Evidence map›Paper›PMID 39872919›Full record

ArticleCureus2025

Evaluating the Performance of ChatGPT-4o Oncology Expert in Comparison to Standard Medical Oncology Knowledge: A Focus on Treatment-Related Clinical Questions.

Oguzcan Kinikoglu, Deniz Isik

Abstract read
In one paragraph

Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

2 authors.

Oguzcan KinikogluMedical Oncology, Kartal Dr. Lütfi Kirdar City Hospital, Health Science University, Istanbul, TUR.
Deniz IsikMedical Oncology, Kartal Dr. Lütfi Kirdar City Hospital, Health Science University, Istanbul, TUR.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Integrating artificial intelligence (AI) into oncology can revolutionize decision-making by providing accurate information. This study evaluates the performance of ChatGPT-4o (OpenAI, San Francisco, CA) Oncology Expert, in addressing open-ended clinical oncology questions. Thirty-seven treatment-related questions on solid organ tumors were selected from a hematology-oncology textbook. Responses from ChatGPT-4o Oncology Expert and the textbook were anonymized and independently evaluated by two medical oncologists using a structured scoring system focused on accuracy and clinical justification. Statistical analysis, including paired t-tests, was conducted to compare scores, and interrater reliability was assessed using Cohen's Kappa. Oncology Expert achieved a significantly higher average score of 7.83 compared to the textbook's 7.0 (p < 0.01). In 10 cases, Oncology Expert provided more accurate and updated answers, demonstrating its ability to integrate recent medical knowledge. In 26 cases, both sources provided equally relevant answers, but the Oncology Expert's responses were clearer and easier to understand. Cohen's Kappa indicated almost perfect agreement (κ = 0.93). Both sources included outdated information for bladder cancer treatment, underscoring the need for regular updates. ChatGPT-4o Oncology Expert shows significant potential as a clinical tool in oncology by offering precise, up-to-date, and user-friendly responses. It could transform oncology practice by enhancing decision-making efficiency, improving educational tools, and serving as a reliable adjunct to clinical workflows. However, its integration requires regular updates, expert validation, and a collaborative approach to ensure reliability and relevance in the rapidly evolving field of oncology.

Indexed as

artificial intelligence and educationchatgpt-4oclinical-decision makingoncologyoncology expert

Identifiers

PMID39872919
PMCPMC11771770

What OpenQuestion holds

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LicenceCC BY
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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.