Evidence map›Paper›PMID 40949405›Full record

ArticleIndian journal of surgical oncology2025

AI Chatbots in Oncology: A Comparative Study of Sider Fusion AI and Perplexity AI for Gastric Cancer Patients.

Amirhosein Naseri, Mohammad Hossein Antikchi, Maedeh Barahman, Ahmad Shirinzadeh-Dastgiri, Seyed Masoud HaghighiKian, Mohammad Vakili-Ojarood, Amirhossein Rahmani, Amirhossein Shahbazi, Amirmasoud Shiri, Ali Masoudi and 3 more

Abstract read
In one paragraph

Article in Indian journal of surgical oncology, 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. Review
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

13 authors.

Amirhosein NaseriDepartment of Colorectal Surgery, Imam Reza Hospital, AJA University of Medical Sciences, Tehran, Iran.
Mohammad Hossein AntikchiDepartment of Internal Medicine, Yazd Branch, Islamic Azad University, Yazd, Iran.
Maedeh BarahmanDepartment of Radiation Oncology, Firoozgar Clinical Research Development Center, Iran University of Medical Sciences, Tehran, Iran.
Ahmad Shirinzadeh-DastgiriDepartment of Surgery, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
Seyed Masoud HaghighiKianDepartment of Surgery, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
Mohammad Vakili-OjaroodDepartment of Surgery, School of Medicine, Ardabil University of Medical Sciences, Ardabil, Iran.
Amirhossein RahmaniDepartment of Surgery, Iranshahr University of Medical Sciences, Iranshahr, Iran.
Amirhossein ShahbaziGeneral Practitioner, Ilam University of Medical Sciences, Ilam, Iran.
Amirmasoud ShiriGeneral Practitioner, Shiraz University of Medical Sciences, Shiraz, Iran.
Ali MasoudiGeneral Practitioner, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
Maryam AghasipourDepartment of Cancer Biology, College of Medicine, University of Cincinnati, Cincinnati, OH USA.
Kazem AghiliDepartment of Radiology, School of Medicine, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
Hossein NeamatzadehMother and Newborn Health Research Center, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The integration of AI chatbots in healthcare, particularly for young adults diagnosed with gastric cancer, offers a novel approach to enhancing patient support and information access. This evaluation examines the effectiveness of two AI chatbots, Sider Fusion AI Bot, and Perplexity AI, in improving patient outcomes, alleviating anxiety, and promoting informed decision-making. A comparative study design was employed, utilizing a structured rubric guided by experienced gastroenterologists to assess the performance of both chatbots. Evaluation criteria included competency, accuracy, relevance, informativeness, and support capabilities. The study focused on a case involving a 21-year-old male with advanced gastric adenocarcinoma. Both chatbots demonstrated high competency and relevance scores, achieving perfect scores of 5. However, they received low accuracy scores of 1, indicating a need for scrutiny in specific details. Sider Fusion AI Bot excelled in informativeness (score of 5) and support capabilities (rated "high"), while Perplexity AI scored 4 in informativeness and was rated "moderate" for support. Both systems provided essential recommendations for alleviating anxiety and emphasized informed decision-making, but Sider Fusion AI Bot offered a more comprehensive discharge plan. The study highlights the strengths and weaknesses of AI chatbots in oncology, emphasizing the importance of tailored communication styles to enhance patient engagement and outcomes. Sider Fusion AI Bot's superior capacity for detailed explanations and user engagement positions it as a potentially more valuable tool in clinical decision-making. Ongoing research is necessary to address limitations and optimize the integration of AI chatbots in healthcare settings, ensuring they effectively complement human interactions.

Indexed as

AI ChatbotsArtificial intelligenceOncology

Identifiers

PMID40949405
PMCPMC12431968

What Socratic holds

Textmetadata
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 Socratic graph.