ArticleAccess microbiology2024
An evaluation of ChatGPT and Bard (Gemini) in the context of biological knowledge retrieval.
Article in Access microbiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- The doctors of the future: the competition of ChatGPT-4, ChatGPT-4 omni, and Gemini 2.0 Flash in andrology.BMC urology · 2026Article
- AI in biocuration: challenges, opportunities, and a roadmap for sustainable integration.Bioinformatics advances · 2026Article
- Evaluating the Accuracy of Medical Information Generated by ChatGPT and Gemini and Its Alignment With International Clinical Guidelines From the Surviving Sepsis Campaign: Comparative Study.JMIR formative research · 2025Article
- Assessing the performance of generative artificial intelligence in retrieving information against manually curated genetic and genomic data.Database : the journal of biological databases and curation · 2025Article
- FuncFetch: an LLM-assisted workflow enables mining thousands of enzyme-substrate interactions from published manuscripts.Bioinformatics (Oxford, England) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
ChatGPT and Bard (now called Gemini), two conversational AI models developed by OpenAI and Google AI, respectively, have garnered considerable attention for their ability to engage in natural language conversations and perform various language-related tasks. While the versatility of these chatbots in generating text and simulating human-like conversations is undeniable, we wanted to evaluate their effectiveness in retrieving biological knowledge for curation and research purposes. To do so we asked each chatbot a series of questions and scored their answers based on their quality. Out of a maximal score of 24, ChatGPT scored 5 and Bard scored 13. The encountered issues included missing information, incorrect answers, and instances where responses combine accurate and inaccurate details. Notably, both tools tend to fabricate references to scientific papers, undermining their usability. In light of these findings, we recommend that biologists continue to rely on traditional sources while periodically assessing the reliability of ChatGPT and Bard. As ChatGPT aptly suggested, for specific and up-to-date scientific information, established scientific journals, databases, and subject-matter experts remain the preferred avenues for trustworthy data.
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.