- Artificial Intelligence in Infectious Disease Care: Selected Applications in Tuberculosis, Sepsis, and Antimicrobial Stewardship.Diagnostics (Basel, Switzerland) · 2026Review
- Errors in AI-Transformed Patient-Centered Mental Health Documentation Written by Psychiatrists: Qualitative Pre-Post Study.JMIR mental health · 2026Article
- Evaluating the performance of five large language models in generating patient educational content for pediatric cardiothoracic procedures: a comparative study.General thoracic and cardiovascular surgery · 2026Observational
- Large Language Models in Infectious Diseases: A Systemic Review.Research square · 2026Article
- Article
- ZhongdaChat-ED: a medical large language model for personalized erectile dysfunction health consultation and professional clinical decision-making using retrieval-augmented generation.Asian journal of andrology · 2026Article
- A Theoretical Framework forInfection and drug resistance · 2026Article
- A comprehensive review of AI innovations for tackling antimicrobial resistance.Le infezioni in medicina · 2026Review
- Review
- Artificial intelligence in clinical microbiology: results from the first National survey by the Italian association of clinical microbiologists.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2026Article
- Education Research: Can Large Language Models Match MS Specialist Training?: A Comparative Study of AI and Student Responses to Support Neurology Education.Neurology. Education · 2025Article
- Comparing ChatGPT-3.5, Gemini 2.0, and DeepSeek V3 for pediatric pneumonia learning in medical students.Scientific reports · 2025Article
- Can general purpose large language models assist pediatricians in predicting infants with serious bacterial infection?BMC medical informatics and decision making · 2025Article
- Comparative Evaluation and Performance of Large Language Models in Clinical Infection Control Scenarios: A Benchmark Study.Healthcare (Basel, Switzerland) · 2025Article
- Syndromic Analysis of Sepsis Cohorts Using Large Language Models.JAMA network open · 2025Article
- Article
- Phenotyping valvular heart diseases using the lens of unsupervised machine learning: a scoping review.NPJ cardiovascular health · 2025Article
3 more citing papers are in PubMed but not listed here.
Corrections and comments
- Commented on by
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large language models (LLMs) are artificial intelligence systems trained by deep learning algorithms to process natural language and generate text responses to user prompts. Some approach physician performance on a range of medical challenges, leading some proponents to advocate for their potential use in clinical consultation and prompting some consternation about the future of cognitive specialties. However, LLMs currently have limitations that preclude safe clinical deployment in performing specialist consultations, including frequent confabulations, lack of contextual awareness crucial for nuanced diagnostic and treatment plans, inscrutable and unexplainable training data and methods, and propensity to recapitulate biases. Nonetheless, considering the rapid improvement in this technology, growing calls for clinical integration, and healthcare systems that chronically undervalue cognitive specialties, it is critical that infectious diseases clinicians engage with LLMs to enable informed advocacy for how they should-and shouldn't-be used to augment specialist care.
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.