Evidence map›Paper›PMID 41835657›Full record

ArticleCureus2026

Can I Do Better Than AI: A Comparative Analysis of a Medical Student Essay Created With and Without Generative AI.

Nabihah Hussaini, Susan M Astley, Adam Perrett, Elaine F Harkness

Abstract read
In one paragraph

Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Nabihah HussainiSchool of Medical Sciences, The University of Manchester, Manchester, GBR.
Susan M AstleyDivision of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine, and Health, The University of Manchester, Manchester, GBR.
Adam PerrettDivision of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine, and Health, The University of Manchester, Manchester, GBR.
Elaine F HarknessDivision of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine, and Health, The University of Manchester, Manchester, GBR.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background With the increasing integration of generative artificial intelligence (AI) tools in education, questions have emerged about their effectiveness in academic tasks such as reviewing literature and writing essays. This paper evaluates whether generative AI could outperform a third-year medical student writing a literature review. Methods A third-year medical student wrote a 3000-word literature review on the prevention of breast cancer following University guidelines. Three prompting methods were used to generate essays with the same title and guidance using 'ChatGPT-4o' (OpenAI, San Francisco, CA, USA). Each essay was then graded alongside the student's essay by an AI chatbot using the marking scheme provided. The highest-scoring AI essay and the student's essay were critically compared and analysed. They were then tested for similarity to pre-existing literature using Turnitin, a widely used plagiarism detector. Results The student's essay comprised 3044 words, written in 452 minutes. In contrast, the highest-marked AI-generated text comprised 3260 words, written in only 15 minutes. However, despite efficiency in time, the AI-generated essay demonstrated reduced originality, receiving a Turnitin similarity score of 46%, compared to the human-written score of 26%, indicating a greater overlap of content from existing sources. Furthermore, it was found that a single prompt was not sufficient to produce a high-quality, original, AI-generated essay; instead, a large series of instructions was required to produce a suitable review. Conclusions Generative AI can assist students by complementing their efforts, but it produces work that may lack originality. Further research is required to improve AI's ability to generate original, in-depth content.

Indexed as

artificial intelligencechatgptlarge language modelsmedical educationmedical student performance evaluation

Identifiers

PMID41835657
PMCPMC12981321

What OpenQuestion holds

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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.