Evidence map›Paper›PMID 39045247›Full record

ArticleAccess microbiology2024

An evaluation of ChatGPT and Bard (Gemini) in the context of biological knowledge retrieval.

Ron Caspi, Peter D Karp

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Ron CaspiSRI International, Menlo Park, CA 94025, USA.ORCID 0000-0002-6113-3590
Peter D KarpSRI International, Menlo Park, CA 94025, USA.ORCID 0000-0002-5876-6418

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

biocurationlarge language model

Identifiers

PMID39045247
PMCPMC11261719

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.