Evidence map›Paper›PMID 40596129›Full record

ArticleScientific reports2025

Comparison of AI chatbot predicted and realworld survival outcomes in hepatocellular carcinoma.

Engin Eren Kavak, Efe Cem Erdat, Zeynep Altundağ Derin, İsmail Dilli, Pınar Kubilay Tolunay, Berna Öksüzoğlu, Hakan Akbulut

Abstract readComparative Study
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

7 authors.

Engin Eren KavakDepartment of Medical Oncology, Ankara Etlik City Hospital, Ankara, Turkey. engineren2000@yahoo.com.ORCID https://orcid.org/0000-0003-3247-5361
Efe Cem ErdatDepartment of Medical Oncology, Ankara University Medical Faculty, Ankara, Turkey.ORCID https://orcid.org/0000-0002-1250-1297
Zeynep Altundağ DerinDepartment of Medical Oncology, Ankara Etlik City Hospital, Ankara, Turkey.ORCID https://orcid.org/0000-0002-3201-3776
İsmail DilliDepartment of Medical Oncology, Ankara Etlik City Hospital, Ankara, Turkey.ORCID https://orcid.org/0009-0005-9043-9790
Pınar Kubilay TolunayDepartment of Medical Oncology, Ankara University Medical Faculty, Ankara, Turkey.ORCID https://orcid.org/0000-0003-2636-5326
Berna ÖksüzoğluDepartment of Medical Oncology, Ankara Etlik City Hospital, Ankara, Turkey.
Hakan AkbulutDepartment of Medical Oncology, Ankara University Medical Faculty, Ankara, Turkey.ORCID https://orcid.org/0000-0003-1631-5739

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study compares survival predictions made by an artificial intelligence (AI) based chatbot with real-world data in hepatocellular carcinoma (HCC) patients. It aims to evaluate the reliability and accuracy of AI technologies in HCC prognosis. A retrospective analysis was conducted on patients diagnosed with HCC. The estimated survival times for each patient were calculated using an artificial intelligence chatbot. The follow-up periods and mortality data for the patients were used to obtain real-life survival data. The predicted and actual survival times were statistically compared. ChatGPT-4o consistently overestimated the overall survival (OS) times compared to real-world outcomes.A statistically significant discrepancy was observed between the predicted and actual survival times (p < 0.05). Nevertheless, while the survival predictions of AI were more accurate in patients with advanced-stage HCC, the predictions differed significantly in patients with early-stage HCC. AI has the potential to play an important role in the prognosis of complex diseases such as HCC. However, this study's findings indicate that AI's predictions are not entirely consistent with real-world data, particularly in the context of early-stage diseases. Further large-scale studies may enhance the dependability of incorporating AI into clinical decision-support systems. The use of AI-assisted predictions may prove to be a valuable tool for the prediction of survival in patients with HCC. Nevertheless, further studies are required to substantiate the accuracy and reliability of these systems before they can be implemented in clinical practice.

Indexed as

Artificial IntelligenceCarcinoma, HepatocellularLiver NeoplasmsAdultAgedFemaleGenerative Artificial IntelligenceHumansMaleMiddle AgedPrognosisRetrospective StudiesSurvival AnalysisArtificial intelligenceHepatocellular carcinomaPredicted overall survivalPrognostic model

Identifiers

PMID40596129
PMCPMC12219167

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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.