Evidence map›Paper›PMID 38770053›Full record

ArticleCureus2024

Assessment of Artificial Intelligence Platforms With Regard to Medical Microbiology Knowledge: An Analysis of ChatGPT and Gemini.

Jai Ranjan, Absar Ahmad, Monalisa Subudhi, Ajay Kumar

Abstract read
In one paragraph

Article in Cureus, 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
  2. Performance of AI Chatbots in Preliminary Diagnosis of Maxillofacial Pathologies.Medical science monitor : international medical journal of experimental and clinical research · 2025
    Article
  3. Article
  4. Article
  5. Article
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

4 authors.

Jai RanjanMicrobiology, All India Institute of Medical Sciences, Bathinda, Bathinda, IND.
Absar AhmadAnimal Genetics and Breeding, Faculty of Veterinary Science and Animal Husbandry, Birsa Agricultural University, Ranchi, IND.
Monalisa SubudhiMicrobiology, Institute of Medical Sciences and SUM-II Hospital, Bhubaneswar, IND.
Ajay KumarMicrobiology, Manipal Tata Medical College, Manipal Academy of Higher Education, Manipal, IND.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The performance of two artificial intelligence (AI) platforms, ChatGPT 3.5 (OpenAI, California, United States) and Gemini (Google AI, California, United States) was assessed by answering 200 questions of microbiology drawn from validated sources. The questions were selected from topics such as General Microbiology, Immunology, and Microbiology Applied to Infectious Diseases. The study was conducted from December 2023 to March 2024, and the responses of the different AI platforms were compared with an answer key. Statistical analysis was performed to assess accuracy. ChatGPT 3.5 and Gemini had comparable accuracy with correct response scores of 71% and 70.5%, respectively. Their performance varied across different sections. Gemini performed better in General Microbiology and Immunology, and ChatGPT 3.5 had a better score in the Applied Microbiology section. The study's findings highlight that AI platforms such as ChatGPT and Gemini can be utilized in microbiology and medical education. The evolution and continuous updating of AI platforms are required to improve their performance.

Indexed as

artificial intelligencelarge language modellearningmedical educationmicrobiology

Identifiers

PMID38770053
PMCPMC11104281

What OpenQuestion holds

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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.